productivity improvement at new braunfels smokehouse

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Productivity Improvement at New Braunfels Smokehouse A Senior Design Project Kenia A. Artola Industrial Engineering Serena Contreras Engineering Science Ting King Yong Engineering Management Presented to the Faculty of the Engineering Department in Partial Fulfillment of the Requirements for Their Respective Bachelor of Science Degrees May 2021 ________________________ Rafael Moras, Ph.D., P.E. Supervisor

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Page 1: Productivity Improvement at New Braunfels Smokehouse

Productivity Improvement at

New Braunfels Smokehouse

A Senior Design Project

Kenia A. Artola

Industrial Engineering

Serena Contreras

Engineering Science

Ting King Yong

Engineering Management

Presented to the Faculty of the Engineering Department

in Partial Fulfillment of the Requirements for Their Respective

Bachelor of Science Degrees

May 2021

________________________

Rafael Moras, Ph.D., P.E.

Supervisor

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Abstract

This senior design project was conducted in conjunction with an internship at New

Braunfels Smokehouse. We were tasked with improving the packing and shipping area, the

development of key performance indicators (KPIs), and the implementation of an enterprise

resource planning (ERP) software solution. We discuss our work and results obtained during the

fall 2020 and spring 2021 semesters. This project has made a difference in the way the company

is run and has been praised by upper management. The KPIs have given an insight of various

areas of improvement for an effective usage of resources and this way maximize profit. The new

workflow design has improved the organization and efficiency in the picking and packing area.

With the early stages of ERP implementation, a greater level of organization and tracking has

been achieved.

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Dedication

This Senior design is dedicated to all of those who have been there for me these four

year: God, Mother Mary, St. Therese of Lisieux, and all my other holy friends in heaven who

have interceded to make this dream come true. Furthermore, this is dedicated to my family and

friends; especially to my parents for never cutting off my wings, instead encouraged me every

step of the way.

- Kenia Artola

I would like to dedicate this Senior Design Project to my friends and family who have

supported me through these last four years especially my father who won’t get to see my

accomplishments, but I know he is with me every step of the way. I would also like to thank the

St. Mary’s Community for the constant love and reassurance.

- Serena Contreras

I dedicate this senior design project to God, for giving me the blessing of pursuing a

career. Also, to my family and friends, who have been my major support and motivation through

all these years and who always encourage me to work hard for my dreams. Finally, to my

professors and St. Mary’s staff who have helped me along the way with their guidance and

inspiration. To everyone who believed in me, thank you.

- Ting King Yong

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Acknowledgement

We would like to express our sincere gratitude to New Braunfels Smokehouse for

allowing us to perform this senior design project in partnership with them. We would also like to

thank the general manager and supervisor of this project, Mr. Josh Klein, as well as St. Mary’s

alumna, Ms. Gabriela Santos, for their guidance and support throughout this year. Finally, we

would like to recognize our academic advisor, Dr. Moras, for his continuous assistance to make

this project successful.

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Table of Contents

Abstract ............................................................................................................................... II

Dedication ......................................................................................................................... III

Acknowledgement ............................................................................................................ IV

Chapter I: Introduction ........................................................................................................ 1

1.1 Company Description ............................................................................................... 1

1.2 Team Composition and Internship Description ........................................................ 1

Chapter II: Literature Review ............................................................................................. 2

2.1 Problem Statement .................................................................................................... 2

2.2 Literature Review...................................................................................................... 3

2.3 Deliverables .............................................................................................................. 7

2.4 Standards ................................................................................................................... 8

Chapter III: Development of Key Performance Indicators ............................................... 14

3.1 Problem Definition.................................................................................................. 14

3.2 Constraints .............................................................................................................. 14

3.3 DMAIC Analysis .................................................................................................... 15

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3.4 Analysis................................................................................................................... 19

3.5 Summary of Results and Implementation ............................................................... 27

3.6 Conclusion .............................................................................................................. 28

Chapter IV: Simulation Model.......................................................................................... 30

4.1 Problem Definition.................................................................................................. 30

4.2 Constraints .............................................................................................................. 31

4.3 PDSA Approach...................................................................................................... 32

4.4 Analysis................................................................................................................... 44

4.5 Summary of Results ................................................................................................ 51

4.6 Validation ................................................................................................................ 53

4.8 Conclusion .............................................................................................................. 54

Chapter V: Enterprise Resource Planning ........................................................................ 57

5.1 Problem Definition.................................................................................................. 57

5.2 Constraints .............................................................................................................. 57

5.3 Methods................................................................................................................... 58

5.4 Analysis................................................................................................................... 58

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5.5 Results and Implementation .................................................................................... 66

5.6 SOP Manual ............................................................................................................ 67

5.7 Conclusion .............................................................................................................. 67

Chapter VI: Project Conclusion and Reflections .............................................................. 74

Overall Project Conclusion ........................................................................................... 74

Kenia’s Reflection ........................................................................................................ 74

Serena’s Reflection ....................................................................................................... 76

Ting King’s Reflection ................................................................................................. 79

References ......................................................................................................................... 81

Appendices ........................................................................................................................ 83

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CHAPTER I: INTRODUCTION

1.1 Company Description

Before becoming New Braunfels Smokehouse, the family business first started in 1943 as

an ice plant to store the meats from local farmers. In 1945 the Dunbar family decided to turn

their storage into a Smokehouse, giving birth to New Braunfels Smokehouse, which originally

offered traditional hickory smoked hams, turkeys, and sausages. Today, their catalog has been

expanded and diversified to also including gourmet cheeses and desserts. Even though it first

started as a local business, it now serves as one of the main meat products suppliers for Buc’ees

(a well-known Texan chain of gas stations and convenience stores) and to Omaha Steaks (one of

the nation’s largest meat marketers), expanding their marketing to not only business-to-consumer

(B2C), but also business-to-business (B2B). As a family-run business, the leadership

includes Hale and Elizabeth Snyder, who represent the third generation of the family managing

the business. The current general manager and supervisor of the project is Josh Klein.

1.2 Team Composition and Internship Description

This senior design project was conducted in conjunction with an internship sponsored by

New Braunfels Smokehouse. The multidisciplinary team was composed by Kenia Artola

(Industrial Engineering), Serena Contreras (Engineering Science), and Ting King Yong

(Engineering Management). The project was divided into three main sections that each of the

members was in charge of.

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CHAPTER II: LITERATURE REVIEW

2.1 Problem Statement

As a local business, one of the major pillars at New Braunfels Smokehouse is conserving

deep-rooted traditions. These traditions range from how they cook their meats, to how they

manage their inventories and ship their products. Under the principle of Kaizen, also known as

continuous improvement, the company provided us with numerous opportunities to help them

grow. This senior design project aims to help with the following areas of enhancement.

The first one is the development of key performance indicators (KPIs) to give upper

management a visual representation of the current business performance. Even though all

information of the business is recorded, most of it is dispersed in many documents stored in

multiple locations of the plant. There was a need to put this information together and examine it

to give insight of how well the business as doing, which areas were causing issues that could be

prevented, and which areas could be improved to minimize expenses and maximize profit.

An opportunity for improvement was the change of the former workflow in the shipping

area. At the time this project was being conducted, the plant itself had not been expanded in 15

years and, comparing the size of the demands to the space available, there was a need for new

methods to allow a smooth transportation and management of goods. The previous workflow

caused traffic congestion among wagons, which resulted in an increased picking process time.

Additionally, the arrangement of the previous workflow– having the jarred goods as the last step

of the process– was causing many missing items in the orders, which then had to be replaced at

the company’s cost.

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The jarred goods issue was also explored as part of this senior design project. The goal

of the creation and implementation of an ERP system was to help the Smokehouse maximize the

efficiency of the manufacturing process, track products in production, and insure all items are

included in the picking and packing process. This system would help connect every aspect of the

Smokehouse, from raw materials to shipping, and allow employees to track progress. The ERP

system would not change the way the goods are made but would give an extra step in the

management part of the process. Each area of the warehouse would weigh the goods, and input

information regarding the ingredients or process into the system. In previous years, the

Smokehouse had not tracked this information rigorously and, in the event USDA inspections, it

had been harder to fully track each finished product back to the vendor that produced the meat.

This system would overall help the organization of the process and allow the company to track

everything coming in and going out of the Smokehouse.

2.2 Literature Review

A review of literature relevant to this senior design work is presented here. According to

Dupuy, Botta-Genoulaz, and Guinet (2005), the traceability in the food industry is essential if

complaints arise from customers. It is also important when tracking if the most is being made of

the ingredients given to a company. Loos (2001) agreed when stating that, racing systems in the

food industry are used to represent the process of raw materials to an end product. This is a

crucial part in customer complaints and in the recipes of products. The picking and packing

process is a key component of our project. It is important to improve efficiency to help create a

better, faster way of sending product out to customers.

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Distribution centers in a variety of industries have systems in place to track items and

organize warehouses. Fujita (2018) indicated that this process often includes receiving

inspections, shelving, replenishment, picking, shipping inspection, packing, and loading.

Tracking systems are essential to business because if a problem arises, time and money are

needed to fix it. Basu and Blanning (2000) added that along with the tracking system, many

companies have begun using virtual organization of workflows to focus on “cross-functional”

business processes that link the different parts of their business together. However, there are not

many applications that can provide this information so the best way to create workflows is

through independent studies. Many warehouses run into this issue as well as congestion in the

workplace, according to Zhang, Batta, and Nagi (2009). Workflow studies have shown that by

creating a new layout and improving flow can provide effective solutions to decreasing picking

and packing times. The issue of congestion has led many companies to turn to machinery

(Kattepur, Mukherjee, & Balamuralidhar, 2018). In our case, this is not an option due to

traditional approaches that would include not wanting to change certain procedures (e.g.,

shipping layout, not keeping track of inventory and waste, etc.) and spatial restraints.

Lee, Chang, Shim, and Cho (2015) indicated that manpower is used in two ways in

regard to picking: pre-picking and auto picking. Pre-picking is carried out with the help of

technology to receive and sort orders by unit, box, and pallet location. Auto-picking picking is

the process of getting the products off the shelves and into packaging. A solution that involves

manpower suggested by Shiau and Lee (2010) was, to combine picking and packing systems by

having the system process not only picking items but container selection, loading configuration,

and loading/picking sequence as well. This would eliminate time spent waiting for picked items

to be packaged and would not back up distribution lines. Larger warehouses use this method

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through a warehouse management system (WMS) which tells the picker exactly what is needed

thus helping the workflow. Another solution, according to Giannikas, Lu, Robertson, and

McFarlane (2017), would be an interventionist approach that would allow the picker to have

more freedom in the picking process to make their own decisions to create efficiency in sending

out items. Richards (2017) agreed with this indicating that a warehouse runs solely on the

effectiveness of the systems in place to ship out items.

To model the picking process and determine the efficiency of the workflow a simulation

model was built using ProModel. As Harrell and Price (2003), stated “ProModel is a simulation

and animation tool designed to model manufacturing systems of all types quickly and

accurately.” As for the time studies we performed, ProModel offers Stat::Fit, a software package

that can be used, analyzes for goodness-of-fit analysis of observational data.

It was important to produce an accurate representation of the workflow and the area

designed for shipping so that, by the use of simulation modeling, we could make projections on

prospective improvements initiatives. We also knew that even before the simulation models were

developed, an adequate understanding of the processes at-hand would facilitate our search for

solutions. Tearwattanarattikal, Namphacharoen and Chamrasporn (2008), clearly supported the

need for modeling: “the concept of simulation technique is to imitate the real system as a model

and after that use the model to work in many conditions and study the effects to evaluate the

solution strategies for the real system.” We developed a blueprint of the production facility in an

accurate scale on the simulation background.

An ERP system is beneficial to every aspect of a company because the software brings all

areas into one database to allow members of each process to be aware of what progress is being

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made around the company. According to Themistocleous, Roseman, Loos, Buonanno, Faverio,

Pigni, and Tagliavini (2005), many growing companies have shied away from this approach

because they feel a new form of organization will stunt growth and affect the complexity of

processes. ERP’s are beneficial because they create organization and manage processes that

include large amounts of data. Sadrzadehrafiei, Chofreh, Hossenini, and Sulaiman (2013), agree

with the aforementioned authors by adding that the ERP systems are beneficial to companies in

the food industry because of traceability and the organization of the bill of materials. This ERP

system helps bring all aspects of a company together, so everyone is aware of what is in each

area. Implementation of the ERP system is an important part of the system’s success according to

Hong and Kim (2002).

When many companies began to shift their systems to technology ERP systems, the

foreign concepts caused a pushback from workers, this push back led to costly failure, as

companies wasted opportunities to improve. To prevent these losses, it was suggested that ERP

systems be implemented by following a ‘five-stage process’: project preparation, business

blueprint, realization, final preparation, and “go live” and support (Ehie and Madsen, 2005).

Each of these stages would serve as checkpoints and help get the entire company involved to

help the implementation be a success. Scott (2008) agreed with the notion that the

implementation of ERP systems is typically complex. Organization is needed in most companies

and ERP’s are a good way of doing so but often it feels like starting from scratch when all

physical processes are now done on software. As the ERP system is used more and more the

software is user friendly but will be difficult at the beginning of the process.

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A tool beneficial to companies is the development of key performance indicators (KPIs).

These quantifiable indexes are aimed to help companies measure their performances through the

years. As Parmenter (2020) suggested, performance should not be considered a random exercise,

but rather should a product of known critical success factors (CFS) which are communicated to

associates who can work based on these. He also proposed to embrace Kaizen as a measure to

further progression. With this continuous improvement mentality, it is important to highlight that

KPIs are not a fixed number, but, on the other hand, these change over time as the company

improves. While businesses increase in popularity and size, it is hard to keep track of the

performance of each department. KPIs make this task easier, by giving every person, department,

and organization overall, a quantitative measure of their progress.

2.3 Deliverables

With the principle of Kaizen in mind, we aimed to assist New Braunfels Smokehouse

with their productivity improvement efforts in multiple areas of the company. Performance

tracking is not an easy task; having too much information at hand can be considered a tedious

assignment. For this reason, key performance indicators would be created in an effort to make

this job easier through the use of visual aids. With the use of graphics, it would be easier to the

assess improvement and detect the areas of concern within the business. By creating various

KPIs we aimed to help the company find flaws in their processes as well as opportunities to

maximize profit.

We conducted a simulation study as part of this senior design project; our goal was to

design a workflow that would increase the productivity of picking and packing by reducing the

time spent for an order to enter and exit the picking process. Reducing these times would allow

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the company to process more orders daily. We used ProModel, a digital simulation software

package, and the Plan-Do-Study-Act (PDSA) approach. This approach allowed us to

contemplate various scenarios in order to determine which was the most suitable for the

area. To contrast the former workflow and the one we designed, we built two simulation

models. We expected that an analysis of the simulation results would produce information that

would help us improve the workflow at the facility.

As part of the implementation of the new ERP, a few documents were created. The

formulation specifications are the manufacturing version of a recipe book. All the recipes were

entered into a document and organized by what process was need and which ingredients were

used. For example, some products have brines included in cooking process while others do not,

and there are over 15 options for those brines at the Smokehouse. From the formulation

specifications, a bill of materials was created. Taking the information given regarding products,

the cost of each good and ingredient was calculated so management could track purchases and

profit. At the time of this senior project, the ERP system was a new tool for everyone at the

Smokehouse; the scope of this project included the creation of an initial prototype of a few

sections of a standard operating procedure (SOP) manual to facilitate its implementation and

utilization.

2.4 Standards

New Braunfels Smokehouse, in order to operate, is required by law to comply with

multiple standards set by federal agencies/departments; the main goal of the standards is ensure

the welfare of their employees and customers. Three of the most important agencies that the

Smokehouse is under supervision are the Occupational Safety and Health Administration

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(OSHA), the Food and Drug Administration (FDA), and the United States Department of

Agriculture (USDA). These branches of the government set standards to multiple industries with

aims of delivering adequate work environments for the people and safeguard the public health.

OSHA is part of the United States Department of Labor. Its purpose is to ensure safe and

healthy working conditions to every employee in the US by setting standards and providing them

training, outreach, education, and assistance. OSHA has a section that is called the

“meatpacking” Voluntary Standard II, which includes, rotation and job enlargement, rest

allowances, overtime control, shiftwork design and training. It has also compliance regulations

for the general industry of meat packing, including a decent walking and working surfaces, exit

routes and emergency planning, adequate ventilation, a safety process to manage highly

hazardous chemicals, personal protective equipment, sanitation and many more.

The Smokehouse follows OSHA standards and the aforementioned voluntary ones.

Before entering the “hazardous” part of the plant–i.e., the shipping and packing area and the

meat preparation and packing area–, upper management or area supervisors explain the safety

protocol to any visitor or new staff member. No person can enter these areas without appropriate

safety boots, glasses, hairnet and helmet. Steel-toe boots are mandatory in order to protect the

person’s feet. Glasses are used to protect the eyes from any chemicals or material that could be a

possible hazard. A hairnet is worn to follow internal sanitary standards, thus avoiding possible

contamination of meat and dairy products. Some employees are required to wear a safety helmet,

which has various purposes: (1) it prevents a person’s head from being hit by anything that could

fall on top of it and (2) it helps identify the status of the person. For example, red helmets are

used by visitors. The helmet is red because is a very bright color that could be seen in any danger

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situation, or if an exit procedure must be carried out, other employees must guide the red helmets

the nearest exit route. A light blue helmet is used by the group leads, a blue one by area

supervisors, a white one by administrative employees, a green one by management, orange ones

for the IT department, and grey helmets for maintenance personnel.

Rest allowances are strictly observed at the Smokehouse. Every employee has the right to

two 15-minute breaks and a 30-minute lunch break each day.

In the meat preparation and packing area, every standing surface has its own drain so that

the liquid spilled on the floor will flush instead of being stuck and cause any accident. In the

meat slicing area, every employee should use a special type of glove that would prevent

accidents with the industrial knife they work with. In the smoking section of the plant, there is

also a special type of safety dress code.

Before employees begin working at the plant, they are trained and are informed of the

safety regulations, including exit routes and emergency procedures. Plant sanitation is overseen

by the FDA and USDA agencies.

The Food and Drug Administration operates under the Department of Health and Human

Services, also known as Health Department. As mentioned, the mission of the FDA is to protect

the public health. To achieve this, they are responsible of numerous operations like the regulation

of the production, distribution, and marketing of food, cosmetics, human and veterinary, drugs,

among others. New Braunfels Smokehouse, as a food production plant, is regulated by this

entity. The processes overviewed range from the time food arrives to the plant, to the brine

preparation, to cooking process, to the packaging. Each stage of production must comply with

specific regulations pertinent to the condition of the food and the handling. The area in which

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raw meat is received must not have any contact with any other to prevent cross-contamination of

potentially harmful bacteria. For the stage of brine preparation, the quantities of ingredients must

be specific and should not exceed the maximum allowed, as some of the condiments are harmful

when used in large proportions (e.g., cure and phosphate).

Timing is essential for the cooking process. The time the meat is placed in the

Smokehouse must be carefully calculated. In this case, undercooked meat means that bacteria

may not have been killed; overcooked meat accounts for the possibility of improper digestion

and metabolization and other undesirable consequences. As soon as the smoking process in

concluded, the meat must be placed in a cold place to avoid the build of moisture. It is necessary

that the products are vacuum-sealed to prevent freezer burns and dehydration.

Boots must be disinfected when walking from area to area to prevent cross-contamination

of food. Likewise, alongside OSHA regulations, the usage of colored coats is a way of avoiding

the mentioned cross-contamination. Gloves and masks are mandatory to prevent contamination

from human bacteria (e.g., those in saliva).

New Braunfels Smokehouse is a food distributor whose main focus is meat. According to

the United States Department of Agriculture’s (USDA) meat production standards, all meat

products intended for “commercial sale” must be inspected. To insure this, New Braunfels

Smokehouse has a USDA inspector present at the plant during all production hours. This

inspector has been assigned to the Smokehouse to make sure all USDA standards and regulations

are being met at all times.

The USDA features two types of inspection: federal, and state. The Federal Meat

Inspection Act (FMIA) requires the USDA to inspect all product meat for human consumption.

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A large part of the FMIA regulation begins at the slaughter of an animal. This process is not

present at the Smokehouse because the meat is ordered and shipped to the plant. This meat must

be inspected by farm workers at the vending location when leaving the slaughter location and

inspected by Smokehouse workers when it arrives at the production center. This inspection at the

plant starts with the carcass of the animal and processing of the animal’s meat. This is the first

inspection and manufacturing process present at the Smokehouse. If a product is seen as unfit for

production, it will be taken out of stock and marked as a loss. However, if this is not done and

caught later in the process the product is then put on recall which forces the entire production

line to shut down and brings significant time and money loss to the Smokehouse.

State inspection is similar to that at the Federal level. State regulation differs from state to

state for goods sold within their respective state. The Federal program must approve all State

regulations. New Braunfels Smokehouse sells product across the United States, so the State

Regulations are not in place, as the Federal Regulations are a priority.

While our team did not directly follow or work under the previously mentioned

standards, the Enterprise Resource Planning (ERP) system includes a product-tracking a feature.

In the event that a product must be recalled, the USDA can follow the ingredient in question and

determine what products that ingredient went into, what batch, and where these products were

shipped or sold to. The implementation of the ERP system will make USDA tracking more

efficient and help track product quicker than the paper format used in the past.

New Braunfels Smokehouse must comply with the aforementioned standards, which are

not only meant to maintain the safety and health of all associates and the plant in general, but

also to ensure the company is delivering high-quality products to their customers. Failure to

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comply with these regulations may lead to a lawsuit, the payment of a large fine, and/or the

shutting down of the plant.

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CHAPTER III: DEVELOPMENT OF KEY PERFORMANCE

INDICATORS

3.1 Problem Definition

This chapter was completed and written by Ting King Yong. As mentioned before, one of

the opportunities for improvement provided by New Braunfels Smokehouse was the creation of

key performance indicators (KPIs) to assist upper management with their performance tracking

and to help set new requirements and goals. KPIs are generally utilized to facilitate the

understanding of a company’s standing through information comparison with graphics. The use

KPIs at the Smokehouse made it possible to recognize issues that needed the company’s

attention as well as identify new opportunities to increase sales. In the section labeled Analysis

we describe in detail what information each KPI is aimed to provide.

3.2 Constraints

A major constraint I faced was the abundance of raw data dispersed throughout different

forms and the absence of data consolidation (e.g., databases). Many data sets were kept on paper

and had to be logged into an electronic source to make their summarization and analysis easier.

Another constraint I faced was that most pick lists for November and December were thrown

away or not signed; this reduced the number of lists available for me to use in the analysis,

causing my figures to be skewed. Likewise, I faced a traceability constraint since there was no

way of tracking which items were being send, causing missing customer orders to no be correctly

fulfilled by no sending the correct item or not sending the item at all.

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3.3 DMAIC Analysis

I utilized the Define, Measure, Analyze, Implement, and Control (DMAIC) method. The

first three phases of this methodology were essential for the successful creation of the KPIs,

while the implement stage was being conducted by the time this project was concluded. The last

phase, control, was out of the scope of the project due to time constraints.

Define. I aimed to tackle the problem of not having indicators that allow a better and

easier understanding of the performance of different departments. Three KPIs were created

focused on multiple areas of the company, while a fourth one was created based on another one,

to explain more in detail the performance of a specific area.

The first KPI focused on pickers performance. The calendar year is divided into two

major sections: the regular season (January to October) and the high-demand season (November

and December). During the regular season, there are only one full-time and one part-time

pickers. During the high-demand season, temporary pickers are hired to ensure all orders are

fulfilled in a timely manner. This KPI aimed to explain the improvement of pickers from

November to December. I only focused on these two months, as this period is when

approximately 80% of the sales volume for the entire year takes place.

Another KPI focused on marketing intelligence in regards of how sales for an item are

affected by the page on the catalog it is placed. We had two types of readers: front-to-back and

back-to-front; the former can be considered the “typical readers” as they start from the cover,

while the latter are those who start from the back of the catalogs. In order to reach the largest

number of customers, it was crucial to include strategies that would catch the attention of non-

typical readers. With this goal in mind, this KPI as aimed to give an overview of what pages

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should show the more important (i.e., the top selling) items, as these pages are the ones that catch

the most attention.

I analyzed a KPI aimed track customer complaints. New Braunfels Smokehouse offers

customers a 100% satisfaction policy. Unsatisfied customers may file a complaint that will lead

the Smokehouse to either reimburse them the amount of their purchase or ship them an exchange

product at no additional cost. These complaints are recorded in what the Smokehouse refers to as

“yellow papers” (a referential image is shown in Error! Reference source not found.). At the

beginning of this project, I was asked to evaluate these complaints and break them out to

determine the major areas of concern that were causing such expenses of refunding or

exchanging products, which can be avoided.

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Figure 1 - Yellow paper

The KPI labeled shipping breakdown was created with the intention of giving a deeper

and more detailed explanation of the main factor causing customer complaints. Later in this

report, all the factors affecting customer satisfaction are explained. From such analysis, it was

concluded that the shipping department was the most prevalent reason mentioned on the yellow

papers. The purpose of this KPI was to explore which factors needed to be improved.

Measure. In order to create KPIs, it was essential to gather relevant data. Most of the

information needed to develop the KPIs already existed as raw printed data that had to be logged

into multiple spreadsheets for their further analysis. For the pickers’ performance, data were

collected from the picking lists of November and December, as shown in Figure 2. The pick lists

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were used as a guide for the pickers, who were in charge of fulfilling customer orders. The lists

included what items and how many of these each order needs to be completed. To assess how

each picker performed in such period of time, I asked them to sign the lists used by each one.

For the marketing study KPI, I asked Arturo Veliz, the chief financial officer (CFO) of

the company, for the catalog sales breakdown for 2018 to 2020, to examine which catalog pages

contributed the largest sales volume. By comparing the sales for those years, I was able to

identify which pages provided a strategic advantage when placing top selling products. The

information gathered for customer complaints and shipping breakdown, respectively, was

collected by examining the yellow papers for an entire year (August 2019 to July 2020). In

Figure 1 we display this document. It contains information from customer complaints, including

the date and the reason for the complaint, among other factors. The data collected were recorded

in various Excel spreadsheets, which allowed for an easier summary.

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Figure 2- Pick list

After recording the information in multiple Excel spreadsheets, I used Tableau, a

sophisticated data visualization software package, to create the figures that would later be used to

represent the KPIs. In the following section, Analysis, I provide details on the execution of the

third phase, as well as the status of the last two phases of the DMAIC cycle.

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3.4 Analysis

Pickers’ performance KPI

The daily average number of orders picked in November and December is shown in

Figures 3 and 4, respectively. I excluded all pickers who worked less than seven days during the

high-demand season, as they would affect the analysis by creating numerous outliers that would

have affected the calculated overall average that the Smokehouse used to assess performance of

pickers. Taking this into consideration, the calculated mean for November was 13.16 orders

(displayed as the dashed line on Figure 3). From the 14 pickers working on this month, 43%

performed above average (shown in red) and 57% below average (shown in grey).

Figure 3 - Daily average orders picked during November

In December, the mean increased to 14.39 orders (displayed as the dashed line on Figure

4) and, from the 10 pickers working on that month, 60% performed above average (shown in

red) and 40% below average (shown in grey). This small improvement appeared to indicate that,

within a month, there was an overall improvement of 17% and that, on average, pickers also

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Figure 4 - Daily average orders picked during December

increased their average daily performance by 1.23 orders. In Figure 5, a comparison of the

improvement in performance for each picker is exhibited. From the eight pickers that performed

below average during November, two of them left the company, four of them outperformed

during December, and two continued performing below average. However, their daily average

number of picked orders increased. One picker went from above average to below average, but

nonetheless, his daily mean increased as well.

Marketing Intelligence KPI

It was possible to observe how disperse the sales for the products on the catalogs were for

2018 to 2020. In order to analyze this information, I grouped the revenue yielded by products

depending on the page these were placed in the catalog. In Figure 6, we furnish plots of sales

dispersed throughout the catalog from 2018 to 2020. In 2018, most sales were focused only on

the first half of the catalog, while in 2019 the most sales were dispersed to the first two thirds of

the catalog. Finally, in 2020 it can be noticed that sales were more dispersed than the previous

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years. This significant change on how sales are dispersed suggested that customers were reading

the entire catalog and not only the first pages.

Figure 5 - Daily average orders comparison for November and December

Figure 6 - Sales Comparison from 2018 to 2020

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I proposed that, in order to catch even more the attention of customers, it would be

advisable to place top selling products (including pre-made packages, which are cheaper than

buying every item separately), at the front and also at the back of the catalog, to catch the

attention of both types of readers. By grabbing their attention with interesting items, they would

most likely wish to read the rest of the catalog to see what other products they would be

interested in purchasing.

Customer Complaints KPI

In order to minimize the expense of having to issue refunds or sending an exchange to

customers, it was necessary to breakdown all those complaints into reasons/departments. For

this, I developed a table with each reason/department and the quantity of complaints (Table 1) I

built a Pareto chart to provide a visual representation of the breakdown of these complaints,

which is displayed in Figure 7. Although this chart did not strictly follow the 80-20 pareto

principle (which suggests that 80% of the damages are caused by 20% of the factors), it still

followed the ultimate principle that the majority of the damages (complaints) were caused by a

very small number of factors (reason/department)—60% of the complaints came from 20% of

the reasons (60-20) and 80% of the complaints were cause by 33% of the causes (80-33). This

analysis provided information on which reasons/departments needed to be solved first, as these

are the ones with most complaints.

We concluded that the three major factors causing customer dissatisfaction were

shipping, delivery, and customer errors such as entering the wrong shipping address when

placing the order. Those causes represented 20% of all the reasons and caused almost 60% of the

complaints during that year. However, the first factor (shipping) had a significant difference in

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Reason/Department Quantity Percentage

of total

Running percentage

of total

Shipping 475 26.7% 27%

Delivery 290 16.3% 43%

Customer 281 15.8% 59%

Poor quality 216 12.2% 71%

Customer cancelled 153 8.6% 80%

Office 110 6.2% 86%

Packing 105 5.9% 92%

Did not get out of here on time 49 2.8% 95%

Del damaged - spoiled 39 2.2% 97%

Customer loyalty 24 1.4% 98%

Internet 11 0.6% 99%

Ability commerce 8 0.5% 99%

Computer issues 7 0.4% 100%

Items purchased from 5 0.3% 100%

Unhappy with substitution 3 0.2% 100%

Table 1 - Customer complaints breakdown into reasons/departments

Figure 7 - Pareto chart for customer complaints per reason/department for August 2019 to July 2020

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the number of complaints compared to delivery and customer. By itself, shipping (which

accounted for 0.07% of the factors) was the cause of more than a quarter of all complaints.

Having this information on hand, it was apparent that the department that needed the most

attention was shipping, making this the top priority.

Shipping breakdown KPI

Being aware that shipping needed the immediate attention, another need arose:

determining the specific reasons causing customer dissatisfaction in this department. A pie chart

including the specific reason for complaints within the shipping unit is presented on Figure 8.

Items left out and wrong items being sent were the two major problems in the shipping

department, accounting for 55% and 35% of all complaints for that area, respectively. By

creating and analyzing the aforementioned KPIs, I also helped Kenia on the creation of a new

workflow for the shipping area. When doing this, I realized that the Smokehouse’s former

workflow did not only cause many points of congestion within the area, but it was also the

reason why many orders were not being adequate fulfilled. This, at the same time, led to

customer complaints.

In Chapter III we provide an in-depth description of the issues concerning the workflow

and how a new improvement flow was created. Looking at the picking process, the path the

pickers followed, and their guides (pick lists), I was able to appreciate the root cause of the

problem. Firstly, the pick lists were supposed to be followed in a top-down order: pick the items

listed on top first and finishing with the items at the bottom of the list. The order the items were

initially placed on the pick lists was:

Jarred goods → Cheeses and desserts → Frozen meats

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Figure 8 - Pie chart with specific reasons within the shipping department

However, since jarred goods and desserts are fragile items, many pickers decided to pick

them last to prevent breakage. While this appeared to be a smart solution, it had the undesirable

effect that many workers would wind up forgetting these items. Thus, the solution for this issue

was the creation of a new workflow that allowed smooth transportation and a change in the pick

lists as:

Frozen meats → Cheeses and desserts → Jarred goods

The proposed workflow also followed the new order for the pick lists, making it an

effective one. Likewise, the implementation of the ERP system was also aimed to help this issue

by adding a tracking system. This system would make the tracking of each item in the plant

possible by scanning each product at every station they are moved to. It would help verify

whether or not the orders were being sent with the correct items.

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By the time this senior design project was concluded, the fourth phase of the DMAIC

analysis, implement, was being conducted. Focusing on the customer complaints and shipping

breakdown KPIs, the design and implementation of the new workflow and a change on the pick

lists order, has already shown some improvement within the shipping department. While talking

to our supervisor, Josh Klein, he stated that this past high-demand season was record-breaking.

He pointed out that, with these changes they were able to achieve several things, like fulfilling all

orders by the time they were expected to be shipped and triple the number of orders shipped in

one day with regards of previous years. Likewise, it is worth mentioning that these record

number were done with a smaller number of employees that in prior years. Within this same

stage, the aforementioned inventory tracking system is being implemented by the time the

project has concluded and this report is being turned in.

For the control phase of the DMAIC analysis, the recommendations and plans are to

include the name of the pickers in the inventory tracking system to make it plausible know which

employee picked which order, this way when a customer complaint comes it will be easier to

know who was in charge of such customer order. This system will also make possible to assess

how well the pickers are performing—not only in matter of how fast they are, but also in how

well they fulfill the orders—and help create incentives for them to do even better and to come

back every year during high-demand season. Similarly, the creation of a database for customer

complaints will make is easier to calculate if the number of these have been reduced or not and

what needs to be done to help this number decrease.

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3.5 Summary of Results and Implementation

An analysis of the information concerning the pickers performance KPI revealed that,

within one month, the average daily orders picked for each picker was increased by nearly 10

percent. Also, since this analysis was completed for each of the pickers, it was possible to

determine who the outstanding performers were. With these results, I aimed to also let the

company know who to hire for the next high-demand season.

The results obtained by the implementation of the marketing study KPI made it was clear

that part of the customers were back-to-front readers and that, by catching the attention of clients

at the beginning and at the end of the catalog, many customers may be prompted to read the

entire document. By having them checking out the entire catalog, the chances of them purchasing

more products are higher, possibly increasing this way the overall sales and the company’s

earnings.

The customer complaints KPI provided information on which reasons were causing the

most customer complaints. With the analysis of the yellow papers, it was possible to find out that

shipping was the area causing most costumer complaints, and therefore needed the necessary

attention to have this solved. In order to provide the necessary tools to help the shipping

department lower their mistakes and avoid customer complaints, the shipping breakdown KPI

was essential. This indicator provided insight on the specific issues happening in this area. It was

now possible to determine that sending the orders with missing products or sending the wrong

products was a prevalent mistake. To tackle this matter, a new workflow and a change in the pick

list order were created and applied. Likewise, the ERP will implement an inventory tracking

system, using barcodes and scanners that will allow the effective traceability of the different

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products offered. This system will alert the users when an order is not correctly fulfilled if an

item is missing or if there is an extra item. This is aimed to help minimize the mentioned errors

causing customer complaints.

3.6 Conclusion

This phase of the project was completed successfully. When I presented this information

to Mr. Klein, our project supervisor, he was satisfied with the results and mentioned that this

information and figures would be of great help when exploring future opportunities to grow. Mr.

Klein and Mr. Veliz, the company’s CFO, were especially surprised by the results revealed by

the customer complaints and shipping breakdown KPIs. They mentioned that while they knew

the Smokehouse received customer complaints, they were not aware of their quantity and how

negatively these affected their business. Overall, the objectives of giving a visual representation

of some activities/departments of the Smokehouse was effectively met and highly appreciated by

upper management.

The development of KPIs at the Smokehouse was consistent with the six-sigma DMAIC

approach. The problem was originally defined as the need to establish clear performance

measures. The measure phase consisted in collecting information on pickers performance,

catalog sales (marketing intelligence), customer complaints, and the breakdown of complaints

for the shipping department. The analysis phase included work conducted in conjunction with

my colleague Kenia Artola to determine the impact of the existing workflow on the KPI I was

tasked to develop. The improvement activities of this portion of the project consisted in the

implementation of the KPIs to measure performance. Upper management appreciated the

beneficial impact provided by the newly developed visualizations. Putting the KPI into place was

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a necessary task in the Smokehouse’s major effort to install a new ERP system. I expected that

the KPIs we developed in this senior project would continue to be used and improved in

accordance with the needs emerging from the implementation of the ERP. This would be part of

the control/sustain DMAIC phase.

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CHAPTER IV: SIMULATION MODEL

4.1 Problem Definition

In this chapter, we describe the implementation of a simulation model that was developed

to facilitate the process of designing an improved workflow at the facility. This chapter was

written by Kenia Artola.

The existing workflow for the shipping area was not efficient. The picking and packing

areas had the stations arranged in a way that their pickers began and ended their cycle in the

same area. The pickers chose a wagon, picked the jarred goods, prepared the orders and came

back to the same point to place the wagons for packers to finish the package preparation. The

problem was that the area in which pickers began and ended their job was too narrow. At first

sight, empty wagons could be seen waiting to be grabbed and full wagons waiting to be packed.

In this same narrow space, pickers were grabbing the jarred goods for each order while their

wagons were parked between full and empty wagons, and there were also other pickers placing

order labels in each separate box in order to prepare their wagon and begin their picking cycle.

The jarred goods were placed first on the picking list, but last on the picking process.

Since the jarred goods were fragile and could brake during the picking process, upper

management’s first solution was to leave their picking process last. The problem with this was

that since they appeared first on the picking list, once pickers ended in other locations, they

thought that they had completed the orders and frequently forgot about going back to the jarred

goods–where they began– and completing their orders.

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I was tasked with developing a simulation model to conduct what-if scenarios concerning

layout changes to the shipping area. In this chapter I describe the model and an analysis of its

results; I also provide a summary of the recommendations provided to the management.

4.2 Constraints

The constraints faced regarding the development of the simulation model included an

unexpected layout change that made it necessary for me to revise the model. I started working on

the model in September by performing time studies about the picking process. In the middle of

the fall 2020 semester, a cooler was added to the shipping area, changing the previous workflow

and making previous time studies inaccurate. In Figure 9, I show the area layout we found at the

onset of this project; this is the area I modeled on ProModel. The cooler shown in the image used

to be a small fridge; it later became a whole room to store more inventory of these products.

Afterward, at the beginning of the high season, the spot I had selected for the wagon preparation

station was designated to become an area to store poultry boxes needed for the ready-to-eat

(RTE) crew (Figure 10). The boxes could not be placed anywhere else due to the space they took

and there was no other place available for me to locate the new station. The box named “wagon

prep” was my first proposal of a new station that could be added to the new workflow.

Another proposal interfered with the cooler’s exit and the route available for pickers to

exit was very narrow and would result in an uncomfortable path. This other proposal is shown in

Figure 11, also, in the box marked as “wagon prep.” The exit route shown in the figure was very

narrow due to the presence of shelves and other equipment– such as labeling tables– placed in

the way. Time was a major constraint. The high-demand season starts in mid-November and

goes until the end of December. While it would have been idea to collect data during the entire

high season, the problem was that the academic semester at the University ended on November

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25th. The time studies used for the simulation were performed from November 1st to November

24th.

Figure 9 - Former Workflow layout

4.3 PDSA Approach

I used the Six-Sigma based Plan-Do-Study-Act (PDSA) approach as a guide to compare

various picking processes scenarios in order to find the one that would increase the process

productivity. I built several models as proposals of new picking processes for our superiors to

choose from given the constraints present in the area.

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My senior project colleague Ting found that there was a large number of complaints

about missing items from the jarred goods. For this, my team along with upper management, had

to come up with the idea of moving the jarred goods to the last part of the picking list.

Figure 10 - New wagon preparation station (first proposal)

Plan. We brainstormed and planned for improvement so that I could offer management

several options to consider. Once Josh Klein (general manager) and Oscar (area supervisor)

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found one of these proposals suitable, we started implementing the changes in the shipping area.

The proposal chosen was the one presented in Figure 13 and the changes are explained in the Do

part of this section.

Figure 11 - New wagon preparation station (second proposal)

Do. The new wagon preparation station was clearly marked on the floor and all wagons

were moved to the new station. The problem related to the missing jarred goods items was that

the pick list was organized by item location. An example is presented on the pick list we show in

Figure 2: the first item “code” is “W6 40 B4.” This code means that the item is located in area

W6, wagon number 40, and it is the 4th box from left to right on shelve B. Since all the areas

were marked by a W and a number, the jarred goods had the same letter reference as other items,

but these appeared first on the pick list because the area they were in was first than the other

locations in the list. In order for the change not to be drastic for the the ready-to-eat–crew (RTE),

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who were responsible for replenishing inventory), we decided to place an X instead of a W to

represent the location of the jarred goods area. Now, the area of the jarred goods would not be

represented by a W but by an X, and, by default, the system would place them last in the picking

list. In Figure 12, we furnish the new pick list, on which X’s can already be seen at the end of the

list, representing the jarred goods location. For instance, the last item features code “X4 01 A5”,

meaning that the item would be found in area X4 (which is the jarred goods location), wagon 1,

and it is the fifth item located from left to right on shelve A.

After changes such as the implementation of the new wagon preparation station, the

change on the pick list order and the new physical layout changes were made at the facility, I

started building the simulation model and included these aspects of the layout while performing

time studies on the picking process along with Ting. One of the initial steps was to create an

AUTOCAD blueprint of the area to be simulated. In Figure 13, we display the new layout; the

background is the AUTOCAD blueprint Ting developed. Ting and I measured every packing

table and station in order to have an accurate representation of the shipping area. I broke each

stage of the process into stations, which I modeled as ProModel locations. These ProModel

locations in both simulation models represent the locations on the actual shipping area. They

consist of the freezer room marked as “Freezer”, the cooler room marked as “Cooler”, the jarred

goods area marked as “Jarred Goods”, the queue for packing tables 1 and 2 created by wagons

waiting to be packed, both packing tables and the pallets for each table where completed orders

are placed waiting to be shipped, all locations marked accordingly. In both simulation models I

added the “Boxes” location as an indicator of pallets containing boxes for the packing process,

one at the side of each packing table.

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Figure 12-Actual image of updated pick list with jarred goods at the end

Even though they were not locations with direct relation to the model, I added them up to

make the simulation model as similar to the real shipping area as possible. There are differences

in other locations between the models. For the old layout simulation model, I added a wagon

preparation station even though there was no physical area marked for this location as in the new

layout simulation model. In the old layout model, I placed this location in the same area as the

jarred goods since both locations were merged before; they prepared the wagons in the same area

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they loaded the jarred goods into the wagons. This merge created a ProModel queue, marked as

“QueueJGWP”, for those wagons waiting to pick up the jarred goods. The new layout simulation

model does not have a queue for this area since after the changes were implemented in the

Figure 13-New workflow design simulation model

shipping area there was no merge between these locations and the queue was eliminated. We

show the ProModel locations code both simulation models in Figure 14 and Figure 15,

respectively. In both codes, at the side of the location name, the maximum capacity of wagons

allowed in each is stated at the side, along with how many units are of each location and the rule

for all, which is the first in-first out rule (FIFO.)

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The ProModel entities represent each cart type used in the picking process. The way

that New Braunfels Smokehouse processes the orders is to divide the orders by picklists,

and each picklist as one of the five cart types. In fact, each cart type is stated at the top of each

picklist so that the pickers prepare their wagon accordingly. Cart types 1 and 6 are not included

in the model since they represent different prepacked orders, and as the name suggest, there was

no picking process for them since they are already packed beforehand. Cart type 2 is designed to

pick up orders consisting of multiple single items. Cart type 3 consisted of orders containing a

maximum of ten items per product; the maximum number of different products was six. Cart

type 4 was designed to pick up orders with ten items per product; the maximum number of

different products was ten. Lastly, cart type 5 was a default setup for any other order with a

larger number of products than the previous cart types can contain. A wagon represents

one entity and each of these entities represents 16 orders from the same cart type, since the

maximum capacity of orders to pick is 16 per wagon.

The path each picker takes is represented by the ProModel path network. Once the

wagon is prepared, they follow a picking path. After preparing the wagon, they collect items

from the freezer, cooler, jarred goods and then drop the wagon on the packing tables. For both

simulation models, the path networks represent a collection of nodes that make up the picking

process flow.

In Figures 16 and 17, we display the ProModel codes of the path networks for the

simulation models. This process is broken into two since depending on the cart type, the flow

differed at the end of the packing tables. Cart types 2 and 4 should be packed on table 1, which is

why I named their path as “picker’s path 1.” Cart types 3 and 5 should be packed on table 2 and

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follow “picker’s path 2”. To the side of the nodes, there is a column that indicates whether the

path is unidirectional, marked as “Uni” or bidirectional, marked as “Bi”, as well as the distance

between each pair of nodes.

Figure 14- Old layout simulation model code part 1

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Figure 15- New layout simulation model code part 1

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Each location contains its own node in the model, and the nodes that do not represent any

location represent a turning point in the picking process. By turning point, I refer to a location in

which the picker makes a turn or changes their direction. We exhibit the code for all the nodes

representing a location in Figures 18 and 19, marked as interfaces.

The simulation model resources represent the pickers in the system. In Figure 20, we

present the resources code for the old layout simulation model. Similarly, in Figure 21, we show

the resources code for the new layout simulation model. During the high season, the

Smokehouse hires temporal pickers, and along with the ones they have yearlong, they sum up to

nine. In the model, there are two kinds of pickers. Five of them, named “picker 1” follow the

“picker’s path 1”, and four represent “picker 2” which follow the “picker’s path 2”.

The ProModel processing tables are a representation of the whole process. It shows

which entity enters a location, how much time it spends in the location, which entity comes out

its destination, and the move logic. In my simulation models, each entity follows this process:

Wagon preparation station → Freezer → Cooler → Jarred Goods → Packing table → Packing

table pallet. Queues are included in the model as well.

In Figures 22 and 23, we exhibit the processing table for both simulations respectively. In

the table, at the side of each location, I included the time distributions for each location. The time

recorded for each location was obtained by completing time studies in which used either the

stopwatch or the continuous method. The continuous timing method consists in zeroing the

stopwatch at the beginning of the process being timed and letting in run continuously until the

process finishes. Once the process finished, the watch is stopped, and the time taken is recorded.

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Figure 16- Old layout simulation code part 2

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Ting and I measured the time it took each picker to complete the whole process up until

dropping off the wagon at the packing table. We used time lapses without completely stopping

the watch to record the time taken for a picker to complete each station. After I collected the

data, I entered it into StatFit, a goodness-of-fit program that allows the user to enter raw data to

obtain recommendations on named probability distributions that may be used in the simulation to

model the data. In Table 2, we exhibit the time distributions we obtained after feed the time

studies data for each cart and location into StatFit. The output in the table does not change since

it is the same order from beginning to end. The move logic depends on the aforementioned

“picker’s path”. Cart type 2 and 4 are picked by any of the “pickers 1” and cart type 3 and 5 by

any of the “pickers 2”.

Time Dist. Wagon Prep Freezer Cooler Jarred Goods Packing

Cart type 2 N(1.35,0.421) N(14.9,5.1) N(3.74,1.68) N(2.27,1.41) N(10.3,3.04)

Cart type 3 N(1.38,0.0361) N(15.1,3.33) ER(0.102,4) B(0.669,6,1.74,7.82) N(9.77,3.42)

Cart type 4 N(0.727,0.484) N(9.97,5.09) N(2.04,0.783) N(8.09,1.98) N(8.09,1.98)

Cart type 5 N(0.811,0.343) N(13.8,6.42) N(3.2,2.28) N(9.62,2.95) N(9.62,2.95)

Table 2- Time Distributions found by Using StatFit

The last part of the code consists of the arrivals section. Cart entity types arriving to the

wagon preparation station, the occurrences, and the frequency according to which they arrive. In

both simulation models I considered infinite occurrences since every day its different on the

number of orders they receive. However, this is somehow limited by their frequency. To

determine the frequency at which each cart type arrived, I used the time studies I had previously

completed. I separated every order by day and cart type and created a distribution using StatFit,

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using the amount of cart types picked those days. The tables for the simulation model codes are

shown in Figure 24 and Figure 25, respectively.

Study. An analysis of the results of the simulation models helped me to compare and

analyze their differences. I compared the data to my predictions and interpreted the possible

benefits that would result from the possible implementation of each model. This information is

thoroughly explained in the Analysis and Summary of Results parts of this section.

Act. The last stage of the PDSA consists in planning the next change of improvement.

Changes made in the simulation were implemented exactly the same way in the shipping area of

the Smokehouse. These improvements made the area much more efficient than other areas in the

facility. At the time of this final report, the Smokehouse had an opportunity to increase the

efficiency of its other areas. This constitutes a promising opportunity for future senior design

project teams from St. Mary’s.

4.4 Analysis

The results of the two simulation models (i.e., the current and the proposed layouts) are

shown in Figure 14 and Figure 15, respectively. The times featured in ProModel were measured

in hundredths of a second, and to make the results clearer, I converted them into minutes and

seconds format. This conversion is shown in Tables 3 and 4, respectively. In Table 3, we display

the times for average time in system for the models. Similarly, in Table 4, we exhibit the times

for average time in operation. In the model representing the former workflow layout, there were

90 exits in total. Exits represent the number of wagons that went through the whole picking and

packing process and exited the system successfully. Since each wagon represented 16 orders, the

total numbers of orders exiting the system was 1,440.

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Figure 17- New layout simulation code part 2

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Figure 18- Old layout simulation model code part 3

Figure 19- New layout simulation model code part 3

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Figure 20- Old layout simulation model code part 4

Figure 21- New layout simulation model code part 4

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Figure 22- Old layout simulation model code part 5

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Figure 23- New layout simulation model code part 5

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Figure 24- Old layout simulation model code part 6

Figure 25- New layout simulation model code part 6

The output of the simulation also shows the average time in system and the average

operation time. Time in system represents the time it took the order to complete the entire

process, including waiting time in queues. Operation time represents the time in which an order

went through a productive process in the system–i.e., time spent in packing–. In this model,

ProModel separated the time for each cart type since they were different entities. Average time in

system for the different cart types were: (1) 53 minutes 25 seconds, (2) 55 minutes 04 seconds,

(3) 46 minutes 50 seconds and (4) 58 minutes 04 seconds. As for the average time in operation,

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the results were: (1) 33 minutes 40 seconds, (2) 28 minutes 40 seconds, (3) 21 minutes 27

seconds and (4) 31 minutes 13 seconds.

In the model that represented the proposed design workflow layout, there were 97 carts or

1,552 orders exiting the system. For the average time in system, the results were: (1) 48 minutes

54 seconds, (2) 46 minutes 45 seconds, (3) 39 minutes 59 seconds and (4) 46 minutes 53

seconds. Alternately, the time in operation results were: (1) 33 minutes 16 seconds, (2) 28

minutes, (3) 23 minutes 44 seconds and (4) 30 minutes 30 seconds.

4.5 Summary of Results

An analysis of simulation results suggested that there would be a major decrease in

average time in system from the old layout simulation to the new one, depending on the cart

type. The decrease in time for each cart were (1) 4 minutes 31 seconds, (2) 8 minutes 10 seconds,

(3) 6 minutes 51 seconds and (4) 11 minutes 11 seconds. We would focus on these results to

determine if the new workflow really helped to improve the shipping area’s efficiency. Average

time in operation did not have a significant change. The operation time is the time spent by each

picker spent at each location, and even though it has an impact on the total processing time, the

new layout would not determine how fast the resource picks the item or how fast did it take the

packers to finish their job. This is the reason why the decrease in average in time operation was

not as significant. The differences were: (1) 24 seconds, (2) 11 seconds, (3) 2 minutes 17 seconds

(increase) and (4) 43 seconds. Furthermore, for the second model, there was also an increase of 7

wagons, or 112 orders completed per day.

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Figure 26 - Former workflow ProModel results table

Figure 27 - New workflow ProModel Results

Average time in system

Former Layout New layout Difference

53min25sec 48min54sec -4min31 sec

55min04sec 46min45sec -8min 10 sec

46min50sec 39min59sec -6 min 51 sec

58min04sec 46min53sec -11 min 11 sec

Table 3 - ProModel results for Avg. time in system (minutes-seconds format)

Average time in operation

Former Layout New Layout Difference

33min40sec 33min16sec -24 seconds

28min11sec 28 min -11 seconds

21min27sec 23min 44 sec +2min17sec

31min13sec 30min30sec -43 seconds

Table 4 - ProModel results for Avg. time in Operation (minutes-seconds format)

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4.6 Validation

The time studies used to create the simulation model were taken once the physical layout

and processing changes were implemented. To validate the new layout simulation model, I

performed a two-tailed t-test on Microsoft Excel assuming unequal, unknown variances, at a 0.05

significance level. The null hypothesis was that the observed time measurements and the

ProModel results were equal. The alternative hypothesis was that there was some difference

between them. For the test, Array 1 consisted of the observed mean of the picking processes and

Array two consisted of the ProModel average time in operation results for the new layout design.

The p-value given by the t-test performed was 0.95, and since it is greater than the significance

level 0.05, it can be concluded that we could not reject the null hypothesis. These exercises were

an indication that the simulation model had been validated. We exhibit Microsoft Excel results in

Table 5; the aforementioned p-value of 0.95 is found in the highlighted cell.

4.7 Statistical significance of simulation results

Statistical analysis was conducted to determine the significance in the difference between

the ProModel results for the old layout and the ProModel results of the new layout. I also used a

two-tailed t-test assuming unequal variances with a 0.05 significance level. The results of the test

are depicted in Table 5.

For the test, array 1 consisted of the ProModel average time in system results for the first

simulation and array 2 of the new layout average time in system results. The p-value given was

0.045, which was less than the 0.05 significance level. We concluded that there was a significant

difference between both simulation results. This significant difference means that there was a

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change between the old layout design results and the new layout design results. In Table 6, we

show the Microsoft Excel results for this t-test.

t-Test: Two-Sample Assuming Unequal Variances

Variable 1 Variable 2

Mean 28.8725 28.67

Variance 16.36849167 29.64353333

Observations 4 4

Hypothesized Mean Difference 0

df 6

t Stat 0.059706189

P(T<=t) one-tail 0.477164312

t Critical one-tail 1.943180281

P(T<=t) two-tail 0.954328624

t Critical two-tail 2.446911851

Table 5- T-test results for simulation similarity to observed time measurements

4.8 Conclusion

The objective of the new workflow was to decrease the process time as a whole by reducing

waiting times and traffic congestion caused by the conglomeration of wagons in a single area. An

analysis of simulation results suggested that the new workflow design would be successful. My

predictions and expectations were fulfilled, as there was a noticeable improvement in the picking

and packing area when proposed layout was actually implemented by the Smokehouse.

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t-Test: Two-Sample Assuming Unequal Variances

Variable 1 Variable 2

Mean 53.345 45.6325

Variance 22.55256667 15.114825

Observations 4 4

Hypothesized Mean Difference 0

df 6

t Stat 2.513288817

P(T<=t) one-tail 0.022849812

t Critical one-tail 1.943180281

P(T<=t) two-tail 0.045699624

t Critical two-tail 2.446911851

Table 6 - T-test results for significant difference between old layout simulation and new layout simulation

The picking process time was reduced, and the number of orders completed per day

increased. According to our supervisor, Josh Klein, during the 2020 winter break–the high

season–the Smokehouse managed to complete, and ship triple the number of orders they did

before. It is worth mentioning that this tremendous success was achieved which fewer personnel

and working hours than previous years. In previous years, they had 54 workers in the shipping

area working 19 hours a day. This was changed when USDA reduced their number of working

hours to 12 and the Smokehouse reduced their personnel to 32 employees in the shipping area.

The simulation study was deemed successful. Its use may also be described as part of a

rather productive DMAIC initiative. The existing layout problem was defined. The measure

phase consisted in collecting statistics and documenting the limitations of the layout in terms of

efficiency and productivity. The design and development of the simulation models were

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conducted as part of the analysis phase. While no trivial solutions were initially found, the

results of the simulation model suggested major opportunities for improvement. I was very

fortunate to witness the implementation of my recommendations. A new layout based on

simulation results was set in place. The DMACI control/sustain phase was outside the scope of

this project, but recommendations were given to the Smokehouse leadership team to carry it out

in an effort to determine ways to improve the flow of products.

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CHAPTER V: ENTERPRISE RESOURCE PLANNING

5.1 Problem Definition

In this chapter, we describe the work related to the implementation of an Enterprise

Resource Planning (ERP) system at the Smokehouse. This chapter was written by Serena

Contreras.

In previous years, New Braunfels Smokehouse held and tracked records on paper instead

of digital documents. An opportunity that arose from this policy was the creation and

implementation of an ERP system. The purpose of this system would be to help management and

warehouse workers track the good that came into and out of the Smokehouse as well as track

finances. Management thought that with the new system in place, the Smokehouse would

improve record keeping and resource tracking. All ingredients would be monitored, weighted,

and inspected in each step of the manufacturing process, which would also help with finance

tracking and spending. This system would help each area of the Smokehouse communicate with

each other and follow the steps needed by USDA and Smokehouse standards.

5.2 Constraints

The most significant constraint that I faced was the lack of formulation specifications.

The Smokehouse had some recipes being followed while others differed from employee to

employee. Another constraint was that important documents were difficult to locate and retrieve.

Since most documents were on paper, it was easy to lose important information like recipes and

vendor statements. A smaller constraint was that during the ERP implementation, the

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Smokehouse was not the only team working on the project. The ERP system was supplied by

Tamlin Software, so ERP meetings and testing were conducted according to their schedule.

5.3 Methods

In implementing the ERP system, formulation specifications and bills of materials for the

products were created. An initial prototype of what the standard operational procedures (SOP)

for the ERP system would look like was developed as well. All work described here fell under

the Lean Six-Sigma DMAIC umbrella.

Spending and pricing forms were obtained to help find the costs to buy and manufacture

the product. Information on vendors, customers, recipes, and ingredients was collected and

entered into an Excel sheet. Formulation specifications were created for each item; this included

the ingredients needed at each stage of the production process. From the specifications, all

ingredient amounts were placed by product into another Excel document that calculates the cost

to make each product by batch and can be altered to calculate the cost of each product by batch

by year. This was combined with the selling price of each product to create a profit margin, for

management. This information was beneficial in the creation of the ERP system. The ERP

system featured two software platforms: AccountMate and Manufacturing Conductor. The

information was placed into the AccountMate system to make the product numbers which are

then used in the Manufacturing Conductor software.

5.4 Analysis

Upon starting my portion of the project, the Smokehouse tasked me with creating a bill of

materials to analyze the spending and profit margins for each of the products manufactured at the

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facility. To calculate spending on each product, individual formulation specifications had to be

created. In Figure 28, we display a sample of the formulation specifications, where the

highlighted box reflects missing information. This information refers back to the lack of structure

regarding certain ingredients and how workers would guess the amount put into the products.

Figure 28 - Formulation Specification

When working with the Tamlin team and test running the ERP software, information was

not initially consistent. Newly implemented AccountMate (Figure 29) was used to keep the

records and finances of the Smokehouse. Product numbers, ingredient specifications, vendor and

customer information were entered so cost and profit margins could be calculated.

Manufacturing Conductor (Figure 30) pulled this information and initialized and tracked the

manufacturing process by entering the information into the manufacturing scheduler for the raw

materials crew to start processing the meat. All steps were documented, and each product was

weighed before and after each step in the process to track loss and price per pound. Product

numbers and selling numbers were not consistent within these two software systems. Some

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numbers had additional digits to convey they were raw materials while others added numbers to

specify flavor. The Tamlin team understood the differentiation, but the Smokehouse personnel

did not. This is one of the aspects that had to be changed. After speaking with both sides, I

inferred what the miscommunication was and explained the two in the user manual. Where

inconsistencies were once present, understanding now stood.

Figure 29 - AccountMate Software Prompting Screen

Figure 30 - Manufacturing Conductor Prompting Screen

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The initial format for recipes was a paper copy, as displayed in Figure 31. This was

printed, written, and then reupdated each time a change was made. This document would have to

be replaced all throughout the smokehouse where it was needed. The ERP Recipe Format

(Figure 32) is more condensed and shows the phrase “brine.” I designed and implemented a brine

list (Figure 33) to facilitate the process of transitioning into the online format.

Figure 31 - Written Recipe Document

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Figure 32 - ERP Recipe Format

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Figure 33 - Brine List

When I initially utilized Manufacturing Conductor, it was not evident that AccountMate

was needed first. Each recipe must be completed in AccountMate before moving to

Manufacturing Conductor to produce the product. To initialize a product line, Recipe

Maintenance (Figure 34) must first be pulled up. Within this step, item numbers are required

(Figure 35). Each item number corresponds to the item number previously created in

AccountMate.

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Figure 34 - Recipe Maintenance Screen

Figure 35 - Item Number Lookup

Once the product is registered in Recipe Maintenance, the recipe remains in the

Manufacturing Conductor system until it is classified as complete or a loss. After Recipe

Maintenance is complete, the product can be seen in Work Center Maintenance (Figure 36Figure

36). At this step, the processes the product will go through are entered so the product will be

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saved in each station individual schedule. Some products go to a brine station while others go to

tossing, for example. Each product varies according to the recipe associated with it.

Figure 36 - Work Center Maintenance

Once each product is entered into Work Center Maintenance, MC Scheduler, a module

within Manufacturing Conductor (Figure 37Figure 37), receives information on what products

should be begun, worked on, or completed on a given day.

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Figure 37 - MC Scheduler

5.5 Results and Implementation

The ERP system was initially composed of two parts, AccountMate and Manufacturing

Conductor. At the time of this report (April 2021,) AccountMate had been updated and all

current information entered into the software. AccountMate is an ever-changing platform,

meaning that the information should be dynamically updated. All new vendors, customers,

ingredients, and products are placed into this platform to begin tracking and cost analysis. The

design and construction of the bill of materials was an important factor in the set-up process of

the Manufacturing Conductor software because within this software all measurements are

tracked. For instance, we were now able to track how salt was needed in each recipe more

efficiently because of the automated system and not have to manually find each individual paper

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recipe. Each product in Manufacturing Conductor was pulled from AccountMate and ran through

the system for each area of the Smokehouse to contribute to and track.

As of April 2021, the implementation of the full ERP system had progressed according to

schedule. We were informed by the vendors that this system would take a minimum of three

years to implement. Tamlin was working with New Braunfels Smokehouse and making changes

to the ERP system. The construction of a Standard Operating Procedure (SOP) manual, a task

that was given to this writer, was progressing as expected. While it was still a work in progress,

it would continue to be changed after the completion of this senior project. Additional tests of the

AccountMate and Manufacturing Conductor software packages were planned so that they could

be added to the SOP manual.

5.6 SOP Manual

I was tasked with carrying out the beginning stages of the design and creation of a

standard operating procedure (SOP) for the ERP platform. This manual would help Smokehouse

personnel operate the ERP system once implementation is completed. In April 2021, I was

working on information on the tasks for each area of the Smokehouse and personnel. The initial

draft of the SOP manual can be found in Appendix B.

According to LucidChart (2019) an SOP manual should be written so each process and

area of the warehouse is explained and easily accessible. The SOP would supply direction on

how to create an order, follow the production processes at the Smokehouse, and check inventory

among other necessary items. The SOP should be designed for usability in all areas of the

production process. The prospective needs of the user and smokehouse must be considered. The

manual should be written so users have direction in each of their positions within the company.

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All directions should be written for easy comprehension. Direction should also be given for

specific procedure (LudicChart, 2019) and all necessary information for each step. In order to do

this, I design a format so that each step of a process would be explained individually; screenshots

of the interfaces the user would see are included (Figure 38). At the preliminary stages of the

SOP, information on creating and starting a product order had been collected and would be

added to the existing draft SOP manual. My initial intent was to make the SOP prototype easy to

follow by all users. I wrote the initial sections from the standpoint of someone who was not

familiar with ERP but had gained an understanding of the needs of the prospective users of the

system.

In order to create an SOP, a given process must firstly be fully explained and understood

by the author. An SOP is a document written to give direction; therefore, the author must know

what each process entails to give guidance to the reader. The author must also be concise and

Figure 38 - SOP Page: Scheduling an Order

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emphatetic. When writing to those learning a new program, it is crucial that the wording be kept

to a minimum and only include important phrases and commands to help learn quickly and

effectively.

The author of an SOP must also write with an objective in mind. The objective of this

SOP was to communicate with prospective users of the yet-to-be-deployed system and guarantee

a smooth learning process. Each step and role must also be clearly defined. The audience may be

confused about a new process, and the creation of the manual should be there to ease frustration.

According to LucidChart (2019), an essestial piece of the SOP is the title page. The title

page should include information regarding the company in which the manual pertains to. In

Figure 39, I include an image of the title page for the prototype. This would be the first page a

user would see. On the initial pages, I have also given information on the tasks that each

software is designed to complete (Figure 40) and showcased the easy-to-follow language that

will be used throughout the SOP manual (Figure 41).

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Figure 39 - SOP manual title page

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Figure 40 - SOP page : Manufacturing Conductor

Figure 41 - SOP page: AccountMate

I have explained the function of each software component. I have also described how to

create an order, how to create boxes and bags in the shipping department, and how to follow the

manufacturing of an item. At the time of this report, the three components of the ERP mentioned

above are the only processes of the system that have been fully implemented and would not

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require further modifications. As more aspects of the ERP system come into place, the SOP

would be expanded, edited, and tested for usability to fit such changes and additions.

Maintaining the SOP over time is essential to the usability of the document (Pearce (2018)).

While the manual is not complete, our the purpose was to design and recommend a document

format that could be referenced in the future.

The development of the SOP was conducted in the framework of a DMAIC initiative.

The problem was defined as the need to help prospective users of the ERP system navigate

through the multiple software screens provided by Tamlin software including AccountMate and

Manufacturing Conductor. As for the measurement aspect of the DMAIC initiative, no

measurements were collected, but provisions would be made to test the usability of the document

by asking Smokehouse personnel to test it. The analysis phase would include the examination of

the feedback provided by prospective users and determine how the manual may be improved. A

full-blown control phase would probably not take place until after the ERP is fully implemented;

however, an effort should be made to ensure that any portions of the SOP that are approved and

deployed continue to go through a Kaizen process.

5.7 Conclusion

At the conclusion of this project, the implementation of ERP system had progressed as

planned. It would continue to develop over the next 12 to 24 months. After working with Tamlin

and the Smokehouse management team, I understand how much work goes into the

implementation process and how much reward is expected to come from it. In addition to having

to learn the fundamentals a comprehensive software solution, I appreciate the crucial role

“people skills” play in such a major undertaking. Upon running the software multiple times,

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myself, I concluded that the organization of files, and tracking or ingredients and spending had

already been a fruitful effort. While the ERP system had not been fully implemented, the

Smokehouse’s management team had already seen a difference in the way the Smokehouse had

been operating.

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CHAPTER VI: PROJECT CONCLUSION AND REFLECTIONS

Overall Project Conclusion

This senior design project was concluded successfully. Major accomplishments included

(1) the design and implementation of KPIs, (2) the design of implementation of a new workflow

at the facility, and (3) the completion of the first stages of an all-encompassing ERP effort that

would facilitate a much-needed transition from a manual process to an automated one. The

initiatives had a positive impact at the Smokehouse.

As team members, we learned how to work together towards a specific goal. Each part of

the project had as final objective to tackle the existing problem in the shipping area of the plant

and reach a common success. As engineering students, we learned many things throughout the

process and shared some of our knowledge through our actions. This project was executed

properly and efficiently, with outcomes we are proud of as team members and as interns at New

Braunfels Smokehouse.

As an integral part of our St. Mary’s education, each author was asked to articulate a

personal reflection on how their tenure at St. Mary’s made an impact on their personal beliefs.

The reflections, which include their considerations about personal growth, perspective on other

people, relationship with God, and appreciation for the natural world, are presented next.

Kenia’s Reflection

As many other international students, I had the dream of pursuing my university degree

in the US but knew that this dream required many sacrifices. I knew that my merits during high

school were of great impact upon fulfilling this dream but maintaining a good performance

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during the four years of my degree would be the difficult part of it. Academic aspect aside, the

fact of being alone in a new country was one of the greatest challenges. Fortunately, St. Mary’s

helped me overcome every obstacle.

St. Mary’s provided me with many opportunities to grow not only academically, but also

spiritually. I grew up very attached to the Catholic religion because my parents taught me to do

so, but at St. Mary’s I learned what practicing my religion really was. With classes such as civic

engagement and the program “Meals on Wheels” I understood what “loving thy neighbor as

thyself” really meant. Helping others turned out to have a very different meaning of what I

thought. I realized that it was not really helping my friends, but instead helping people who I did

not really knew and were in much more need. With programs such as Continuing the Heritage, I

could experience what it was to help my community even if it was just by painting the church’s

school, like I did. It felt great to give a back a little bit of everything I was receiving and thinking

of all those people who don’t have much but what we give as a school and community through

this and many other volunteering programs that St. Mary’s offer.

The hardest part of being an international student is being alone in a new place and

having your family thousands of miles apart. My first days at St. Mary’s were hard. I did not feel

the need to attend the events offered on campus until I did. In events such as Oyster Bake or the

ones hosted by University Programming Council, I got to meet new people that today I can call

my friends. These are friends who became family and helped me fill the emptiness inside I felt at

first. I am very grateful of attending a campus in which everyone gets the opportunity to know

each other.

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I am very pleased to have received a high-quality education. The Core Curriculum at St.

Mary’s helped me open my mind to various topics that even though they were not related to my

career; they were of great impact in my life. With the SMC classes I learn about legal justice,

ancient philosophy and history, art techniques, literature and composition techniques, ethics,

faith and many other things that helped me became a more cultured person. St. Mary’s 11:1

student-faculty ratio became of great importance in my education. I feel I got to know my

professors very well and learn what they expected form me in every class. Having such talented

professors is still helping me prepare for the future. By sharing their own experiences, the faculty

showed me what I am expected to be once I finish my student period.

I am very grateful to my university for helping me financially, professionally,

academically and spiritually, and to its faculty for teaching me how to be a good engineer and for

transmitting their knowledge to me. Now, when I think about these my four years at St. Mary’s, I

realize that I would never be where I am at now, if it was not for all the help this fine institution

offers to their students. Today, I realize that all the Marianist values and characteristics grew in

me and will be in me forever. Now, everything I learn through all these years contributed in the

making of this senior design project. The ethics of doing it right, the abilities of expressing

myself in any way, it all summarizes to everything I have done and to everything I will do.

Serena’s Reflection

I had known I wanted to go to school for engineering since I was in seventh grade, but I

was told countless times that it was no profession for a woman. I kept up with my grade and in

high school I graduated at the top of my class. When applying for schools my parents reminded

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me to be mindful of the expenses a 4-year university required and how important it was to have

merit-based scholarships. When all the dust settled, I chose to attend St. Mary’s.

Upon arriving at St. Mary’s, I was skeptical about my education because the degree plan,

I originally wanted was not offered and I had to make some decisions that would impact my

future in a different way. Throughout my time here I took various courses that I initially didn’t

see as beneficial for the career path I wanted. However, in my experience working on this senior

project I saw how well rounded the engineering department has made me.

Not only has my academic side been improved but the spiritual and social aspects of my

life have too. I was born into a Catholic family and as I grew up, I continued down that path. I

knew that I could show God’s love in each aspect of my life and the St. Mary’s community

reaffirmed that for me. I was given opportunities to help the surrounding community and meet

the people that make San Antonio amazing. I learned that it doesn’t take much to lend a helping

hand and even the smallest of actions can change a person’s life. The St. Mary’s core classes that

I took dug deeper into what it meant to be a Marianist and what we can learn about ourselves

from these experiences. I was able to work at the Guadalupe Community Center and put a smile

on people’s faces and I learned just how fortunate I was to have what I have and have the

opportunities that I have.

I was fortunate enough to continue learning about my faith around campus by talking

with the Marianist brothers and going to mass in Assumption Chapel however the greatest

opportunity I had to build on my faith was through Rattler Awakening. Right before I attended

Rattler Awakening, I had experienced the worst few weeks of my life. My father had passed

away, I was struggling to understand my faith, and on top of it all I couldn’t focus on my classes.

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That weekend helped me grow in ways that I didn’t know I could. I was able to open up and talk

about difficult points in my life and surround myself with people who would lift me up when I

would fall. Becoming part of the Rattler Awakening Family was one of the best experiences I

had during my time at St. Mary’s. After attending that retreat, I was able to staff on three retreats

afterward and see the growth in the retreatants and reflect on how I had grown. I got to meet new

people and immerse myself in University Ministry. Seeing these faces around campus helps me

remember how I felt those few weekends and reminds me that God is in everything.

In the Fall of 2019, I was also able to join Greek Life. Sorority Life helped me expand

my social circle and meet people I otherwise would not have. This was another community that I

am a part of that continues to help me grow as a person and chase my dreams. My sorority sisters

have motivated me and continue to believe in me when I don’t believe in myself. I have stepped

out of my comfort zone, met lifelong friends, and supported the community. Sigma Sigma Sigma

Sorority has five values: wisdom, power, faith, hope, and love. These ideals line up with my own

and what I believe St. Mary’s has accomplished in the past four years. I have gained wisdom

through my studies. I had learned to be a leader and use my power to accomplish what needs to

be done. My faith has been strengthened by Rattler Awakening and University Ministry. Each

person has hope for a better tomorrow and we should strive for that every day. And finally love,

love has been the greatest of the five. The St. Mary’s community shows love to each individual

which is a huge part of the reason I have enjoyed my experience here.

Every aspect of the campus and community has helped me grow as a person and

supported me in my endeavors and this senior project group, class, and faculty has done the

same. I am grateful that St. Mary’s has helped me with all that they have and had given me the

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opportunity to follow my dreams. I know that if I had chosen to attend any other university my

experience would not be as enriching or meaningful as the one I received here.

Ting King’s Reflection

My motivation to coming to St. Mary’s came from my experience with the Marianist

education. I went to a Marianist school back in Peru and throughout all those years I could

experience that, besides a high-quality academic education, my school was also focused on

shaping leaders and people of good. Because I experienced first handed the quality and great

education Marianist institutions provided, I decided to continue this amazing experience at St.

Mary’s. During my time here, I have grown in many aspects such as professionally and

personally, but also spiritually.

The opportunities provided by St. Mary’s to help me find my purpose in life were

infinite. Through the St. Mary’s Core classes I was able to learn new things that went far from

academic, but rather aimed to help me grow to the best possible version of me. The Self class

helped me reflect on my purpose of life and what I want to achieve in this. In this I reflected on

what does being a human being and how my relationship with others define who I am. In the

Civic Engagement class, I learned that no matter what career path I follow, there are multiple

ways I can be involved with my community. I also realized that being involved with my

community should not be seen as an obligation, but rather as a way of giving thanks for all the

help and resources my community provides me with. In my ethics class I learned about what is

defined as good, what is defined as bad, and how these definitions vary from person to person.

Furthermore, I came up to realize that my freedom has limits, and these limits are set by me

when my decisions are harming others. The Ethics lab provided me with multiple real-life

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scenarios in which I had to decide what was the right thing to do, but from the perspective as an

engineer.

The academic and ethical education provided by St. Mary’s embodies all the

characteristics of the Marianist education. Formation in faith: through the Marianist Leadership

Program I was able to strengthen religion and my relationship with God. Integral, Quality

education: I have had the blessing of having outstanding professors who constantly strive to help

me grow professionally but also personally. Family spirit: the St. Mary’s community is a diverse

and multicultural community and although the differences of one another are evident, we are

constantly trying to live as a family: respecting and accepting one another. Service, Justice, and

Peace and the Integrity of Creation: Continuing the Heritage and other events encourage me to

stay connected to my community and promote peaceful interactions among each other.

Adaptation and Change: during this past year everyone has had to make drastic changes in our

lives, St. Mary’s offered me resources to make this adaptation smoother while still excelling my

schoolwork.

I am very grateful for the opportunity of completing my engineering bachelor’s degree at

St. Mary’s University. It has been a major blessing in my life getting to know amazing faculty,

staff, and students who have helped me become the person I am today.

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References

Basu, A., & Blanning, R. W. (2000). A formal approach to workflow analysis. Information Systems Research, 11(1),

17-36.

Dupuy, C., Botta-Genoulaz, V., & Guinet, A. (2005). Batch dispersion model to optimize traceability in food

industry. Journal of food engineering, 70(3), 333-339.

Ehie, I. C., & Madsen, M. (2005). Identifying critical issues in enterprise resource planning (ERP) implementation.

Computers in industry, 56(6), 545-557.

Fujita, K. (2018). New Approach to Improving Site Operations of Distribution Centers with IoT Technology.

Fujitsu Scientific & Technical Journal, 54(2), 34-38.

Giannikas, V., Lu, W., Robertson, B., & McFarlane, D. (2017). An interventionist strategy for warehouse order

picking: Evidence from two case studies. International Journal of Production Economics, 189, 63-76.

Harrell, C. R., & Price, R. N. (2003, December). Simulation modeling using promodel technology: simulation

modeling using promodel technology. In Proceedings of the 35th conference on Winter simulation: driving

innovation (pp. 175-181).

Hong, K. K., & Kim, Y. G. (2002). The critical success factors for ERP implementation: an organizational fit

perspective. Information & management, 40(1), 25-40.

Kattepur, A., Mukherjee, A., & Balamuralidhar, P. (2018, September). Verification and timing analysis of Industry

4.0 warehouse automation workflows. In 2018 IEEE 23rd International Conference on Emerging Technologies and

Factory Automation (ETFA) (Vol. 1, pp. 1297-1304). IEEE.

Lee, J. A., Chang, Y. S., Shim, H. J., & Cho, S. J. (2015). A study on the picking process time. Procedia

Manufacturing, 3, 731-738.

Loos, P. (2001). Gozintographs for by-products and cyclic production: an approach for ERP System Application.

AMCIS 2001 Proceedings, 215.

LucidChart (2019) How to Write a Standard Operating Procedure | Lucidchart Blog. 29 Apr. 2019. Web.

Parmenter, D. (2020). Key Performance Indicators: Developing, Implementing, and Using Winning KPIs: Vol.

Forth. Wiley, (xvii), 232.

Pearce, Oliver. (2018) "5 Fundamental Steps to Creating Powerful Standard Operating Procedures." Web.

Richards, G. (2017). Warehouse management: a complete guide to improving efficiency and minimizing costs in

the modern warehouse. Kogan Page Publishers.

Sadrzadehrafiei, S., Chofreh, A. G., Hosseini, N. K., & Sulaiman, R. (2013). The benefits of enterprise resource

planning (ERP) system implementation in dry food packaging industry. Procedia Technology, 11, 220-226.

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Scott, J. E. (2008). Technology acceptance and ERP documentation usability. Communications of the ACM, 51(11),

121-124.

Shiau, J. Y., & Lee, M. C. (2010). A warehouse management system with sequential picking for multi-container

deliveries. Computers & Industrial Engineering, 58(3), 382-392.

Tearwattanarattikal, P., Namphacharoen, S., & Chamrasporn, C. (2008). Using ProModel as a simulation tools to

assist plant layout design and planning: Case study plastic packaging factory. Songklanakarin Journal of Science &

Technology, 30(1).

Themistocleous, M., Roseman, M., Loos, P., Buonanno, G., Faverio, P., Pigni, F., ... & Tagliavini, M. (2005).

Factors affecting ERP system adoption. Journal of Enterprise Information Management.

Zhang, M., Batta, R., & Nagi, R. (2009). Modeling of workflow congestion and optimization of flow routing in a

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Appendices

Appendix A – Yellow paper’s codes

CODE REASON SPEC

1 DELIVERY NOT DEL ON TIME

2 DELIVERY NOT DEL AT ALL

3 DELIVERY NOT DEL - RTND

4 DEL DMGD - SPOILED -

5A POOR QUALITY MOLD

5B POOR QUALITY SALTY / PEPPERY

5C POOR QUALITY FAT

5D POOR QUALITY FLAVOR

5E POOR QUALITY THOUGH / GRISTLE

5F POOR QUALITY DRY / STALE

5G POOR QUALITY SIZE

5H POOR QUALITY MISC

6A PACKING LOOSE CRYOVAC

6B PACKING UNSEALED

6C PACKING HOLES

6D PACKING LOOSE LIDS

7A SHIPPING BROKEN JAR

7B SHIPPING ITEM LEFT OUT

7C SHIPPING WRONG ITEM

7D SHIPPING WRONG SHIP METHOD

7E SHIPPING NOT MANIFESTED

7F SHIPPING MISC

8A OFFICE WRONG SHIP DATE

8B OFFICE WRONG ADDRESS

8C OFFICE WRONG ITEM

8D OFFICE DUPLICATE ORDER

8E OFFICE DISCOUNT NOT GIVEN

8F OFFICE ITEM LEFT OUT

8G OFFICE WRONG SHIP METHOD

8H OFFICE MERGED CUSTOMER FILES

8I OFFICE MISC

9A COMPUTER ISSUES GIFT CERTIFICATES

9B COMPUTER ISSUES DUPLICATES

9C COMPUTER ISSUES MISC

10A ABILITY COMMERCE WRONG SHIP DATE

10B ABILITY COMMERCE WRONG ADDRESS

10C ABILITY COMMERCE WRONG ITEM

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10D ABILITY COMMERCE DUPLICATE ORDER

10E ABILITY COMMERCE MIS-INFORMATION

11A INTERNET WRONG SHIP DATE

11B INTERNET WRONG ADDRESS

11C INTERNET WRONG ITEM

11D INTERNET DUPLICATE ORDER

11E INTERNET DISCOUNT NOT GIVEN

11F INTERNET MISC

12A CUSTOMER WRONG SHIP DATE

12B CUSTOMER WRONG ADDRESS

12C CUSTOMER WRONG ITEM

12D CUSTOMER DUPLICATE ORDER

12E CUSTOMER WRONG SOURCE CODE

12F CUSTOMER WRONG STORAGE

12G CUSTOMER WRONG COOKING

12H CUSTOMER SPOILED

12I CUSTOMER JUST DIDN’T LIKE

12J CUSTOMER LAST RESORT

13A ITEMS PURCHASED FROM RESTAURANT

13B ITEMS PURCHASED FROM WURSTFEST

13C ITEMS PURCHASED FROM HOUSTON

14A CUSTOMER CANCELLED NEVER LEFT US/AWASH

14B CUSTOMER CANCELLED SHIPPED

15 COSTUMER LOYALTY -

16 DID NOT GET OUT OF HERE ON TIME -

17 UNHAPPY WITH SUBSTITUTION -

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Appendix B – Enterprise Resource Planning (ERP) system’s Standard Operating Procedures

(SOP) manual

This document is designed to assist the manufacturing team at the New Braunfels

Smokehouse in the use and comprehension of the Enterprise Resource Planning (ERP) system

following implementation.

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AccountMate

AccountMate is a software system that can be used for the following at the New

Braunfels Smokehouse:

• Finances and Accounting

• Costing

• Vendor, Customer, and Item numbers

• Inventory Maintenance

• To create items, purchase orders, and sales orders

• Inventory per Pallett, Case and Unit

• Upper Management Reports or MRP Reports

To use AccountMate, open the “AccountMate” Application.

Upon opening the application, you will see the following screen.

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[Note: Additional descriptions for each of the AccountMate sections will be

included here]

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Manufacturing Conductor

Manufacturing Conductor can be used for following the process of each product from

Raw to RTE to Shipping

Open the “Manufacturing Conductor Application”.

Upon opening the application you will see the following screen.

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Receiving Worksheet

In Receiving Worksheet, the user can:

• Obtain item numbers from AccountMate

• Enter all information about Raw goods from shipments at the Receiving Dock

Recipe Maintenance

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In Recipe Maintenance, the user can:

• Pull information about ingredients form AccountMate

• Obtain a description of all ingredients inputted into a given recipe

Work Center Maintenance

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In Work Center Maintenance, the user can:

• Input a description of each process needed for a given product

• Follow the flow or materials

• Obtain specific preparation instructions for a given product

Packaging Line

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In Packaging Line, the user can:

• Finish Goods to go to Ready to Eat (RTE)

• Create Cases and Palettes for inventory

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Scheduling an Order

Open Manufacturing Conductor

Open MC Scheduler

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(The Production Manager will use MC Scheduler to schedule what needs to be

produced for any given order or day.)

Click Auto Schedule

This will schedule all associated processes

A screen will notify you if there is more requested than in inventory

Change “Center”

The drop down menu will show each schedule seen in Recipe Maintenance

If a step is not needed for that product the screen will be blank

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Check each step for missing information

Close

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Creating Bags and Boxes

(The Shipping department will be responsible for all Boags and Boxes being created)

Open Manufacturing Conductor

Open Manufacturing Conductor Shop Floor

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Select Box from the drop down menu

Click Create

Click Complete

Click Logout Items

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Manufacturing an Order

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Production Requirement Pick Ticket (Manufacturing Conductor) or Material Resource

Planning Report (AccountMate)

AccountMate

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Create Purchase Order

Buy Item

Print Purchase Order

Manufacturing Conductor

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Receiving Planner

All purchase orders will be shown

Receive

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Quantity and Label

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Add Line for each item

Receive

Should disappear from initial list

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RECEIVING WORKSHEET PO #

Manufacturing Conductor Shop Floor

Input User Code

Work Station (Select which apply)

Thaw

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Import Schedule

Select

Start Production

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Print Label – Barcode Traveler

BOM

Scan Receiving Label

Complete

Log Out Items

Brine

Import Schedule

Import

BOM

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Scan Receiving Label (By Item)

Inject

Batch #

Scan Barcode Traveler

BOM

Scan all Barcode Traveler/ Receiving Labels

Complete

Log Out Items

Oven

Scan Barcode Traveler

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IPI – Depends on each individual item on whether inspection is needed

Complete

Logout Items

Print

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Chiller

Scan Barcode Traveler

Logout Item

Packaging Line

New Batch

Scan Over Barcode

Select Work Station

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Item # (Intermediate WIP)

Schedule # of product

Start

Yes

Single BOM

Scan (Oven), Bags, Boxes

Finish

Print Label

Finish

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Print Case Label

Print Pallet Label