facilities planning and production management

FACILITIES PLANNING AND

PRODUCTION MANAGEMENT

Final Report: TOWER OF HANOI

Linnaeus University

School of Engineering

1SE007 – Facilities Planning and Production Management

Authors: Examiners:

Boris Batljan Anders Ingwald

Fatih Topaloglu Anna Glarner

Nikolaos Georgadakis Farvid Mojtaba

Serkan Alan

Acknowledgement

We would like to thank our teacher/tutors Anders Ingwald, Anna Glarner and Farvid Mojtaba

who have helped and supported us when make this report. Also we want to thank for the

lectures and necessary information.

Table of Contents

1. Introduction .................................................................................................................. 4

1.1 Task .................................................................................................................................... 4

1.2 Account for Assumptions..................................................................................................... 4

2. Theory .......................................................................................................................... 5

3. Empirical Findings ......................................................................................................... 8

3.1 Task 1: Business Strategy........................................................................................................... 8

3.2 Task 2: Routing/List of Operations ............................................................................................ 9

3.3 Task 3: Facility Layout .............................................................................................................. 13

3.4 Task 4: Material Handling ........................................................................................................ 15

3.5 Task 5: Safety Stock and Economic Order Quantity ................................................................ 16

3.6 Task 6: Demand and Forecast ................................................................................................. 17

4. Results and conclusions ............................................................................................... 19

5. References .................................................................................................................. 19

Appendix ........................................................................................................................ 21

1. Introduction Here are we going to present our task and assumptions.

1.1 Task

We have six different tasks for this report. Tasks are showed at below with short descriptions.

1. Business Strategy: We will define business goals, who is our customer and where

should the facility be layout.

2. Routing/List of Operations : Which way we choose for production, our production

numbers and calculations.

3. Facility Layout : How our facility layout, how we choose that alternative and desicion

process will be presented.

4. Material Handling : How our material transfer between stations and reasons will be

presented.

5. Safety Stock and Economic Order Quantity : With holding and setup we calculate

optimal production.

6. Demand and Forecast : Acording to previous years data, we will calculate demand and

forecast for 2011.

1.2 Account for Assumptions

Assume Operation Times to Produce a Game

Figure 1: Time Calculation for Parts

We assume production times. For brass, we have four different diameters. The table above

shows average brass produces time.

Working Time

We have 7.5 hours working time and half an hour lunch break per a day. We assume that we

will work 20 days a month. When we calculate work days per month we consider weekends,

religion holidays, national holidays. It means we have 27,000 seconds per a day; 540,000

seconds per a month working time.

In task 5 annual demand is used as 72000 but normally it is 71811. It helps to

calculation.

Name of Part Time for Produce

One

We Need for One

Game

Time to Produce One Game

Brass 10 seconds 4 40

Peg 8 seconds 3 24

Base 70 seconds 1 70

2. Theory Here are we going to present the theory we used.

2.1 Relationship Chart

According to Chien (2004) is Muther‟s view and method regarding systematic layout

planning (SLP), not only a proven tool in providing layout design guidelines, the method is

used over the whole world among enterprises and in the academic world.

Figure 2. Example of an Activity Relationship Chart

When starting with the SLP procedure and improving the Activity Relationship Chart does the

process start with making the relationship between each activity on the chart more

comparable. The activity relationship is used to decide the relationship score and diagram

between each activity and is an important indicator in decision-making. Figure 2 shows an

example of an activity relationship chart, the chart usually looks like the one in figure 2.

2.2 Material Requirements Planning (MRPI)

According to Hill (2005) is MRP a system that determines the final services and products

(depends on what kind of products and the amount) that a company will produce in the future;

the system does also specify the necessary inputs to meet that demand. MRP is also used to

manage the capacity needs in a company and also the material needs. For example is the

demand for engines, tires and brakes linked to the demand of vehicles. To determine the

number of engines, tires and brakes we need to determine the number for vehicles. When we

have the number of vehicles can we calculate the requirements for all dependent items.

2.3 Master Production Schedule (MPS)

Master production schedule focuses on to produce certain quantities of services or products in

a particular time periods. To do this, does it take statements of demand (forecast sales and

known orders) and test those against statements of capacity and resources for the same

period(s). The result would be an anticipated schedule of finished services and products. This

schedule has a key role in the control system that leads to an agreement between marketing

and operations on what a company shall produce. The requirements are inventory records, the

quantity and timing of current operations schedules, outstanding purchase orders, up-to-date

bills of materials that reflect changes and clear information about the customers requirements

(the existing customers), current orders and sales forecasts. The master production schedule is

based mostly on forecast in the later periods of the planning horizon. (Hill, 2005)

2.4 Material Handling System Equation

According to Tompkins (2010) are they‟re some material handling system designs, and the

material handling system equation is one of them. The handling system is used to identify

opportunities for improvement. It gives framework to identify solutions to material handling

problems. What defines what type of material has been moved, where and when identifies

the time and place requirements, who and how tells the material handling methods. Whit

these questions shall the system lead to a recommended system. The material handling system

equation is given by:

Materials + Moves + Methods = Recommended System

Figure 3. Material Handling System Equation

2.5 Simple/Weighted Moving Average and Exponential Smoothing

Inman (2006) explains that a simple moving average takes a predetermined number of

periods, sums their actual demand, then the sums is divided by the number of periods to reach

a forecast. For each period, the latest period gets added and the oldest period of data drops off.

A good example could be if we use actual demand with example numbers; 45 in January, 60

in February and 72 in March would give the result of:

45 + 60 + 72 = 177/3 = 59

If there would be interesting to get the forecast for May, we would drop January‟s demand

from the equation and add the demand from April.

A weighted moving average takes the predetermined weight to each month of past data, then

shall the past data from each period be summed, and at least be divided by the total of the

weights. The results are later on summed to achieve a weighted forecast. Generally can it be

said that when the older the data is, the smaller is the weight, and the more recent the data is,

the higher will the weight be. If we say that we use demand examples with a weighted

average using weights of .4, .3, .2 and .1, would the forecast fore June be:

60(.1) + 72(.2) + 58(.3) + 40(.4) = 53.8

Exponential smoothing is about taking the precious period‟s forecast and adjusts it by a

predetermined smoothing constant, ά (called alpha) multiplied by the difference in the

previous forecast and the demand that actually occurred during the previously forecasted

period (called forecast error). The formula of exponential smoothing is:

New forecast = previous forecast + alpha (actual demand – previous forecast)

F = F + ά(A-F)

Exponential smoothing requires an amount of past data and a beginning or initial forecast. It

does also require the forecaster to begin the forecast in a past period and to continuously work

forward to the period for which a current forecast is needed. Initial forecast can be an actual

forecast from a previous period, actual demand from a previous period, or it can also be

estimated by averaging all or part of the past data. The accuracy of the initial forecast will not

be critical if someone is using large amounts of data, just because exponential smoothing is

self-correcting. If there are enough periods of old data, will the exponential smoothing

eventually make enough correction to compensate for a reasonably inaccurate initial forecast.

Using for example an initial forecast of 50, and an alpha of .7 for February will the forecast

for February be:

New forecast (February) = 50 + .7(45-50) = 41.5

New forecast (March) = 41.5 + .7(60-41.5) = 54.45

This process will continue until the forecaster reaches the desired period. (Inman, 2006)

2.6 Economic Order Quantity (EOQ)

According to Hill (2005) is there one fundamental decision in the inventory management,

which can be very hard to answer. The question is how much a company should order, in the

context of what quantity will result in the lowest total cost. The economic order quantity

(EOQ) and the economic batch quantity (EBQ)/economic lot size (ELS) models are linked to

the question: “how much shall for example a company order to minimize the total cost of

holding inventory?” The formulas for each are given below:

- EOQ = 2zCs/cC

- EBQ/ELS = 2zCs/cC * p/p-d

Variables:

z = total annual usage

Cs = cost of placing an order

c = unit cost of the item

C = carrying cost rate per year

p = production provisioning rate (units) per day

d = demand rate (units) per day

It is important to note that these models make the following assumptions:

- The rate of demand is constant

- Costs remain fixed

- Operations capacity and inventory holdings are unlimited

3. Empirical Findings

3.1 Task 1: Business Strategy

Business Goals

Our company is customer oriented. We try to be always in time for our deliveries and never

let our customers down .The quality of our products is high but without exaggerations to the

price or the appearance. Also, as a profit organization, we are looking forward to increase our

earnings year by year but not in the expense of our clients. On the contrary it is our constant

effort to lower the production cost by improving the facility‟s quality rate. That is the reason

that the production line is equipped with new and up to date machinery.

Customers

Since we are a factory, it is preferred for us to sell our products at wholesale. We ship the

products to retailers all over the world, toy stores, hobby centers and also supermarkets on

selected countries trying to make our product available for a big number of people. The target

group varies from country to country but in general are people interested in puzzles and

mathematical games.

Location of the customer

Although our industry is capable of delivering all over the world, we focus on countries that

have normal or high living standards. Also, one of the regions that the company pays more

attention is west Europe and France in particular. That is happening because of the popularity

the game has there since its inventor of was of French nationality

Facility Location

After consideration, it was decided that best location for a company of this kind is best to be

in central Europe. France and Germany represent the prerequisites we have set. Stability in

the economic life in addition to the low tax rates for new or young factories were the main

concerns. The proximity to the target countries also affected our decision. Moreover, rent

costs, experienced labor in factories and easier delivering throughout Europe was also

considered.

Specifics about the factory‟s location

- A non-urban area low rents, plenty of space

- Close to highway raw material/finished products deliveries

- Preferably close to a port shipping with containers

3.2 Task 2: Routing/List of Operations

Machine Choosing:

Name Cost per Hour Cost per Month

Alternative 1 103 Vertical

Machining Centre

150 56,320

104 Assembly Table 20

106 CNC Auto Lathe

with Bar Feed

140

2x Operators 21x2

Alternative 2 101 Band Saw

Machine

20 56,480

102 CNC Auto Lathe 100

103 Vertical

Machining Centre

150


3x Operators 21x3

Alternative 3 103 Vertical

Machining Centre

150 82,080


2x 106 CNC Auto

Lathe with Bar Feed

140x2

3x Operators 21x3

Figure 4: Machine Alternatives

First we compare alternative 1 and alternative 2. Both alternatives satisfy our demand and

operation costs are almost same. We choose alternative 1 because alternative 1 is more

automatic and less human dependent. Human work can be wrong or more possibility to make

fault. On the other hand automatic systems are more trustable and less possibility to make

some faults.

Then we think we can use 3 machines for 3 parts. On that way our production will be so fast

because we produce all parts all the time and do not lose time for setup machines. But when

we compare our production, numbers between produced parts are too far from each other and

we do not need that production. Also our production is too much that our demand. So we do

not have to that much operation cost because we do not need it.

Calculate Working Time

Spend Time Description Time per Month (Seconds)

Brass Setup time 147,600

Mini Setup Time 5,400

Peg Setup Time 19,200

Base No Setup Time 0

Figure 5: Setup Time for Machines

Name of Part Days a Month Seconds a Month

Brass 12 304,200

Peg 8 196,800

Base 20 540,000

Figure 6: Clear Work Time

In production planning, we have 106 CNC Auto Lathe with Bar Feeder which we are

planning to produce brass and peg. The machine produces brass 2 days, after one day peg,

then 2 days Brass and one day peg. And we have 103 Vertical Machining Centre which we

produce just base part.

Calculate Production

Name of

Part

Average

Deviation

for Planned

Production

Time

Total Planned

Production

Time per

Month

(Seconds)

Clear

Work

Time per

Month

(Seconds)

Needed

Time to

Produce for

one Game

(Seconds)

Real

Capacit

y per

Month

(Items)

Avera

ge

Scrap

Rate

Approved

Product

Capacity

per Month

(Items)

Base 1.18 540,000 457,627 70 6,536 5.58% 6,157

Peg 0.94 196,800 209,361 24 8,723 1.34% 7,715

Brass ø40 1.05 90,000 85,714 12 7,142 0.74% 7,089

Brass ø30 1.02 80,200 78,627 11 7,148 1.02% 7,075

Brass ø25 1.02 67,000 65,686 9 7,298 0.78% 7,241

Brass ø20 1.14 67,000 58,772 8 7,347 0.66% 7,299

Figure 7: Number of Parts We Produce

When we calculate “Average Deviation for Planned Production Time” we tool average of

previous five years, then we divided “Average Deviation for Planned Production Time” with

“Total Planned Production Time per Month” and got “Clear Work time per Month”. For

calculate “Real Capacity per Month” we divided “Clear Work Time per Month” with

“Needed Time to Produce for one Game”. We took average of previous five years scrap rate

to calculate “Average Scrap Rate”. At the end, to find “Approved Product Capacity per

Month” we subtract “Average Scrap Rate” from “Real Capacity per Month”.

Calculate Cost

Name Cost per Hour Working Hours per Month

103 Vertical Machining Centre 150 160

106 CNC Auto Lathe with Bar Feeder 140 160

104 Assembly Table 20 160

2 Operators 21x2 160

Total Operating Cost: 56,320

Figure 8: Operating Cost

Name of Part Real Capacity

per Month

Length

(Meters)

Price per Meter

(Pound)

Cost

Base 6,536 0.102 6 4,000

Peg 26,169 0.05 1.8 2,355

Brass ø40 7,142 0.005 170 6,071

Brass ø30 7,148 0.005 140 5,004

Brass ø25 7,298 0.005 78 2,846

Brass ø20 7,347 0.005 50 1,837

Total Cost for Row Material 22,113

Figure 9: Raw Material Cost

Calculate How Many Games We Can Sell

According to previous years‟ quality rate, we calculate weighted moving average for quality.

Our weighted moving average for quality is %98.77. Our lowest production is base. So when

we calculate how many games we will produce per a month, we took base production number

as game production number which is 6,157. Our game production per year is

12x6,157=73,884. For find how many games we can produce to sell per year, we multiply

how many games produced with weighted moving average for quality which is

73,884x0.9877=72,975

Our capacity for produce games to sell is 72,975 games per year

List of Operation for Peg

Part name:

Peg

Prep. by:

Group 62

Part no:

AI1003

Date:

05-03-2013

Amount:

3,271 / Day

Op # Description Work site Setup time

(min)

Op. time

(sec)

Cost (£)

0 Feeding 106 - 3 0.12

1 Screw Cutting 106 30 min / 3 35.12

2 Cutting 106 1 day 2 35.08

List of Operation for Base

Part name:

Base

Prep. by:

Group 62

Part no:

AI1002

Date:

05-03-2013

Amount:

6,536 / Month


(min)

Op. Time

(sec)

Cost (£)

0 Put the Parts 103 - 7 0.29

1 Site Cutting 103 - 10 0.42

2 Face Cutting 103 - 20 0.84

3 Frame Cutting 103 - 10 0.42

4 Drill 103 - 8 0.33

5 Screw Cutting 103 - 8 0.33

6 Take the parts 103 - 7 0.29

List of Operation for Brass

Part name:

Brass Pieces

Prep. by:

Group 62

Part no:

AI1004-1007

Date:

05-03-2013

Amount:

4,822 / 2 Days


(min)

Op. Time

(sec)

Cost (£)

0 Feeding 106 - 2 0.08

1 Drilling 106 30 min / 2 21.08

2 Cutting 20 106 2 days 4 21.16

3 Cutting 25 106 5 5 21.19

4 Cutting 30 106 5 7 21.27

5 Cutting 40 106 5 8 21.31

3.3 Task 3: Facility Layout

While designing the facility layout we tried to be as simple as possible. We considered about

different difficulties that may present on the production and tried to prevent them. Since the

machines 103 and 106 are the only ones to use raw materials (the first levels of our bill of

material) we placed them next to the storage of raw materials. Also, knowing that the brass

bars are difficult to handle (even 6 meters long) we placed the 106 in such way that the

feeding is easy and simple.

The storage of finished products was designed as a simple square room next to the last

production station. Shelves are being used and the capacity is big enough to store a lot more

products than our safety stock.

The handling of the products is easy and carts are used to move objects around the facility.

But for safety reasons, the machinery on our blueprints is designed bigger than the reality

(example: 103- in reality 5mX4m, on blueprints 6mx5m) . This idea was approved among

other reasons because movement around the factory must be easy for the workers and contact

with dangerous machine should be avoided. The area covered was increased by nearly 25%

(160m2198m

2) but the extra cost for renting a bigger place was considered an investment

on safety.

Production

The production is close to straight-line production prototype but there are some differences.

Also there is no storage between the stations and the handing is easy because of the resistance

of the product (no special handling needed ex. Temperature or fragility )

Relationship Chart

Figure 10. Relationship Chart

As mentioned before, it is of big importance that 103 and 106 are close to the raw material

storage and also near to the assembly station that is the next step of the production. In

addition, the finished product storage must be close to the assembly area to minimize the

transporting time.

1.Raw Material Storage

2.Lathe 106

3.Vertical 103

4.Assembly 104

5.Finished Product Storage

Proximity needed

Proximity is

unimportant

Relationship diagram

Figure 11. Relationship Diagram

Space Relationship Diagram

Figure 12. Space Relationship Diagram

Layouts

Figure 12. Layouts

The second layout is both more space efficient and production assisting.

Raw Material R.M Storage

106

103

Assembly

1 2

3

4

Storage

5

4mX8m 1. 5mX9m 2.

3X3

4.

5mX6m 3. 5mX5m 5.

3.4 Task 4: Material Handling

When my group and me thinking about material handling, we took some decisions. Everyone

can use it easily because we do not have someone who responsible for material handling,

operators do that. It is not necessary to be sensitive because we will not carry fragile parts.

And of course we have to choose cheapest alternative.

According these requirements, we decide to use push/pull hand control wheeled steel cars. We

planned that in every station has cars. We locate two cars for every station. With that, operator

take row material from car and when it is finished put part to another car. And also we have

one extra car. When operator moves the parts to next station, he/she replace car‟s location

with free car. With that production will not be stop.

Also we want to show our material handling system with “Material Handling Systems

Equation”

Why? We have to transfer parts between stations.

What? Row material, machined part and finished product

Where? Between stations and storages

When? When part‟s operation finish in that station

How? With push/pull hand control wheeled steel cars

Who? Operators

Which? Hand control wheeled steel cars by operators

Figure 13. Material Handling

3.5 Task 5: Safety Stock and Economic Order Quantity

EOQ is mainly describe the relationship between ordering cost, holding cost and the order

quantity. Thanks to EOQ, total cost can be minimized by the optimum batch size.

Formulations which are used in task 5 are presented below.

Formulations

The general Q* formulation is include A= ordering cost, h= holding cost, D= annual demand

𝐻𝑜𝑙𝑑𝑖𝑛𝑔 𝐶𝑜𝑠𝑡 = (𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛 𝐶𝑜𝑠𝑡𝑠 + 𝑅𝑎𝑤 𝑚𝑎𝑡𝑒𝑟𝑖𝑎𝑙 𝐶𝑜𝑠𝑡𝑠)/2

𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛 𝐶𝑜𝑠𝑡 = 𝑀𝑎𝑐𝑕𝑖𝑛𝑒 𝐶𝑜𝑠𝑡 + 𝑂𝑝𝑒𝑟𝑎𝑡𝑜𝑟 𝑐𝑜𝑠𝑡

𝑂𝑟𝑑𝑒𝑟𝑖𝑛𝑔 𝐶𝑜𝑠𝑡 = 𝑆𝑒𝑡𝑢𝑝 𝑡𝑖𝑚𝑒 ∗ 𝑐𝑜𝑠𝑡 𝑜𝑓 𝑚𝑎𝑐𝑕𝑖𝑛𝑒(𝑕𝑜𝑢𝑟)

base holding cost operation

cost

raw material

cost

setup

cost

EOQ

1,89 3,16 0,612 0 0

brass 20 holding cost operation

cost

raw material

cost

setup

cost

EOQ

1,44 0,38 2,5 11,67 1080


cost

raw material

cost

setup

cost

EOQ

2,16 0,42 3,9 11,67 882


cost

raw material

cost

setup

cost

EOQ

3,75 0,50 7 11,67 669


cost

raw material

cost

setup

cost

EOQ

4,52 0,54 8,5 11,67 610

pegs holding cost operation

cost

raw material

cost

setup

cost

EOQ

0,51 0,38 0,63 11,67 1823

Figure 14: EOQ Calculation

Calculation example of Brass 20

Operation Cost= (8/60)*(140/60)+(21/60)*0,2=0,38

8 second is operation time over 60 to find minute. 140 is cost of machine per hour. 21 is

operator cost per hour and multiple with 0,2 which is operator requirement.

Raw material Cost= (5/100)*50=2,5

5mm shows thickness of brass and 50 is raw material cost for per meter.

Holding cost= (operation cost + raw material cost)/2 =1,44

Ordering cost= 5*(140/60)=11,67

5 shows setup time and 140 is operation time of machine.

𝐸𝑂𝑄 = 2∗𝐴∗𝐷

𝑕 , 𝐸𝑂𝑄 =

2∗11,67∗72000

1,44= 1080

Safety Stock

In this project safety stock added to amount of production. Safety stock is prevent to company

shortage stock by maintain the product. Stock out problem can affect the company more than

keep stock. So safety stock which another name is buffer stock should be used to meet

customer needs. Also sometimes forecasting doesn‟t find certain number of sales. In this

situation safety stock help to meet customer needs too. In this project safety stock is choose

as a 5% of 6000. Company have 300 units of finished product each month. This number can

meet the fluctuation of forecast or demand.

3.6 Task 6: Demand and Forecast

Company‟s last two years sales data presented in table 3. Thanks to these data next year

forecast can be done. Simple moving average, weighted moving average, regression and

exponential smoothing methods used for the 2011„s forecast. The 2009 and 2010 sales data

represented below.

1 2 3 4 5 6 7 8 9 10 11 12

2009 5805 6061 5888 5944 5845 5822 5992 6079 5892 6141 5873 5892

2010 5810 5882 5771 6184 6167 6075 6046 6136 5967 6075 6024 5954

SMA is mainly unweighted mean of previous “n” data. In this part of report during the

forecasting n is equal to 5. In table 9 data looks varying slowly so n used like 5. If between

two months these sales amount were changing much, varying would be more and in this case

n had to be smaller number. There is a calculation example of SMA for January of 2011.

𝑺𝑴𝑨 =𝟔𝟏𝟑𝟔+𝟓𝟗𝟔𝟕+𝟔𝟎𝟕𝟓+𝟔𝟎𝟐𝟒+𝟓𝟗𝟓𝟒

𝟓= 𝟔𝟎𝟑𝟏

Weighted moving average method is another method which is used for forecasting. This

method is also mean of previous “n” data but the difference is that previous first data

weighted more than others. In this chapter this weight like 0,4 for first previous month sales

data, 0,3 for second , 0,2 for third and 0,1 for fourth data. It means that first previous month

sales data affect the forecast more than others because of the weight coefficient. There is a

calculation example for January of 2011.

𝑾𝑴𝑨 = 𝟎, 𝟒 ∗ 𝟓𝟗𝟓𝟒 + 𝟎, 𝟑 ∗ 𝟔𝟎𝟐𝟒 + 𝟎, 𝟐 ∗ 𝟔𝟎𝟕𝟓 + 𝟎, 𝟏 ∗ 𝟓𝟗𝟔𝟕 = 𝟔𝟎𝟎𝟏

Regression is method to find a relationship among the data to forecast. It is nonlinear

regression and based on dependent parameter and independent variables. For the regression

method Minitab 14 which is statistical calculation software is used. The model show that

basic parameter is 5910 and each month increase the forecast by the 9,49* ( month)

𝑭 = 𝟓𝟗𝟏𝟎 + 𝟗, 𝟒𝟗 ∗ 𝒎𝒐𝒏𝒕𝒉 So forecast for January of 2011 is like:

𝑭 = 𝟓𝟗𝟏𝟎 + 𝟗, 𝟒𝟗 ∗ 𝟏 = 5919

Exponential smoothing method is last method for forecasting. This method mainly weighted

by a number (α) between 0 and 1 to difference of past real and forecasting data. In this part α

is equal to 0,3. Deciding α is mainly most important part of this method. When α is so big it

means that previous data and its forecast so important for next month forecast. 0,3 is chosen

and it is not big number to affect next forecast so much. There is a calculation for January

2011.6041 is showed that 2010/12 sales forecast and 5954 is same month real data.

F2011/01= 6041+0,3*(5954-6041)= 6015

During forecasting all four method was used and then average of these methods results is the

forecast for 2011. Average of these four method is more useful than per method because when

average of them used it is decrease the error of forecast. There is average of January 2011

forecast table.

Figure 15: Forecast for 2011

MPS

Week 1 2 3 4 5 6 7 8 9 10 11 12

Forecast 5992 5984 5982 5978 5977 5979 5980 5982 5984 5987 5991 5996

Available 89 186 285 300 300 300 300 300 300 300 300 300

MPS 6081 6081 6081 5993 5977 5979 5980 5982 5984 6987 5991 5996

On Hand 0

Figure 16: MPS

First three months machines work full capacity to meet customer needs and make a safety

stock. Safety stock is chosen as 5% of 6000 which is general number for forecast. When reach

the safety stock which is fourth month in this case, manufacturing is going on just as amount

of forecast.

2011

SMA WMA Regression Exponential Average

January 6031 6001 5919 6015 5992

February 6002 5995 5929 6008 5984

March 6006 5984 5938 6001 5982

April 5987 5982 5948 5995 5978

May 5978 5982 5957 5990 5977

June 5982 5979 5967 5986 5979

July 5980 5978 5976 5984 5980

August 5979 5979 5986 5983 5982

September 5979 5980 5995 5982 5984

October 5980 5982 6005 5983 5987

November 5982 5984 6014 5984 5991

December 5985 5988 6024 5986 5996

MRP

As you can see there is kind of bill of material of Tower Hanoi. In this figure req show that

abbreviation of requirement. January and Fabruary of 2011 has same production amount. So

these two months has same MRP.

4. Results and conclusions

In this paper we consider about almost all manufacturing phase. From facility layout to

scheduling all parts activities calculated or discussed. Good planning enables to companies to

use minimum resources while getting the maximum benefit.

The aim of this paper is to show how planned manufacturing , facility layout, decided

business strategy, selected material handling also how it can be planned better and how it can

be supported by the relevant other theories. This is done by solving several tasks related to

facilities planning and production management. In the first task, the business strategy is

explained in detail. The selection of the machines is argued in the second task. There are three

alternative to produce tower of Hanoi. Alternative one which is consist of vertical machining

centre, assembly table, CNC Auto lathe with bar feed is chosen because of the costs. Also in

second task all the manufacturing operation time presented. In the third task, two facility

layout are suggested for the machines which are selected in the second task. Alternative 2 is

selected because it has less space and better material handling function for CNC Auto lathe

machine. In the fourth task, material handling system equation are identified and answered

seven question and decided to material handling system. Lastly, in the fifth and sixth task

calculated EOQ and decided safety stock with one year forecast.

Tower of hanoi

req=6081

Base Plate

req=6081

set of pieces

req=6081

Brass 20

req=6081

Brass 25

req=6081

Brass 30

req=6081

Brass 40

req=6081

Pegs

req=18243

Figure 17: MRP

5. References

Inman, R. Anthony. "Forecasting." Encyclopedia of Management. Ed. Marilyn M. Helms. 5th

ed. Detroit: Gale, 2006. 307-311. Gale Virtual Reference Library. Web. 5 Mar. 2013.

Hill, Terry. 2005. Operations management. 2nd ed. Basingstoke: Palgrave Macmillan

Tompkins, J.A., White, J.A. and Bozer, Y.A., 2010. Facilities Planning. 4th

ed. Hoboken:

John Wiley & Sons, Inc.

Chien, T.K., 2004. An empirical study of facility layout using a modified SLP procedure.

Journal of Manufacturing Technology Management, [e-journal] 15(6). Available through: The

Emerald Research Register <www.emeraldinsight.com/1741-038X.htm> [Accessed 5 March

2013]

http://www.emeraldinsight.com/1741-038X.htm

Appendix

The game, Tower of Hanoi, consists of 1 ground plate made of aluminum, 3 play pegs made of

aluminum and 4 play bricks made of brass.

Figure 2 Drawings of Tower of Hanoi

Figure 3 Bill of Material

Figure 4 Product structure

Level 2

Level 1 Tower of Hanoi

Pegs Set of Pieces Base Plate

Piece 1 Piece 4 Piece 3 Piece 2

3

1

1

1

1

1 1

Level 3

Brass, SS-ISO 5170, Ø 40 x 6




Aluminium, EN6082-T6, 50 x 102 x 8

Aluminium, EN6082-T6,

Ø 8 x 35

Table 1 Cost of material

Material Standard length Cost

Aluminum, EN6082-T6, 50x8 3 m 6 £/m

Aluminum, EN6082-T6, Ø 8 2 m 1,8 £/m

Brass SS-ISO 5170, Ø 40 4 m 170 £/m

Brass SS-ISO 5170, Ø 30 4 m 140 £/m

Brass SS-ISO 5170, Ø 25 6 m 78 £/m

Brass SS-ISO 5170, Ø 20 6 m 50 £/m

If bought cut in special length the lead-time is 2 weeks.

Table 2 Production and quality rate of AI1001

Year Production Quality rate 2006 80 000 98,9%

2007 79 000 98,1%

2008 77 000 99,1%

2009 73 000 99%

2010 65 000 98,8%

Table 3. During 2009 and 2010 WÄDUR had the following orders:

Month

Orders

2009 2010

Jan 5805 5810

Feb 6061 5882

March 5888 5771

April 5944 6184

May 5845 6167

June 5822 6075

July 5992 6046

Aug 6079 6136

Sept 5892 5967

Oct 6141 6075

Nov 5873 6024

Dec 5892 5954

Table 4. Average deviation from planned production time.

Year AI1002 AI1003 AI1004 AI1005 AI1006 AI1007 AI 1001 2006 1,0 0,75 0,86 1,0 1,0 1,1 0,9

2007 1,3 0,75 1,0 1,1 1,0 1,1 0,9

2008 1,3 1,0 1,28 1,1 1,1 1,1 0,95

2009 1,0 1,2 0,71 1,0 1,1 1,2 0,95

2010 1,3 1,0 1,42 0,9 0,9 1,2 0,9

1 means according to plan, >1 it takes more time, <1 it requires less time

Table 5 Average scrap rate

Year AI1002 AI1003 AI1004 AI1005 AI1006 AI1007 AI 1001 2006 7,1 % 2,1 % 0,6 % 1,0 % 0,9 % 0,5 % 1,1 %

2007 6,0 % 1,2 % 0,7 % 1,1 % 0,9 % 0,5 % 1,9 %

2008 6,0 % 1,1 % 0,9 % 1,1 % 0,7 % 0,7 % 0,9 %

2009 3,7 % 1,4 % 0,5 % 1,0 % 0,8 % 0,7 % 1,0 %

2010 5,1 % 0,9 % 1,0 % 0,9 % 0,6 % 0,9 % 1,2 %