methodology for manufacturing process analysis for rfid implementation

Building Radio frequency IDentification for the Global Environment

Report: Methodology for manufacturing process analysis for RFID implementation

Authors: Alexandra Brintrup (Cambridge Auto-ID Lab), Paul Roberts (NESTLÉ), Mark Astle (NESTLÉ)

March 2008 This work has been partly funded by the European Commission contract No: IST-2005-033546

About the BRIDGE Project: BRIDGE (Building Radio frequency IDentification for the Global Environment) is a 13 million Euro RFID project running over 3 years and partly funded (€7,5 million) by the European Union. The objective of the BRIDGE project is to research, develop and implement tools to enable the deployment of EPCglobal applications in Europe. Thirty interdisciplinary partners from 12 countries (Europe and Asia) are working together on : Hardware development, Serial Look-up Service, Serial-Level Supply Chain Control, Security; Anti-counterfeiting, Drug Pedigree, Supply Chain Management, Manufacturing Process, Reusable Asset Management, Products in Service, Item Level Tagging for non-food items as well as Dissemination tools, Education material and Policy recommendations. For more information on the BRIDGE project: www.bridge-project.eu This document results from work being done in the framework of the BRIDGE project. It does not represent an official deliverable formally approved by the European Commission. This document: In this document we aim to develop a set of process analysis tools to help organisations identify opportunities where RFID can bring value. The process analysis tools form the opportunity analysis phase of the roadmap followed in the manufacturing work package, where value addition through waste reduction is identified. The remainder of the roadmap consists of several other windows of analysis organisations need to consider, including an intermediary feasibility analysis phase where application requirements relating to information flow, feasibility, human factors and IT infrastructure are collected, a business case phase where the benefits derived from RFID implementation are compared against the costs of implementation before deployment.

Disclaimer: Copyright 2007 by (Cambridge Auto ID Lab, Nestlé) All rights reserved. The information in this document is proprietary to these BRIDGE consortium members This document contains preliminary information and is not subject to any license agreement or any other agreement as between with respect to the above referenced consortium members. This document contains only intended strategies, developments, and/or functionalities and is not intended to be binding on any of the above referenced consortium members (either jointly or severally) with respect to any particular course of business, product strategy, and/or development of the above referenced consortium members. To the maximum extent allowed under applicable law, the above referenced consortium members assume no responsibility for errors or omissions in this document. The above referenced consortium members do not warrant the accuracy or completeness of the information, text, graphics, links, or other items contained within this material. This document is provided without a warranty of any kind, either express or implied, including but not limited to the implied warranties of merchantability, satisfactory quality, fitness for a particular purpose, or non-infringement. No licence to any underlying IPR is granted or to be implied from any use or reliance on the information contained within or accessed through this document. The above referenced consortium members shall have no liability for damages of any kind including without limitation direct, special, indirect, or consequential damages that may result from the use of these materials. This limitation shall not apply in cases of intentional or gross negligence. Because some jurisdictions do not allow the exclusion or limitation of liability for consequential or incidental damages, the above limitation may not apply to you. The statutory liability for personal injury and defective products is not affected. The above referenced consortium members have no control over the information that you may access through the use of hot links contained in these materials and does not endorse your use of third-party Web pages nor provide any warranty whatsoever relating to third-party Web pages.

BRIDGE – Building Radio frequency IDentification solutions for the Global Environment

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TABLE OF CONTENTS

1. INTRODUCTION ......................................................................................................................................... 5

2. THE SEVEN WASTES VERSUS RFID ................................................................................................... 6

3. A PRACTICAL ROADMAP TO RFID VALUE IDENTIFICATION ..................................................... 10

3.1 DATA COLLECTION ............................................................................................................................... 10 3.1.1 Physical Process Mapping (PPM) ......................................................................................... 11 3.1.2 UML Use Case Diagrams (UCD) ........................................................................................... 13

3.2 DATA DEPENDENCY ............................................................................................................................. 15 3.3 DATA VISIBILITY ................................................................................................................................... 18 3.4 PRODUCTION RESPONSIVENESS APPROACH (PRA) .......................................................................... 21

4. USING THE TOOLKIT.............................................................................................................................. 26

5. CONCLUSION ........................................................................................................................................... 27

6. REFERENCES........................................................................................................................................... 28

List of Tables TABLE 1 TOYOTA PRODUCTION SYSTEM TYPES OF WASTAGE REDUCTION THROUGH RFID ............................... 9 TABLE 2 SUMMARY OF DISTURBANCES ................................................................................................................ 22 TABLE 3 DISTURBANCE RESPONSES .................................................................................................................... 23 TABLE 4 MAPPING TOOLS FOR RFID IMPLEMENTATION ...................................................................................... 26

List of Figures FIGURE 1 ROADMAP TO RFID DEPLOYMENT ......................................................................................................... 6 FIGURE 2 BARCODE SCAN SCENARIOS .................................................................................................................. 7 FIGURE 3 PHYSICAL PROCESS MAPPING .............................................................................................................. 11 FIGURE 4 PPM - CASE EXAMPLE ......................................................................................................................... 13 FIGURE 5 UML USE CASE DIAGRAM ................................................................................................................... 14 FIGURE 6 UCD- CASE EXAMPLE.......................................................................................................................... 15 FIGURE 7 DATA DEPENDENCY DIAGRAM ............................................................................................................. 16 FIGURE 8 DDD – CASE EXAMPLE ........................................................................................................................ 17 FIGURE 9 DATA VISIBILITY DIAGRAM.................................................................................................................... 19 FIGURE 10 DATAVIS-CASE EXAMPLE .................................................................................................................. 20 FIGURE 11 IMPACT VERSUS DISTURBANCE (REPRESENTATIVE NUMBERS BASED ON EXPERIENCE) .................. 23 FIGURE 12 DISTURBANCE RESPONSE CAPABILITY CHART ................................................................................... 24 FIGURE 13 IMPACT/RESPONSE CHART ................................................................................................................ 25


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GLOSSARY BIF Business Integration Framework

DataVis Data Visibility Diagram

DDD Data Dependency Diagram

ERP Enterprise Resource Planning

FIFO First-in-first-out

GRAB Ground & Roast Aroma Boost

IBC Intermediate Bulk Container

IT Information Technology

JIT Just-in-time

MRT Material Resource Tracking

PPM Physical Process Mapping

PRA Production Responsiveness Audit

RFID Radio Frequency Identification

UCD Use Case Diagram

UML Unified Modelling Language

WIP Work-in-progress


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1. Introduction

The BRIDGE project manufacturing work package aims to develop tools and

methodologies helping manufacturing organisations give effective decisions upon RFID

implementation. Implementing Radio frequency identification (RFID) within the four walls of a

manufacturing plant requires extensive analysis and experimentation. The sixth deliverable

“Manufacturing process mapping methodology” from the work package provides a set of

tools to European manufacturing organisations to analyse existing business processes and

target areas where RFID can bring value and reduce waste.

“There is a clear need to extend internal wastage removal to the complete supply

chain. However, there are difficulties in doing this. These include lack of visibility along the

value stream and lack of appropriate tools for creating this visibility.” (Hines P. and Rich N.,

1997) The statement of Hines and Rich holds true not only along the supply chain but also in

manufacturing. The lack of visibility and tools for creating visibility is a hinder to obtain

maximum value from many internal manufacturing operations, including inventory, and work

in process management.

RFID is seen by many as a revolutionary enabler in automatic data capture. RFID

tags coupled with readers and information systems architecture can increase visibility of

operations by associating unique product identification with its current location, and by

synchronising the physical flow of components/products and the related information flow

without human intervention. In addition to being an enabler of visibility, RFID technology has

found uses in a variety of other manufacturing related applications in production automation

and inventory management, a review of which has been given in the project deliverable 8.1

Problem Analysis.

Despite RFID’s success, confusion still remains as to where it can help in

manufacturing. Questions remain as to what aspects should be considered when selecting

applications, which manufacturing wastage RFID may specifically address, and how these

wastages can be identified.

Our previous industrial survey highlighted the need for a structured framework for

RFID value identification and deployment (Brintrup et al 2007). Being a relatively young

technology part of the reason for companies’ confusion is the lack of meaningful generic

case studies and exemplary work. Another part of the reason is the lack of structured tools

and methods to help pinpoint where RFID can create visibility, help in operations and to what

extent.

Apart from developing an understanding of how RFID can help create value, an

understanding of business processes is vital prior to implementation to (1) estimate costs

realistically and to (2) assess risks associated with changes that RFID brings. Saygin points

out that business cases need to be built on defined rules, and without reaching a lean


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perspective on operations and workflow in an organization, RFID cannot bring visibility out of

a chaotic environment (Saygin C. and Sarangapani J., 2006), suggesting the need for a

complete understanding of business processes affected from RFID implementation.

Although there exist various generic business process modelling tools and methods,

none of them seem readily to capture different aspects of a manufacturing system from an

RFID value perspective. This observation was further strengthened in the problem and

requirements analysis phases of our project, after which a decision to draft an RFID-generic

process analysis roadmap was made.

Following on this need, in this document we aim to develop a set of process analysis

tools to help organisations identify opportunities where RFID can bring value. The process

analysis tools form the opportunity analysis phase of the roadmap followed in the

manufacturing work package (shown on Figure 1), where value addition through waste

reduction is identified. The remainder of the roadmap consists of several other windows of

analysis organisations need to consider, including an intermediary feasibility analysis phase

where application requirements relating to information flow, feasibility, human factors and IT

infrastructure are collected, a business case phase where the benefits derived from RFID

implementation are compared against the costs of implementation before deployment.

Figure 1 Roadmap to RFID deployment

2. The seven wastes versus RFID

Lean manufacturing is ‘a philosophy of production that emphasises the minimisation

of the amount of resources (including time) used in the various activities of the enterprise’.

Lean manufacturing involves identifying and eliminating non value adding activities and

focuses on the start-to-end value streams rather than the idea of optimising individual

departments in isolation. Waste is a term frequently associated with lean manufacturing. In

this section we look into the seven wastes of manufacturing systems (Ohno T, 1988) and

Feasibility

Analysis

Business

Case

Opportunity

Analysis

• Identify areas of value addition

through waste reduction

• Select applications for further

consideration

• Assess feasibility of applications

from organisational compatibility, operational reliability, technical

feasibility points of view

• Gather requirements for IT

infrastructure and human factors

• Quantify waste removal and map

onto value drivers

• Draw soft benefits Compare

benefits against costs of implementation

Deployment


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consider how they can be reduced using RFID to move towards a lean organisation. The

following wastes are given:

1. Overproduction, which discourages a smooth flow and leads to excessive lead and

storage times.

2. Waiting, which occurs when time is being used ineffectively.

3. Transport, a non-value adding operation which involves goods being moved around.

4. Inappropriate processing, which occurs when systems or procedures more complex than

necessary are used, leading to excessive transport and poor quality.

5. Unnecessary inventory is unused capital, leading to storage costs, or possible quality

deterioration of goods if the time of storage is critical to its health.

6. Unnecessary motion refers to the ergonomics of production when workers need to move

in unnatural positions repetitively, possibly leading to tired workers and compromises on

quality.

7. Defects are costs directly attributed to wastage of produced material that could potentially

bring revenue.

Figure 2 Barcode scan scenarios Let us consider an occasion when a barcode scan during a goods issue operation to

a physically transforming process step is not carried out at step B (Figure 2 (a)). We know

from our previous industrial survey that this occurs frequently in the normal operations of a

factory, especially during peak seasons where temporary operators are employed. The

information system shows a certain amount of material under a process step C while the

material is actually on its way to undergo its next process step D.

The above scenario has various implications in the above waste categories where

RFID technology offers a number of direct and indirect benefits.

• In the case that the machines allocated to the subsequent process need reconfiguration

the information system may ask for the changes to be made in advance. Looking at the

Process A Process B Process C Process D

Actual flow of batch

Information

system

Process A Process B Process C Process D Actual flow of

batch

Information

system

(a) Scenario 1: Barcode scan missed

(b) Scenario 2: Wrong barcode scan


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alerts from the information system, Process B awaits the arrival of the next batch

although the process has already been carried out, missing out on the valuable time that

can be used to prepare machinery for the actual next batch. Although Process D is the

process that has to be getting ready for the arrival of the batch, it assumes there still is

time. This mismatch leads to a waste of time, i.e. waiting waste.

• The batch may be transported back to Process B for it to be repeated since we have lost

traceability on whether it has actually been carried out, leading to possibly inappropriate

processing, transportation waste and possibly defects.

• The shift manager may decide to scrap the batch if traceability for that process was

critical; for instance in the case of a batch testing process leading to defect waste.

Let us consider another scenario (Figure 2 (b)) where the worker scans the wrong

barcode and associates another batch type with the subsequent process. Since the Process

A for this batch is not completed the batch might be sent for re-processing leading to

wastage in transport, waiting, and possible defects.

• With the scanning of the wrong barcode, two different batches from one are created,

leading to an inaccurate picture of inventory and overproduction of batches for which the

information system displays to have little stock.

• The set of machine resources carrying out Process A seem to be occupied with the batch

assigned to it, while in reality it is not. This causes other batches to wait in the queue

until the error is found out and corrected.

• If the initial batch record is associated with a quality restriction and the newly aggregated

batch is not, the scan error may lead to the production of substandard quality goods,

leading to severe defect wastage. In both of the scenarios if the error is noticed and

correction attempted, time spent to management of information is increased, leading to

waiting wastage.

Although the above scenarios are typical of work in progress management (WIP), if

WIP products are taken as an analogy to assembly operations in automated production

control, the above mistakes can easily be replicated.

In inventory management, reliance on barcode scanning may result in overproduction

wastage, as wrong scans are performed for in and out of the warehouse. The search for the

correct products lead to transport and waiting wastage, and the deterioration of

overproduced or untraceable products in the warehouse lead to defect wastage.

In terms of JIT inventory control, loss of visibility occurs if a barcode is damaged,

leading to overproduction, unnecessary inventory and undermine of the JIT operation,

whereas RFID being more durable can offer higher guarantees for a successful JIT

environment from this perspective.

For the asset tracking and maintenance cluster of RFID in manufacturing, if assets

are used to carry WIP products and thus used to control the production, the same principles


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of WIP management apply. On the other hand, if tagged assets are machinery and

equipment, RFID can help reduce waiting and defect wastage by providing real time visibility

of their condition.

Finally, under all RFID application scenarios, workers are saved from handheld

barcode scanning operations if appropriate readers are used, leading to the elimination of

unnecessary motion wastage. In addition manual record taking, counting, or manual checks

can be reduced or eliminated using RFID enabled systems. These are all clustered under

unnecessary motion wastage.

Table 1 summarises the types of wastage can be reduced by RFID under the cluster

of RFID applications reviewed in the previous section.

Table 1 Toyota Production System types of wastage reduction through RFID

Work-in-progress

management

Inventory management

Manufacturing asset tracking and

maintenance

Manufacturing control

Overproduction Know how much of which

goods/materials are WIP

Know how much of which goods/materials

are in stock

- Enable automated JIT

strategies

Waiting Know where finished

goods/materials are

Know where finished goods/ raw materials

are

Know where assets are

Know condition of assets

Increase product autonomy in distributed

control systems Transport Know where WIP

goods/materials should be brought

to

Know where nearest finished goods /raw

materials are

Know location of nearest available

assets

Where applicable implement automated routing on

production lines Inappropriate processing

Know which goods/materials are suitable for which

processing

Know which raw materials suitable for

which processing

Eliminate production errors due to incorrect manufacturing asset

maintenance

Know which goods/materials are suitable for

which processing Unnecessary inventory

Eliminate mistaken WIP

goods/inventory association

Improve visibility level

Improve inventory visibility

Eliminate unnecessary buffers

waiting for asset maintenance

-

Unnecessary motion

Eliminate manual data collection

Eliminate manual counts

Eliminate manual checks for

maintenance

-

Defects Reduced scraps due to improved

traceability

Know finished goods /raw materials expiry dates and implement

suitable protocols

- -

In addition to the above considerations where errors in barcode scanning can occur,

other benefits of RFID in manufacturing arise from moving onto innovative applications such

as distributed control systems where RFID acts as an enabler to the “intelligent product”

(McFarlane D. et al., 2003). Using RFID, the product may possess a unique identity, can

communicate with its environment through sensory information, and can retain data about

itself. The unique identity may point to the appropriate agent software residing on the

network, enabling the product to make decisions relevant to its own destiny. Advantages of

the system include robustness to disturbance, and flexibility to changes or extensions. On


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the other hand it points to a dramatic change in a company’s manufacturing philosophy.

Henceforth, the discussion encompasses only centrally controlled non-holonic environments

in this document.

Having mapped RFID use to manufacturing wastage elimination, the second step is

to provide organisations with the set of tools to analyse which waste can be targeted, and

where implementation can bring value.

3. A practical roadmap to RFID value identification

The previous section looked into manufacturing system waste reduction using RFID.

In this section four process analysis tools are suggested to allow practitioners to assess

manufacturing processes from an RFID value addition point of view. Value addition has been

collected under three topics: value addition during process data collection, through

conforming to data dependencies and through process visibility increase.

3.1 Data collection

RFID may automate data collection throughout manufacturing processes. Two types

of manufacturing waste are created in situations where data collection is performed through

barcode scanning or manual data entry: unnecessary motion performed by operators and

transport waste, created by bringing items to scan locations. To identify where these types of

waste are created and whether RFID can address them, two tools, offering different angles of

view are suggested: Physical Process Mapping (PPM), and UML Use-Case Diagram (UCD).


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3.1.1 Physical Process Mapping (PPM)

Figure 3 Physical process mapping

Physical process mapping is designed to identify where data collection operations lie

along the manufacturing plant. The resulting map depicts data entry and pull locations and

the method of data collection or entry (such as manual barcode scans, paper or computer

entries), projected among a representation of the manufacturing locations (Figure 3). Current

data pull and push points are numbered. Where there is more than one of the same type of

data point (such as one hundred moulding machines, each consisting of the same data step)

only one data point is depicted and the number of different units are given next to it. In

addition to providing information on data collection operations, the physical representation

also illustrates the complexity of production routes from a geographical point of view. The

diagram acts as an intuitive start point in thinking where RFID can be potentially replace

manual data collection and the extent of data operations. It provides a snapshot of data

projected upon operations, and can bring to light which operations are not associated with

data and not traceable.

Case Example:

For the reader to gain a better understanding of the approach, brief case examples are

presented in this document. The application work package partner Nestlé is a multi-national

confectionary and food producer following a centrally controlled batch manufacturing

philosophy. The company looked into deploying RFID for the Intermediate Bulk Container

(IBC) management process using the PPM tool.

2 3x5

Raw and finished goods stock

Machining Finishing

Shipping

1

5

4

Preparation

Buffer


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Figure 4 shows the IBC management PPM. It is revealed that the increasingly complex

current production routes result in high numbers of WIP buffers and high probability of errors

in storing and locating items. A total of 12 process steps use barcode scans, with many

variations in the types of products and process step locations. When discussed with the

Nestlé team, the following action points were summoned as a result of analysis with PPM:

• The high number of barcode scanning operations coupled with complexity of routes and

processes result in unnecessary scanning motion which could be automated with RFID.

• The high number of wrapping locations dictates a more cost effective solution. Installing

RFID on forklift trucks and tagging wrapping point locations can be a option.

• Washing and tipping processes are not fully tracked, giving rise to possible in

inappropriate processing, overproduction, waiting and unnecessary inventory wastage.

• The high number of IBCs and locations make inventory counts error-prone (i.e.

overproduction, and unnecessary inventory). An RFID based inventory management

system can be beneficial.


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Figure 4 PPM - Case Example

3.1.2 UML Use Case Diagrams (UCD) The Unified Modelling Language (UML) has been used to analyse the processes

found within complex real life systems including manufacturing systems. Use case diagrams

can be used to depict the functionality of the overall manufacturing system from an actor-

case point of view. The actors of the system interact with the system itself, the use cases, or

services, that the system knows how to perform, and the lines that represent relationships

between these elements. Detailing the system this way enables one to identify the level of

automation during data collection. The data collection action points in the PPM diagram are

connected to relevant actors (Figure 5).

Buffer

Filling

Packing

Washing

Cold Store

Moulding Line

1

3

6

12

11

13

11

11

Tipping

8

Wrapping

Filling

Buffer

J-Factory

E-Factory

Unwrapped Sweet

Management

4

7

2

5

Rework

9

10Rework


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Figure 5 UML Use Case Diagram The UCD gives a complementary view of how data collection is performed throughout

the manufacturing process. While the PPM shows physical data collection points and

complexity of production routes, the UCD shows by which actor the data is collected. The

next step is to find those actors that may cause errors and inaccuracies and analyse if RFID

based automated data collection is possible to replace the actor.

Case Example: Applied on a part of the Nestlé IBC management process, quality checking, the UCD

shows the existence of non-automated data pulling operations (Figure 6). Before an item is

tipped on the wrapping line, its quality status needs to be checked by the operator using a

barcode scanner and information displayer. Then several information system layers

(Middleware, MRT, BIF, and SAP) are parsed through to arrive at the quality status

information which is sent to the display.

Requirements raised from this exercise were the automation of barcode scan and

manual recording processes to result in a leaner manufacturing environment. The automated

RFID would take on the role of the operator to query quality status when an IBC is brought to

the wrapping location. The barcode actor is eliminated, and the operator takes on the new

role of “terminate process” if the display shows wrong quality status.


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Figure 6 UCD- Case Example

3.2 Data dependency

Through automation RFID helps make sure data dependency is respected throughout

manufacturing processes. Four types of manufacturing waste are created in situations where

data dependency is defaulted: waiting (if wrong information association results in delays

when error is noticed and correction attempted), defects (if wrong information association

results in wrongly processed products), overproduction (if wrong information association

results in producing more WIP products than necessary), unnecessary inventory (if wrong

information association results in producing more finished products than necessary). To

identify where these types of waste are created and whether RFID can address them, a Data

Dependency Diagram (DDD) is suggested (Figure 7).

Here each product value adding step is depicted as a product transformation step.

Each transformation step is dependant on a number of data, shown as input boxes to the

step. Data can be gathered using a number of ways, including manual data entry, manual

records on paper, or barcode scans. In addition, data itself can be transformed in terms of

format, for example from a paper recording to a mainframe computer. The frequency of data

collection is associated with the input.

Operator

Middleware

MRT

Operate barcode scanner

Route scanned data to Middleware

Display where new stock should be

brought and if item is of correct quality

status

Perform goods issue

and stock removal

operations

ERP

BIF

Process and send

information to MRT

Pass messages to MRT

Hold control recipes, transfer orders, purchase

orders and material master data

Barcode scanner

Display


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Resulting from this activity is a map of data dependencies existing across the process

flow. The next step is to understand (1) what would cause a data error for each process data

input, creating waiting, defect, overproduction or unnecessary inventory wastes, and (2) if

and how data collection frequency could be increased though RFID.

Figure 7 Data Dependency Diagram Case Example: Figure 8 shows an example process from the Nestlé GRAB oil process reviewed in

D8.1 and D8.2. Process steps were found to be highly data dependant and reliant on the

manual pull/push of information by the process operators, causing severe delays and errors.

Some process steps, although dependant on quality inspection data, do not come to a halt if

this data is not present, which ultimately leads to quality errors at later stages of production

with increasing cost of recalls. For instance, before goods are actually used or reworked,

three data are necessary: call for a tub, location of the tub (in line with FIFO), and quality

inspection. None of these data are automated and the collection of all relies on operator,

making it error-prone in terms of data completeness. RFID based FIFO inventory

management, and automation of the alarm raising when items fall below a predefined quality

status would make sure data dependency is respected in this process.

Product transformation step

Data

dependency

Data transformation step

Data

dependency�

Data

dependency�

Data

dependency�

Data

dependency�

*

*

batch

shift

shift

*


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Figure 8 DDD – Case Example

Scan freezer

Fill tub with GRAB oil from Silo

SSCC barcode

on tubCreate tub ID

Transport tub to freezer

Tub needed

Transport tub to chiller

Apply FIFO

Tub FIFO

locations

SSCC barcode Chiller barcodeGoods issue

barcode

Tub in freezer

Tub neededTub FIFO

locations

tub in chiller

Use and discard tub

Tub did not

expire

Send to rework

SSCC barcode

Goods issue to

the roaster

process order

Empty tub to processor


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3.3 Data Visibility

Visibility is significant contributor to giving effective stock order or goods issue

decisions throughout the manufacturing plant. Yet current methods for performing an

inventory count or for tracking asset movement do not provide real-time visibility leading to

decisions based on outdated, inaccurate information (Lu B.H. et al., 2006). Lack of visibility

on WIP and finished inventory is the root cause of the Bullwhip Effect in the forecast-driven

supply chain, where safety stocks for each supply chain participant are increased due

greater observed variation. Two types of manufacturing waste are created in situations

where visibility of operations is compromised: overproduction (when low visibility leads to the

belief that the work-in-progress stock of levels a given item is lower than it really is), and

unnecessary inventory (when low visibility leads to the belief that the finished stock level of a

given item is lower than it really is). Within a single manufacturing plant, RFID may enable

increases in data visibility at two levels: from batch level to item level throughout

manufacturing processes, and tracking stock at individual manufacturing processes. The

combination of the two gives the decision makers a more accurate, real-time sense of on-

going operations in terms of the time it takes to complete a process, associated batch or

item, the outcome of the process.

To identify where visibility can be increased and its effects on inventory levels, a Data

Visibility Diagram (DataVis) is suggested (Figure 9). There are four simple steps involved in

this approach given as below.

For each process step:

1. Outline

• the visibility level i.e. batch or item level information

• to whom or what the process is visible to

• what is visible (e.g. time it takes process to be completed, location process takes

place, process success for associated items etc.)

2. Discuss how the level of visibility affects the next process step in terms of buffer or

work-in-progress stock

3. Modify the outlined visibility parameters

4. Discuss whether modified parameter increases the level of visibility and creates a

Decision impact Process step 1

Decision impact Process step 2


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positive impact on stock decision making

Figure 9 Data Visibility Diagram Case Example: A DataVis analysis was applied to a fragrance manufacturing process at a Cosmetics

firm. The DataVis shown on Figure 10 reveals that some parts of the process were not

captured, giving raise to inaccurate WIP inventory levels. Containers that carry WIP materials

were at times not visible as they always moved or their barcodes were damaged, and could

not be counted, leading once more to inaccurate inventory information.

When raw material is received, items were booked into the IT system only after

certain quality tests are done. This could result in delays finding raw material and waiting in

the production line for items from suppliers that were already in stock, and at times, re-

ordering of items. The final stages of the process, packaging and palleting, collected batch

level information which was only visible to the operator until dispatch. The line fill process

was not captured and items could be lost in the storage location associated with finished

items. It was found that not all data collected during process steps were visible at the ERP

level, where forecasters gather information from, which resulted in a requirement that

synchronised, timely and accurate information is visible at all levels of information hierarchy.

Furthermore, a transition from batch level information to item level information was required

in the dispatching process to provide accurate record of dispatched items.


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Figure 10 DataVis-Case Example

Weigh materials

Receive process order

Collect materials

Assemble

fragrance

Test quality

Test quality

Line fill for customer

Store

Pallet packing

Move to

warehouse

Dispatch

Material in/out of

inventory

Inventory moves to

WIP inventory

WIP inventory level

adjustment

New inventory type

created

New inventory

finished type

Inventory reduced

operator

operator

batch

batch

item

item

item

batch

item

batch

batch

batch

batch

batch

ERP

operator

ERP

operator

ERP

operator

operator

operator

operator

ERP

Receive raw material


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3.4 Production responsiveness approach (PRA)

Another route for organisations to identify value of RFID implementations is through

disturbance analysis. Production responsiveness is ‘the ability of a production system to

achieve its goals in the presence of disturbances’.

A disturbance is ‘a change occurring internally or externally to a production system,

which can affect its operational performance, and is either outside its control or has not been

planned for by the system’.

(Matson and McFarlane 1999) suggest that a sensible assessment of the impact of

disturbances can only be made with direct reference to an organisation’s production goals,

and that the overall affect of a disturbance covers the immediate effects of the disturbance

and the effects of any response. To achieve its goals in the presence of disturbances, a

production system must respond after the disturbance has occurred and/or have responded

in advance of the known possibility of the occurrence of the disturbance.

A Production Responsiveness auditing tool can be used to help a company evaluate

its current ability to handle disturbances affecting its production performance, and decide

appropriate actions for improving its responsiveness. In our case we look at actions possible

through the use of RFID to improve responsiveness.

The 5 steps of this audit are outlined as follows:

Step 1 Understand the operation: Tools like process mapping can be used to clarify

processes.

Step 2 Goal identification - Understand how operational performance is measured.

Step 3 Disturbance responsiveness assessment: For each type or each class of

disturbances a Disturbance Responsiveness Chart is plotted to capture the nature of the

disturbance and its impact on the process.

Step 4 Disturbance Response Capability Assessment: For each type of disturbance a

Capability Chart is also produced. This chart provides an assessment of how well the

capability can respond to the disturbance.

Step 5 Impact/Response Capability Summary Chart: The final step involves producing a

chart that can assist in comparing disturbances in terms of the current impact on production

goals, and the extent to which capabilities exist for overcoming them. This chart can be used

to help make decisions on improvement actions for adding or improving capabilities.

The responsiveness auditing tool can be applied to examine how well processes and

systems handle the different disturbances that occur during a manufacturing process, and


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highlight the level of their impact. These processes and systems can be examined more

closely to see if improvements can be made through the use of RFID technology.

An example of the approach is given with the Nestlé IBC management process in the

following paragraphs.

Step 1 Understand the operation

The processes involved in the IBC management activity have been heavily examined

using Value Stream Maps, Physical Process Mapping and UML diagrams in Deliverable 8.1.

Readers are referred to the aforementioned deliverable for an illustration of how the WP8

team developed an understanding of the process.

Step 2 Goal identification

Discussions held with the staff at the Halifax plant confirm the main goals of

operations as: Timely and cost effective response to production demand with seamless

operations and as little error handling as possible. The factors mentioned in this goal

statement are: timeliness, reduction of errors, and reduction of costs. Other factors

mentioned include customer satisfaction and loyalty through quality of goods which are

directly relevant to traceability of operations.

Step 3 Disturbance responsiveness assessment

This assessment is concerned with determining the nature of disturbances and their

impact on the goals of an organisation. Listed on Table 2 are the disturbances found in the

Halifax factory after consultation with Nestlé.

Table 2 Summary of disturbances

Code Disturbance (packing lines)

1

Machine breakdowns

2 Product history untraceable

3 Bottlenecks (FLT, hoist, storage space)

4

Materials do not arrive on time

5

Materials in incorrect quality state

Because the measures of frequency of occurrence, and average duration of a delay cover

both the nature and impact of a disturbance, it is felt that one disturbance impact measure

would enable the comparison of disturbances.


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The disturbance impact measure (per unit time) that will be used to compare disturbances is

given in the following equation.

Average Disturbance Impact = Average delay (min) x Frequency of disturbance (min)

The average delays times and frequencies are collected using semi-structured interviews

with the Nestlé managers, and represent average weekly figures.

Figure 11 Impact versus delay code (given on Table 3) (representative numbers based on experience)

Step 4: Disturbance Response Capability Assessment For disturbances identified in Step 3 a corresponding Disturbance Response Capability Chart

is produced as shown on Table 3. This step we determine the existing response capabilities,

their potential to solve disturbances and their current utilisation, once more through semi-

structured interviews with Nestlé managers.

• Each capability is assigned a value of 0, 1, 2 or 3 depending on the potential of that

capability to solve the disturbance. (3-High, 0-Low capability)

• Each capability is assigned a value of 0, 1, 2 or 3 depending on the utilisation of that

capability. (3-High, 0-No utilisation)

Table 3 Disturbance responses

Disturbance Code

Response code Response

1

1.1 Maintenance staff available

1.2 Stoppage analysis module (SAM) – improve and better plan 1.3 Scheduled maintenance

1.4 Spare parts

2 2.1 Barcoding 3 3.1 Spares 4

4.1 Planning and scheduling (partly due to better picture of material movement)

0

200

400

600

800

1000

1200

Avera

ge i

mp

act

1 2 3 4 5

Delay code


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4.2 Labour number increase 4.3 Increase local and buffer stock 4.4 Increase flexibility (substitute sweet type) 4.5 Prevent machine breakdowns (largest contribution)

5 5.1

Barcode based manual quality status check

5.2 More timely data validation checks (check materials in all locations validate on every movement)

5.3 More timely quality checks

5.4 Improved planning (more stock being available at the right time)

Figure 12 Disturbance response capability chart

Figure 12 shows the capability and utility of responses identified in Table 3.

Step 5: Impact/Response Summary Chart If impact of a disturbance is high and the potential of current capabilities to solve the

disturbance is low, additional improvement to existing capabilities should be strongly

considered.

Similarly, if the impact of a disturbance is high, the potential of current capabilities to

solve the disturbance is high, but the current utilisation of these capabilities is low, staff may

need further training or systems may need to be adapted to make better use of information

available. In our case we observe mostly the low utilisation of existing capabilities.

0

1

2

3

41.1

1.2

1.3

1.4

2.1

3.1

4.1

4.2

4.3

4.4

5.1

5.2

5.3

5.4

Capability rating

Utilisation rating


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Figure 13 Impact/Response Chart

Although untimely material arrival has a high disturbance impact there exist many

capabilities, mostly under-utilised. The same can be said for mid-impact machine

breakdowns.

Step 6: Areas for the use of ID technologies

By examining the Impact / Response table and or the Impact/Response chart, it is

possible to highlight processes that may benefit from the use of RFID technology. Although

untraceable history and materials arriving at the incorrect quality state have relatively low

disturbance when compared with machine breakdowns, and the potential of current

capabilities to solve the disturbance is high, the current utilisation of these capabilities is low.

The low utilised capabilities include respecting barcode scan processes. Automated data

capture quickly resents itself as one possible solution that can help utilise this capability.

Machine health diagnosis and prognosis using automated data capture and processing may

point to another area of potential improvement on the existing capability as only periodic

health checks are performed rather than prognosis and condition based maintenance. The

use of RFID technologies in this area are well researched and documented (PROMISE 2004,

DYNAMITE 2006). Improved planning and scheduling capability is another area of

improvement which can significantly affect materials not arriving on time or incorrect quality

state of materials. This is due to the significantly improved visibility on materials moving

through processes. Once visibility is increased, movement can be better planned with

appropriate business logic in place, resulting in a smoother flow. Additionally, materials with

0

200

400

600

800

1000

1200

1400

0 0.5 1 1.5 2 2.5 3 3.5

Capability

Dis

turb

an

ce im

pact

Machine breakdow n

History untraceable

Bottlenecks

Materials do not arrive on time

Material in incorrect quality state


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the incorrect quality state can be tracked earlier in the process and be dealt with without

causing stoppages in the line.

4. Using the toolkit

Table 4 shows a summary of the tools and their use in identifying where RFID can be

used to reduce relevant manufacturing waste. The opportunity analysis phase should consist

of identifying waste estimates in the organisation through a series of interviews with

managers, such that the results of the initial discussion can provide a basis for validating the

mapping process once it is completed. The mapping process can commence with the tool set

offering the estimated wastage. Descriptions of the wastes can be made to managers by

giving them relevant examples without introducing bias. Once mapping is complete a set of

requirements will emerge for the practitioner which can be used to devise a technical and IT

feasibility analysis in the next stage.

Table 4 Mapping tools for RFID implementation

Mapping tool

Waste Origin of tool

Particular strengths

PPM

Unnecessary motion Transport

New Identifies manual data collection points, geographical distribution of data locations leading to unnecessary movement of operators and products

UCD Object management group

Shows the use cases, that the current system knows how to perform, and actors taking part in system functionality. Can be used to differentiate what parts of the process are done by error prone actors, what parameters are modified by the information system.

DDD

Waiting Defects Overproduction Unnecessary inventory Inappropriate processing

New

Identifies process decision points to conclude on the importance of data capture, and what processes are affected from what errors Identifies what level of concurrency is involved in the operations and if process speed will improve if data dependency conformance is automated

DataVis Overproduction Unnecessary inventory

New Identifies how visibility levels and parameters affect batch sizes, and work in progress and finished inventory.

PRA All

(Thorne et al 2007) (Matson and McFarlane 1999)

Examines the impact of disturbances in a manufacturing process, helps understand current capabilities and utilisation of those capabilities to address disturbances. In doing so, examines whether RFID technologies can help improve existing capabilities or their utilisation.


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5. Conclusion

Following our survey and observations from existing literature regarding RFID

adoption plans and barriers to adoption, it has been found that one of the main obstacles to

implementation is the lack of analysis tools to show where and how RFID can bring value.

Building on this observation, we identified how RFID can serve as a vehicle to reduce the

seven wastes of manufacturing and outlined an analysis toolkit for RFID implementation in

manufacturing organisations.

The analysis is comprised of identifying where RFID can bring value through

automated data collection, conformance to data dependencies and improvements in visibility.

PPM and UML Use case diagrams show overall process information and target motion and

transport wastage. DDD diagram shows wastage that may occur due to disrespecting data

dependencies. DataVis diagrams show how visibility improvements can help make better

inventory decisions. Finally use of a production responsiveness audit is proposed to identify

current disturbances in operation, capabilities and utilisation of those capabilities where RFID

technologies are considered to improve existing capabilities or their utilisation. The toolkit

has been validated using industrial case studies throughout the document.


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6. References

Booch G., Rumbaugh, J., and Jacobson I. The Unified Modelling Language User Guide. Addison Wesley, 2000.

Brintrup A. RFID in Manufacturing: Initial Experiences in the BRIDGE Project. RFID Outlook: Towards a European Policy on RFID, vol. Lisbon, Portugal 2007.

DYNAMITE, 2006, "Dynamic Decisions in Maintenance (DYNAMITE)" home page: http://osiris.sunderland.ac.uk/%7Ecs0aad/DYNAMITE/Index.htm, accessed on 02/2008.

Hines P. and Rich N. The seven value stream mapping tools. Int. Journal of Production and Operations Management 1997; 17 (1): 46-64.

Lee H. and Ozer O. Unlocking the value of RFID. Production and Operations Management 2007; 16 (1): 40-64.

Lu B.H., Bateman R.J., and Cheng K. RFID enabled manufacturing: fundamentals, methodology and applications. Int. Journal of Agile Systems and Management 2006; 73-92.

Matson, J. B. and McFarlane, D. C. Assessing the Responsiveness of Existing Production Operations, International Journal of Operations and Production Management, 19 (8):765-784, July. 1999

PROMISE, 2004, "PROduct lifecycle Management and Information tracking using Smart Embedded systems (PROMISE)" home page: www.promise.no, accessed on 02/2008.

Ohno T. Toyota Production System: Beyond Large-Scale Production. Productivity Press, 1988.

Thorne A., Barret D., McFarlane D., Examining the impact of Auto ID technologies on Aircraft Turnaround Process, Industry Engineering and Management Systems (IEMS), Florida, 12-14 March 2007.

methodology for manufacturing process analysis for rfid implementation

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