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
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BRIDGE – Building Radio frequency IDentification solutions for the Global Environment
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.
Opportunity
Analysis
Feasibility
Analysis
• Identify areas of value addition • Assess feasibility of applications
through waste reduction
from organisational compatibility,
operational reliability, technical
• Select applications for further
feasibility points of view
consideration
• Gather requirements for IT
infrastructure and human factors
Business
Case
Deployment
• Quantify waste removal and map
onto value drivers
• Draw soft benefits Compare
benefits against costs of
implementation
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
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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.
Process A
Process B
Process C
Process D
Actual flow of
batch
Information
system
(a) Scenario 1: Barcode scan missed
Actual flow of
batch
Process A
Process B
Process C
Process D
Information
system
(b) Scenario 2: Wrong barcode scan
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
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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-inInventory
Manufacturing
progress
management
asset tracking and
management
maintenance
Overproduction
Waiting
Transport
Inappropriate
processing
Unnecessary
inventory
Unnecessary
motion
Defects
Manufacturing
control
Know how much of
which
goods/materials are
WIP
Know where
finished
goods/materials are
Know how much of
which goods/materials
are in stock
-
Enable
automated JIT
strategies
Know where finished
goods/ raw materials
are
Know where WIP
goods/materials
should be brought
to
Know where nearest
finished goods /raw
materials are
Know where assets
are
Know condition of
assets
Know location of
nearest available
assets
Know which
goods/materials are
suitable for which
processing
Eliminate mistaken
WIP
goods/inventory
association
Improve visibility
level
Eliminate manual
data collection
Know which raw
materials suitable for
which processing
Increase product
autonomy in
distributed
control systems
Where applicable
implement
automated
routing on
production lines
Know which
goods/materials
are suitable for
which processing
-
Reduced scraps
due to improved
traceability
Know finished goods
/raw materials expiry
dates and implement
suitable protocols
Improve inventory
visibility
Eliminate manual
counts
Eliminate production
errors due to incorrect
manufacturing asset
maintenance
Eliminate
unnecessary buffers
waiting for asset
maintenance
Eliminate manual
checks for
maintenance
-
-
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)
Raw and finished goods stock
Preparation
Shipping
5
1
4
Machining
x5
Buffer
2
Finishing
3
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.
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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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Wrapping
13
Buffer
Filling
1
Filling
11
11
11
J-Factory
E-Factory
Cold Store
7
2
6
Tipping
Unwrapped Sweet
Management
5
12
4
Washing
Moulding Line
Buffer
8
Packing
3
Rework
9
10
Rework
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 actorcase 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).
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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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Route scanned data to Middleware
Operator
Barcode scanner
Operate barcode scanner
Middleware
Display where new stock should be
brought and if item is of correct quality
status
Display
Process and send
information to MRT
Pass messages to MRT
Perform goods issue
and stock removal
operations
MRT
BIF
Hold control recipes, transfer orders, purchase
orders and material master data
ERP
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.
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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.
Data
Data
dependency
dependency
*
batch
Product transformation step
*
Data
Data
dependency
Data
transformation
step
shift
dependency
*
Data
dependency
shift
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.
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Fill tub with GRAB oil from Silo
SSCC barcode
on tub
Create tub ID
Transport tub to freezer
Scan freezer
Tub in freezer
Tub FIFO
locations
Tub needed
Apply FIFO
Transport tub to chiller
SSCC barcode
Chiller barcode
Goods issue
barcode
tub in chiller
Tub needed
Tub FIFO
locations
Tub did not
expire
Send to rework
SSCC barcode
Use and discard tub
Goods issue to
the roaster
process order
Empty tub to processor
Figure 8 DDD – Case Example
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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 ongoing 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
Process step 1
Decision impact
Process step 2
Decision impact
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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, reordering 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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h
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material
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Material in/out of
inventory
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tc
op
er
a
to
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Test quality
ER
P
h
tc
ba
Receive process
order
ite
m
Inventory moves to
WIP inventory
op
er
at
or
Collect materials
WIP inventory level
adjustment
ER
P
m
ite
Weigh materials
ite
New inventory type
created
op
er
a
to
r
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Assemble
fragrance
ite
New inventory
finished type
m
ER
P
Test quality
op
er
a
to
r
h
tc
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Line fill for
customer
h
tc
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or
Store
ba
h
tc
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er
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Pallet packing
op
er
at
or
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Move to
warehouse
Inventory reduced
ER
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Figure 10 DataVis-Case Example
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BRIDGE – Building Radio frequency IDentification solutions for the Global Environment
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
1
Disturbance (packing lines)
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.
1200
1000
Average impact
800
600
400
200
0
1
2
3
4
5
Delay code
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 semistructured 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
Response code
Code
1
1.1
1.2
1.3
2
3
4
Response
Maintenance staff available
Stoppage analysis module (SAM) – improve and better plan
Scheduled maintenance
1.4
Spare parts
2.1
3.1
4.1
Barcoding
Spares
Planning and scheduling (partly due to better picture of
material movement)
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5
4.2
4.3
4.4
4.5
Labour number increase
Increase local and buffer stock
Increase flexibility (substitute sweet type)
Prevent machine breakdowns (largest contribution)
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)
5.4
1.1
4
1.2
3
5.3
1.3
2
1
5.2
1.4
0
5.1
2.1
4.4
3.1
4.3
4.1
4.2
Capability rating
Utilisation rating
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.
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Machine breakdow n
History untraceable
Bottlenecks
Materials do not arrive on time
Material in incorrect quality state
Disturbance impact
1400
1200
1000
800
600
400
200
0
0
0.5
1
1.5
2
2.5
3
3.5
Capability
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
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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
Origin of
Waste
Particular strengths
tool
tool
Identifies manual data collection points,
PPM
New
geographical distribution of data locations leading to
unnecessary movement of operators and products
Unnecessary
Shows the use cases, that the current system
motion
knows how to perform, and actors taking part in
Object
Transport
system functionality. Can be used to differentiate
UCD
management
what parts of the process are done by error prone
group
actors, what parameters are modified by the
information system.
Identifies process decision points to conclude
Waiting
Defects
on the importance of data capture, and what
Overproduction
processes are affected from what errors
DDD
Unnecessary
New
Identifies what level of concurrency is involved
in the operations and if process speed will
inventory
improve if data dependency conformance is
Inappropriate
processing
automated
Overproduction
Identifies how visibility levels and parameters
DataVis
Unnecessary
New
affect batch sizes, and work in progress and
inventory
finished inventory.
Examines the impact of disturbances in a
(Thorne et
manufacturing process, helps understand
al 2007)
current capabilities and utilisation of those
(Matson
capabilities to address disturbances. In doing
PRA
All
and
so, examines whether RFID technologies can
McFarlane
help improve existing capabilities or their
1999)
utilisation.
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BRIDGE – Building Radio frequency IDentification solutions for the Global Environment
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):765784, 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.
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