Index
A
active tags, 6
apparel products
order and delivery models, 130–4
order and delivery models in apparel supply chains, 131
assisted sales index, 215
attribute matching satisfaction index (AMSI), 169
B
barcode, 2
Benetton, 208
bill of loading (BoL), 36
black hole, 71
business process reengineering (BPR)approach, 15
C
case-based model, 206
classical optimization techniques, 102
collaborative planning, forecasting and replenishment (CPFR), 134
Compare, 149
compatibility, 84
computer-based simulation method, 206–7
Connection, 149
Critical Ratio (CR)rules, 145
D
database management system, 174
discounted pay-back period (DPP), 221
distribution and retail operations improvement
process reengineering, 15–28
average quantitative data resulting from data collection phase, 18–19
TO BE reengineering, 20–6
cost-benefit analysis, 26–8
cost/saving balance, 28
AS IS analysis, 20
research methodology, 15–19
supply chain processes resulting from the AS IS analysis and corresponding TO BE reengineering, 21–5
visits performed for data collection and main characteristics of there tail stores examined, 16–17
profitability analysis, 28–33
break-even curve of the RFID investment, 31–2
illustration of the break-even curve of the RFID investment, 31
NPV, IRR, PBP and ROI of some specific case studies, 32–3
NPV of investing in RFID for the RS, 29
NPV of investment for the supply chain, 30
NPV of the investment for a representative supply chain, 30–1
NPV over 5 years for a single retail store, 29
RFID Fashion Pilot Project, 33–9
project results, 35–9
tracking points and data collection, 33–5
role of RFID technology, 13–39
E
Earliest Due Date (EDD), 145
Efficient Consumer Response (ECR), 134
electronic article surveillance (EAS), 14
electronic data interchange (EDI), 134
Electronic Product Code – Information Service (EPCIS), 152
enterprise resource planning (ERP)system, 95
European apparel industry
production and distribution, 127–30
event study methodology, 189–90
ex ante evaluation, 216
ExcEdit, 149
experiments, 112–19
basic data of experiment 1, 113
basic data of experiment 2, 114
basic data of experiment 3, 115
basic data of experiment 4, 116
optimized operation assignment and task proportions of four experiments, 118
optimized production control results of four experiments, 119
parameters of learning curve of each operator, 117
F
Fashion Matching Satisfaction Index (FMSI), 168
fashion retail outlets
modelling the effectiveness of RFID technologies in improving sales performance, 203–24
future trends, 222–4
impact assessment, 218–21
Miroglio Fast Fashion case study, 207–10
model testing, 216–18
retail store operations, 206–7
return on investment assessment from RFID technology, 221–2
store operations at Miroglio, 210–16
value assessment, 205–6
fashion retailing
assessing role of RFID, 181–2
cross- and up-selling can be made possible using smart fitting rooms, 182
early detection of customer’s preference, 181
enriching customers in-store purchase experience without changing fitting process, 181–2
new fitting and transaction experience, 182
standardized cross- and up-selling approach, 182
fashion supply chain
RFID in improving distribution and retail operations, 13–39
process reengineering, 15–28
profitability analysis, 28–33
RFID Fashion Pilot Project, 33–9
role of RFID technology in process management and product tracking improvement, 42–65
case studies in the use of RFID technology, 52–64
RFID in supply chains, 44–6
RFID technology in practice, 51–2
RFID use in fashion and textile supply chain, 46–51
fashion supply chain management
role of RFID technologies, 1–11
from barcode to RFID technology, 2–4
barcode vs RFID technologies, 4
RFID applications, 9–11
RFID technology, 4–9
fashion supply chains
order allocation improvement using radio frequency identification (RFID) technologies, 126–54
benefits, 149–53
future trends, 153–4
garment allocation to fulfil customer orders, 139–47
order and delivery models of apparel products, 130–4
production and distribution in European apparel industry, 127–30
software support for garment allocation, 147–9
warehouse based vs flexible garment distribution forms, 134–8
fast fashion, 14
Fiorella Rubino, 208
First-Come-First-Served (FCFS), 145
fixed reader, 7
flexible assembly lines (FALs)
intelligent decision support system production control, 106–11
modified bi-level genetic algorithm, 108–10
operation routing rule, 110–11
system architecture, 106–8
key issues in development, 100–2
fuzzy screening technique, 168–72
G
Gap, 208
garment assembly line operations
role of radio frequency identification (RFID) technologies, 99–123
intelligent decision support system for production control on FALs, 106–11
key issues in developing flexible assembly lines (FALs), 100–2
modelling flexible assembly lines (FALs), 103–5
testing effectiveness of intelligent PCDS system, 111–21
garment manufacturing operations improvement, 71–4
assessing the effectiveness of RFID technology, 81–2
business value of RFID-based manufacturing process management system, 82–6
list of benefits of RFID-based manufacturing process management system, 83
technology push and need pull factors for adoption of an RFID-based garment manufacturing IS, 84
factors in successful implementation of RFID-based manufacturing process management system, 86–90
cost/benefit evaluation, 89
extent of progress supervision, 89–90
list of success factors for RFID-based system implementation, 87–8
organisation motivation, 88
policy, structure and operating process compatibility, 90
staff competence and training, 90
top management support, 89
user involvement, 89
vendor selection, 87–8
implementing RFID-based manufacturing process management system, 76–81
employee efficiency in production, 81
project team structure, 80
set-up of Smart Term Z1 network and cabling connection, 79
system implementation: Stage I trial run (August to December 2007), 78
system implementation: Stage II full implementation (January to May 2008), 78–80
system implementation: Stage III System application in operational enhancement (June 2008 onwards), 80–1
workflow of garment production process in the company, 76–7
lessons learned from managerial level, 92–3
LL3 – clear objectives and expectations, 92
LL4 – obtain users’ acceptance, 93
LL5 – adopt appropriate policy and structural changes, 93
LL6 – obtain top management support, 93
lessons learned from operational level, 93–5
LL7 – conduct periodic operational review meetings, 93
LL8 – start with a trial run, 94
LL9 – keep the operation simple and provide adequate training to users, 94
LL10 – errors may arise from hardware failure, human error, inappropriate procedures, 94
LL11 – system inter-operability, 94
lessons learned from strategic level, 92
LL1 – choose the right partner, 92
LL2 – identify the benefits, 92
lessons learned from the case study, 90–5
list of LL from the RFID-based system implementation, 91
role of RFID technology, 70–96
using RFID technology in garment manufacture, 74–6
GetArticle, 149
H
hand-held scanning system, 166
handheld reader, 7
hard benefits, 92
Hennes & Mauritz (H&M), 208
hybrid reader, 7
I
Identification Friend or Foe (IFF) system, 3
IF-THEN rule, 171
Industrial Engineering Execution Systems (IEES), 80
inflated transport distances, 136
initial operative efficiencies
influence, 119
optimized operation assignment and task proportions, 120
optimized results of production control, 121
intelligent apparel product cross-selling
radio frequency identification (RFID) technologies, 159–84
assessing role of RFID in fashion retailing, 181–2
evaluation of RFID-enabled SDS, 178–81
fashion mix and match decision-making process of fashion designers, 162
implementation of RFID-enabled SDS and IPCS, 174–7
intelligent product cross-selling system (IPCS), 166–74
smart dressing system (SDS), 163–6
intelligent decision support system, 106–11
intelligent optimization algorithms, 102
attributes related to fashion mix and match and level of importance, 167
based on rule-based expert system and fuzzy screening technique, 168–72
evaluating AMSI of each attribute, 170
linguistic terms of fuzzy rating scales with fuzzy number representation, 169
overview of IPCS architecture, 167–8
illustration, 168
validation of IPCS for mix-and-match, 172–4
comparison between the result advised by IPCS system and evaluation result, 173
fashion matching results generated by IPCS system, 173
number of fashion items in each type and their potential matching pairs, 172
screening performance at screening levels of VG, G and F, 174
internal rates of return (IRR), 221
K
knowledge acquisition module, 170
L
learning-curve-based operative efficiency, 105
learning curve model, 101
linear barcode, 3
linguistic rating scale, 169
M
MainScreen, 149
Mango, 208
mathematical equations, 207
mathematical model, 104–5
methodology, 189–90
data collection, 189
event study methodology, 189–90
middleware, 8–9
application interface, 9
data processor and storage, 8
general architecture of component, 9
middleware management, 9
reader interface, 8
Miroglio Fast Fashion
case study, 207–10
current store operations records, 210
model testing, 216–18
modelling flexible assembly lines (FALs), 103–5
learning-curve-based operative efficiency, 105
illustration, 105
mathematical model, 104–5
notations, 103–4
modified bi-level genetic algorithm, 108–10
structure illustration, 109
Motivi, 208
Ms SQL Server, 107
multivariate sensitivity analyses, 217–18
MySQL, 107
N
‘need-pull’ (NP), 71
net present values (NPV), 221
never-out-of-stock (NOS) delivery, 132
North America, 190–3
notations, 103–4
O
Oltre, 208
operation routing rule, 110–11
operational cost, 223
Operations Management and Information Systems, 71
operative efficiency variability, 100–1
Oracle, 107
order allocation improvement
fashion supply chains using radio frequency identification (RFID) technologies, 126–54
benefits, 149–53
future trends, 153–4
garment allocation to fulfil customer orders, 139–47
order and delivery models of apparel products, 130–4
production and distribution in European apparel industry, 127–30
software support for garment allocation, 147–9
warehouse based vs flexible garment distribution forms, 134–8
order rate, 212
OrdWare, 147
P
passive tags, 6
passive ultra-high frequency, 209
point-of-sales (POS) data, 178
polynomial curve line model, 215
polynomial demand model, 215
post-ordered volumes, 132
pre-ordered volumes, 132
process management and product tracking improvement
case studies in the use of RFID technology, 52–64
advantages of RFID technology highlighted by case studies in the FTSC, 61
advantages of RFID technology in FTSC operations, 54–60
case study profile, 54
main drivers of RFID in the case studies, 62–3
main drivers of RFID technology in FTSC operations, 60–4
RFID deployment by case studies in their operations, 60
RFID deployment in fashion and textile SC, 55–9
RFID technology deployment in FTSC, 53–4
summary of the case studies, 53
RFID in supply chains, 44–6
RFID technology in practice, 51–2
RFID use in fashion and textile supply chain, 46–51
fashion and textiles supply chain, 47
operations supported by RFID across the FTSC, 49
role of RFID technology, 42–65
product novelty, 208
production control decision-making, 101–2
production control decision support (PCDS) system
optimized operation assignment and task proportions of case 2 of four experiments, 122
optimized production control results of case 2 of four experiments, 123
testing effectiveness, 111–21
experiments, 112–19
influence of different initial operative efficiencies, 119
R
radio frequency identification (RFID) technology, 4–9
applications in fashion supply chain, 9–11
architecture of an RFID system, 5
from barcode to, 2–4
barcode vs, 4
benefits, 149–53
clothing and textiles RFID adopters vs other adopters, 194–6
difference between apparel and textiles RFID adopters, 195–6
list of apparel and textiles firms, 193
distribution and retail operations improvement, 13–39
process reengineering, 15–28
profitability analysis, 28–33
RFID Fashion Pilot Project, 33–9
evaluation of RFID-enabled SDS, 178–81
comparison of total sales with sales brought by installation of RFID-based IPCS, 178
proportion of total sales brought by RFID-based IPCS, 179
trend of sales quantities caused by two fitting rooms and dressing mirror, 180
garment allocation to fulfil customer orders, 139–47
allocation of garment package to customer order, 144
coding of relevant article and package related data on article and package transponders, 143
decreasing of range of potential target destinations during transport, 146
direct delivery processes, 141–2
process steps of conventional as well as modified process chains for garment delivery, 140
impact assessment, 218–21
implementation of RFID-enabled SDS and IPCS, 174–7
client interfaces of IPCS system, 175
mix-and-match recommendations in fitting room by SDS, 176
movable smart dressing mirror, 177
smart fitting room and smart dressing mirror, 177
industry distribution of RFID adoption in North America, 190–3
distribution by industry, 192
distribution by year, 192
distribution of 410 RFID announcements by industry, 191
intelligent apparel product cross-selling, 159–84
assessing role of RFID in fashion retailing, 181–2
intelligent product cross-selling system (IPCS), 166–74
smart dressing system (SDS), 163–6
measuring the impact in improving efficiency of textile supply chain, 187–201
middleware, 8–9
general architecture of middleware component, 9
modeling the effectiveness in improving sales performance in fashion retail outlets, 203–24
future trends, 222–4
Miroglio Fast Fashion case study, 207–10
model testing, 216–18
retail store operations, 206–7
store operations at Miroglio, 210–16
order allocation improvement in fashion supply chains, 126–54
future trends, 153–4
order and delivery models of apparel products, 130–4
production and distribution in European apparel industry, 127–30
software support for garment allocation, 147–9
process management and product tracking improvement, 42–65
case studies in the use of RFID technology, 52–64
RFID in supply chains, 44–6
RFID technology in practice, 51–2
RFID use in fashion and textile supply chain, 46–51
return on investment assessment from RFID technology, 221–2
under the boosting economy scenario, 223
under the stagnating economy scenario, 222
RFID reader, 7
RFID tags, 5–6
role in garment manufacturing operations improvement, 70–96
assessing the effectiveness of RFID technology, 81–2
business value of RFID, 71–4
business value of RFID-based manufacturing process management system, 82–6
factors in successful implementation of RFID-based manufacturing process management system, 86–90
implementing RFID-based manufacturing process management system, 76–81
lessons learned from the case study, 90–5
using RFID technology in garment manufacture, 74–6
role in improving garment assembly line operations, 99–123
generic architecture for production control decision-making, 100
intelligent decision support system for production control on FALs, 106–11
key issues in developing flexible assembly lines (FALs), 100–2
modelling flexible assembly lines (FALs), 103–5
testing effectiveness of intelligent PCDS system, 111–21
role in textiles and fashion supply chain, 1–11
tag and reader communication, 7–8
high frequency, 8
low frequency, 7–8
ultrahigh frequency, 8
value assessment, 205–6
warehouse based vs flexible garment distribution forms, 134–8
customer-specific order picking for warehouse based delivery and for direct delivery, 137
redirection of articles due to defaulting retailers or re-allocations of articles, 138
ready-made garments, 135
retail store operations modelling, 206–7
retailer orders, 136
return on assets (ROA), 194
return on investment assessment, 221–2
return on sales (ROS), 194
revenue growth, 205–6
RFID Fashion Pilot Project, 33–9
RFID reader, 7
RFID tag, 5–6
antenna, 5–6
integrated circuit, 6
printed circuit board, 6
robustness tests, 216
S
sales forecast, 214
sales over assets (SOA), 194
sales performance improvement
modelling the effectiveness of RFID technologies in fashion retail outlets, 203–24
future trends, 222–4
impact assessment, 218–21
Miroglio Fast Fashion case study, 207–10
model testing, 216–18
retail store operations, 206–7
return on investment assessment from RFID technology, 221–2
store operations at Miroglio, 210–16
value assessment, 205–6
sales variable, 214
service level agreements (SLA), 132
sharp work load peaks, 135
Shortest Processing Time (SPT), 145
simulated behaviour, 216
simulation-based techniques, 102
simulation phased methodology, 206–7
small and medium enterprises (SMEs), 190–1
smart dressing system (SDS), 163–6
smart fitting room system, 166
smart mirror system, 166
soft benefits, 92
software support
garment allocation, 147–9
SQL server, 174
staff assisted sales time, 214
staff training, 179
Standard Industrial Classifications (SIC), 189
stochastic noise, 214
stock-keeping units (SKU), 129
Stock Management Structure model, 207
storage space problems, 135
store opening, 214
store operations modelling
Miroglio, 210–16
causal loop diagram, 210–11
SD model, 211–16
supply chain, 1
supply chain management (SCM), 1
supply chain performance
difference between clothing and textiles adopters, 197
cumulative abnormal changes in inventory days, account receivable days, 198
manufacturing sample firms including apparel and textiles, 199–200
subsamples of other RFID adopters, 201
whole sample, 199
system architecture, 106–8
portion of Miroglio’s fast fashion store operations, 213
scatter plot and fourth-order polynomial fitted curve line of weekly sales, 215
T
‘technology-push’ (TP), 71
textile supply chain
efficiency improvement and measuring the impact of RFID technologies, 187–201
clothing and textiles RFID adopters vs other adopters, 194–6
industry distribution of RFID adoption in North America, 190–3
methodology, 189–90
supply chain performance difference between clothing and textiles adopters, 197
time index, 215
tracking points, 34
transponders, 151–2
two-dimensional (2D) barcode, 3
U
ultra-high frequency (UHF), 164–5
Universal Product Code (UPC), 2
V
vehicle-mount reader, 7
Vensim DSS, 217–18
Voluntary Interindustry Commerce Solutions (VICS), 44
W
warehouse-based delivery, 137–8
weekly sales, 215
Weighted Average Cost of Capital (WACC) method, 221
Wilcoxon signed-rank (WSR), 190
Wright curve, 101
Z
Zara-Inditex, 208
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