Index
A
accounting data, analyzing to categorize fixed and variable costs, 134-135
accuracy
achievable frontier, 213
Active Pharamceutical Ingredient (API), 220
adding
products, 201
variable and fixed costs, facilities, 135
of cost types, 258
validating strategies, national examples, 242-244
validating strategies, regional examples, 244-249
of data, 266
location, 240
packaging requirements, 253
per-unit production costs, 254
predefined product families, 254
production requirements, 253
products that share components or raw materials, 253
products that share transportation requirements, 254
removing products with low volumes, 251-252
of sites, 256
reasons for using, 238
analyzing shipment data, 145-146
API (Active Pharamceutical Ingredient), 220
due diligence and decision making, 226-227
fixing feasible models, 231-232
fixing constraints of optimization, 233
fixing data of optimization, 234
fixing decisions of optimization, 233
fixing objectives of optimization, 232-233
fixing infeasible models, 229-231
including things in models that don’t exist in the actual supply chain, 225-226
optimization, 227
running lots of scenarios, 224-225
separating the important from the trivial, 222-223
assembly sites, 11
B
baseline mode, converting to optimization model, 141
baselines, 139
actual baselines, 139-141, 154
development and validation, 266-267
Illinois Quality Parts, Inc., 144-145
analyzing shipment data and creating customers and demand, 145-146
baseline model results, 149-151
building optimized baselines, 151-154
modeling historical as predefined flows, 146-147
modeling transporation in baselines, 147-148
modeling transportation in, 147-148
optimized baselines, 139-143, 154
beer manufacturing process modeling, 198-200
BOMs, 197
beer manufacturing process modeling, 198-200
bottleneck process, 197
branding nonquantifiable data, 18
buffering lead time, warehouses, 7
business intelligence systems, 12
C
candy bar supply chain, 3
capacity, fixed costs (facilities), 133
capacity modeling, 83
weighted average distance problems, 87-89
carbon emissions, geography, 6
categorization of SKUs, 251
categorizing fixed and variable costs by analyzing accounting data, 134-135
caves, 10
center of gravity models, 23
center of gravity problems, 23-24
outbound transportation costs, 101-102, 118-120
multistop costs, estimating, 113-118
physics weighted-average centering, 24-31
practical center of gravity, 31, 33-34
central warehouses, 9
co-manufacturers, 11
co-packers, 11
combinations, permutations versus, 44-48
commercial truckload (TL), 104
competitors, non-quantifiable data, 18
components, 196
computational reduction, 91-93
computing weighted-average positions, 29
conservation of flow, 183
consolidating products, warehouses, 7
constraints, 13
capacity modeling, 86-90, 93-96
defined, 49
distance-based facility location problem, 50-51
constraints of optimization, fixing, 233
consulting firms, 272
consumer products companies, schematic of supply chains, 219
contract manufacturers, 11
converging baseline model to optimization model, 141
cost types, aggregation, 258
costs
per-unit production costs, aggregation, 254
inbound, 100
by mode of transportation, 104-107
cross-dock, 8
customer-service level, 208
customers
validating strategies, national examples, 242-244
validating strategies, regional examples, 244-249
determining warehouse locations with fixed customers, 160-164
D
data, 13
defined, 48
demand data, 16
organizational challenges, 19-21
precision versus significance, 14-17
transportation costs, 16
data aggregation, 266
data analysis, 264
data cleansing, 264
data of optimization, fixing, 234
data validation, 265
decision making, art of modeling, 226-227
decision variables, 13
decisions, 13
defined, 49
decisions of optimization, fixing, 233
dedicated fleet, 104
demand
defined, 40
outbound transportation costs, 102-103
units of measure, 41
demand data, 16
differing service-level requirements, product modeling, 179
dimensions of products, impact on transportation, 185-187
disruption costs, 18
distance-based facility location problems, 38-44, 49-52
distribution centers, 8
distribution network analysis, outbound flow, 187-191
dual variables, 77
E
economies of scale, plants, 10
estimating outbound transportation costs, 109-111
evaluating supply chain network design, 3-5
Excel formulation, distance-based facility location problems, 53-56
exponential function, 48
F
facilities
adding variable and fixed costs, 135
labor costs, 132
material costs, 132
utility costs, 132
mathematical formulations, 129-130
facility-location problems, distance-based, 38-44, 49-52
capacity modeling, 87-90, 93-96
factorial function, 48
FCNF problems, 92
finance teams, 20
Fixed Charge Network Flow problems, 92
fixed costs, facilities, 127-128, 132-134
categorizing by analyzing accounting data, 134-135
fixing
fixing constraints of optimizations, 233
fixing data of optimizations, 234
fixing decisions of optimizations, 233
fixing objectives of optimizations, 232-233
forward warehouses, 9
full truckload (FTL), 104
G
geocoding, 40
carbon emissions, 6
labor, 6
risk, 6
service level, 6
skills, 6
taxes, 6
transportation costs, 5
utilities, 6
H
hub-and-spoke networks, 172-174
hub warehouses, 9
I
Illinois Quality Parts, Inc., 144-145
analyzing shipment data nad creating customers and demand, 145-146
baseline model results, 149-151
building optimized baselines, 151-154
modeling historic as predefined flows, 146-147
modeling transportation in baselines, 147-148
inbound transportation costs, 100
incremental savings, 190
infeasible solutions
ingredient sourcing constraints, 197
ingredients, 196
intermodal transport, 106
inventory pre-build, warehouses, 8
J
Jade, Walter, 157
JADE Paint and Covering, 157-160
three-echelon supply chains, 166-171
JPMS Chemical Pvt. Ltd., 277-288
state-based single-sourcing, 290-293
K
knapsack problems, 90
L
labor, geography, 6
labor costs, facilities, 132
less-than-truckload (LTL), 105, 161
linear programming, sensitivity analysis, 77
linking locations with multi-echelon supply chains, 172-174
location
determining warehouse locations with fixed plants and customers, 160-164
plants, source of raw materials, 171
locations
aggregation, 240
linking with multi-echelon supply chains, 172-174
logical supply chain network model, optimization, 12
logistics teams, 19
LTL (less-than-truckload), 161
M
manufacturing capacity modeling, 85-86
constraints, 87
manufacturing sites, 11
material costs, facilities, 132
materials, geography, 6
mathematical formulations
facilities, variable costs, 129-130
multi-echelon supply chains, 164-166
measurement units for demand, 41
measuring service levels, 64-65
mixing centers, 8
modeling
art of. See art of modeling
BOMs (bills-of-material), 197
beer manufacturing process modeling, 198-200
historical as predefined flows, 146-147
differing service-level requirements, 179
mathematical formulation, 180-184
Value Grocers. See Value Grocers
variations in logistics characteristics, 178-179
transportation in baselines, 147-148
modeling groups, setting up, 272-274
models
fixing
multi-echelon supply chains
linking locations together, 172-174
mathematical formulations, 164-166
multi-objective optimization, 207-214
multistop costs, estimating, 113-118
multistop transport, 106
N
network design study, steps to completing, 261
step 1: model scoping and data collection, 262-264
step 2: data analysis and validation, 264-266
step 3: baseline development and validation, 266-267
step 4: what-if scenario analysis, 268-269
step 5: final conclusion and development of recommendations, 269-270
organizational challenges, 19-21
NP-Hard problems, 52
O
objective function, service-level analysis, 71-74
objective functions, distance-based facility location problem, 50
objectives, 13
defined, 49
ocean transport, 106
operations teams, 19
fixing
constraints, 233
data, 234
decisions, 233
logical supply chain network model, 12
optimization model, converting to, 141
optimized baselines, 139, 142-143, 154
organizational challenges, non-quantifiable data, 19-21
outbound flow, distribution network analysis, 187-191
outbound transportation costs, 100-102, 118-120
multi-stop costs, estimating, 113-118
P
packaging requirements, aggregation, 253
parcel transport, 105
Pareto optimal solutions, 209-214
per unit cost, outbound transportation, 103-109
per-unit production costs, aggregation, 254
permutations, combinations versus, 44-48
pharmaceutical companies, supply chain schematics, 220
physics weighted-average centering, center of gravity problems, 24-31
plant-attached warehouses, 9
plant capacity modeling. See manufacturing capacity modeling
plant locations, source of raw materials, 171
plants, 11
determining warehouse locations with fixed plants, 160-164
economies of scale, 10
production processes, 11
reasons for having multiple plants, 10
service levels, 10
taxes, 11
transportation costs, 10
practical center of gravity problems, 31-34
distance-based location. See distance-based facility location problems
outbound transportation costs, 101-102, 118-120
multi-stop costs, estimating, 113-118
precision, accuracy versus, 102-103
predefined flows, modeling historical as, 146-147
predefined product families, aggregation, 254
private fleet, 104
BOMs, 197
beer manufacturing process modeling, 198-200
differing service-level requirements, 179
mathematical fomulations, 180-184
distribution network analysis based on outbound flow, 187-191
impact of product dimensions on transportation, 185-187
storage restrictions for temperature-controlled products, 191-193
variations in logistics characteristics, 178-179
production lot sizes, warehouses, 7
production processes, plants, 11
production requirements, aggregation, 253
products
adding, 201
packaging requirements, 253
per-unit production costs, 254
predefined product families, 254
production requirements, 253
products that share components or raw materials, 253
products that share transportation requirements, 254
removing products with low volumes, 251-252
dimensions of, impact on transportation, 185-187
storage restrictions, temperature-controlled products, 191-193
Q
quantitative data, accuracy, 14-17
R
rail transport, 106
rate matrix, regression analysis, 112-113
raw materials, plant locations, 171
recommendations, development of, 269-270
regional supply, 278
regional warehouses, 9
reindustrialization, 208
removing products with low volumes, aggregation, 251-252
results, baseline models, 149-151
retailers, schematics of supply chains, 218
risk
geography, 6
nonquantifiable data, 18
S
sales teams, 19
scenarios, running lots of scenarios, 224-225
schematics of supply chains
for consumer products companies, 219
for pharmaceutical companies, 220
for typical retailers, 218
service level, geography, 6
service-level analysis, 65-67, 69
weighted average distance problems, 69-71
service-level requirements, product modeling, 179
plants, 10
warehouses, 7
shadow prices, 77
shipment data, analyzing, 145-146
site aggregation, 256
size of products, aggregation, 252-253
skills, geography, 6
SKUs, categorization of, 251
source of products, aggregation, 250-251
spokes, warehouses, 9
state-based single-sourcing, 290-293
steps to complete a network design study, 261
step 1: model scoping and data collection, 262-264
step 2: data analysis and validation, 264-266
step 3: baseline development and validation, 266-267
step 4: what-if scenario analysis, 268-269
step 5: final conclusion and development of recommendations, 269-270
storage restrictions for temperature-controlled products, 191-193
strategic network design, 207
suppliers, 11
supply chain network design, 1
art of modeling, including things in models that don’t exist in the actual supply chain, 225-226
multi-echelon supply chains, mathematical formulations, 164-166
schematics
for consumer products companies, 219
for pharmaceutical companies, 220
for typical retailers, 218
separating the important from the trivial, 222-223
three-echelon supply chains. See three-echelon supply chains
T
tablet supply chain, 3
tax rebates, non-quantifiable data, 18
taxes
geography, 6
plants, 11
teams
finance teams, 20
logistics teams, 19
operations teams, 19
sales teams, 19
temperature-controlled products, storage restrictions, 191-193
testing product aggregation strategies, 254-256
third-party manufacturing sites, 11
three-echelon supply chains, 157
determining warehouse locations with fixed plants and customers, 160-164
JADE Paint and Covering, 157-160, 166-171
time period aggregation, 257-258
TL (truckload), 161
toll manufacturers, 11
total cost versus upfront costs, 208
transportation
modeling in baselines, 147-148
products, aggregation, 254
raw materials, 171
transportation costs, 16, 99-101
geography, 5
inbound, 100
by mode of transportation, 104-107
multi-stop costs, estimating, 113-118
plants, 10
transportation mode trade-offs, warehouses, 8
truckload (TL), 161
truckload transport, 104
U
unions, 18
units of measure for demand, 41
upfront cost versus total cost, 208
utilities, geography, 6
utility costs, facilities, 132
V
validating
customer aggregation strategies
value of supply chain network design, 1-3
distribution network analysis based on outbound flow, 187-191
impact of product dimensions on transportation, 185-187
juices, 193
storage restrictions for temperature-controlled products, 191-193
variable costs, facilities, 127-132
categorizing by analyzing accounting data, 134-135
mathematical formulations, 129-130
variations in logistics characteristics, product modeling, 178-179
W-X-Y
warehouse capacity modeling, 84-85
constraints, 86
weighted average distance problems, 87-89
warehouse locations, determining with fixed plants and customers, 160-164
caves, 10
cross-dock, 8
distribution centers, 8
hubs, 9
inventory pre-build, 8
plant-attached warehouses, 9
production lot sizes, 7
regional warehouses, 9
service levels, 7
transportation mode trade-offs, 8
weighted average distance problems, 38-44, 49-52
weighted-average location, 31
weighted-average position, 30
computing, 29
what-if scenario analysis, 211-212, 268-269
Z
ZIP Codes, 240
zones, 173