Logistics Network Configuration Designing & Managing the Supply Chain Chapter 2 Byung-Hyun Ha [email protected].

Logistics Network Configuration

Designing & Managing the Supply Chain

Chapter 2

Byung-Hyun Ha

[email protected]

mailto:[email protected]

Outline

Case: Bis Corporation

What is Logistics Network Configuration?

Methodology Data Collection Modeling and Validation

Solution Techniques

Features of Network Configuration DSS

Summary


Background Produce & distribute soft drinks 2 manufacturing plant 120,000 account (retailers and stores), all over the US 3 existing warehouse (Chicago, Dallas, Sacramento) 20% gross margin $1,000 for each SKU (stock-keeping unit) for all products

Current distribution strategy (designed 15 years ago) Produce and store at the manufacturing plant Pick, load, and ship to a warehouse/distribution center Unload and store at the warehouse Pick, load, and deliver to store


You, consulting company Proposal as reengineering the sales and distribution functions First phase, identifying 10,000 direct delivery account, based on

• Dock receiving capabilities

• Storage capability

• Receiving methodologies

• Merchandising requirements

• Order-generation capabilities

• Delivery time window constraints

• Current pricing

• Promotional activity patterns


Redesign distribution network Grouped accounts into 250 zones, products into 5 families Data collected

• Demand in 1997 by SKU per product family for each zone

• Annual production capacity at each manufacturing plant

• Maximum capacity for each warehouse, new and existing

• Transportation costs per product family per mile for distributing

• Setup cost for establishing a warehouse

Customer service level requirement No more than 48 hours in delivery

Additionally, Estimated yearly growth, variable production cost, cost for

increasing production capacity, …


Issues How can Bis Corporation validate the model? Impact of aggregating customers and products Number of established distribution centers and their locations Allocation of plant’s output between warehouses When and where should production capacity be expanded?

Introduction

Issues of this chapter Development of a model representing logistics network Validation of the model Aggregation of customers and products and accuracy of the

model Number of distribution centers to be established Location of distribution centers Allocation of output of each product in plants among distribution

centers Decision about whether, when, and where to expand production

capacity

Introduction

Components of logistics network Facilities

• Suppliers, warehouse, distribution centers, retail outlets

Flows• Raw material, work-in-process inventory, finished products

Supply

Sources:plantsvendorsports

RegionalWarehouses:stocking points

Field Warehouses:stockingpoints

Customers,demandcenterssinks

Production/purchase costs

Inventory &warehousing costs

Transportation costs

Inventory &warehousing costs

Transportation costs

Network Design

Strategic level – decisions that typically involve major capital investments and have a long term effect Number, location and size of new plants, distribution centers and

warehouses Acquisition of new production equipment and the design of

working centers within each plant Design of transportation facilities, communications equipment,

data processing means, …

Tactical level Determine optimal sourcing strategy (strategic?)

• Which plant/vendor should produce which product Determine best distribution channels (strategic?)

• Which warehouses should service which customers Selection of transportation mode (e.g. rail, truck)

Network Design

Network design or reconfigure problem Objective

• Minimize annual system-wide costs

Subject to• Variety of service level requirements

The objective is to balance service level against Production/purchasing costs Inventory carrying costs Facility costs (handling and fixed costs) Transportation costs

Network Design

Tradeoffs

$-

$10

$20

$30

$40

$50

$60

$70

$80

$90

0 2 4 6 8 10

Number of Warehouses

Co

st (

mil

lio

ns

$)

Total Cost

Transportation Cost

Fixed Cost

Inventory Cost

Network Design

Increasing number of warehouse typically yields improvement in service level increase in inventory cost increase in overhead and setup cost reduction in outbound transportation costs increase in inbound transportation costs

Network Design

Sources: CLM 1999, Herbert W. Davis & Co; LogicTools

3 14 25

Pharmaceuticals Food Companies Chemicals

- High margin product- Service not important (or easy to ship express)- Inventory expensiverelative to transportation

- Low margin product- Service very important- Outbound transportationexpensive relative to inbound

Industry benchmarks: average # of warehouses

Outline



Methodology Data Collection and Aggregation Modeling and Validation

Solution Techniques


Summary

Data Collection

Data for network design Location of customers, stocking points and sources A listing of all products (volumes, transportation modes) Demand for each product by customer location Transportation rates Warehousing costs Shipment sizes by product Order patterns by frequency, size, season, content Order processing costs Customer service requirement and goals

Data Aggregation

Optimization model for the problem? Typical soft drink distribution system: 10,000~20,000 accounts Wal-Mart or JC Penney: hundreds of thousands! Too much

Data aggregation Customer aggregation Product aggregation

Why? Cost of obtaining and processing data Form in which data is available Size of the resulting location model Accuracy of forecast demand

Data Aggregation: Customer

Customer aggregation Aggregating customers located in close proximity

• Using a grid network or clustering techniques

All customers within a single zone• Replaced by a single customer located at the centroid of the zone

Aggregation by classes• Service levels, frequency of delivery, …

Customer zone balances accuracy loss due to over aggregation needless complexity

Data Aggregation: Customer

Experimental results: cost difference < 0.05% Considering transportation costs only Customer data

• Original data had 18,000 ship-to locations

• Aggregated data had 800 ship-to locations

• Total demand was the same in both cases

Total Cost:$5,796,000 Total Customers: 18,000 Total Cost:$5,793,000 Total Customers: 800

Data Aggregation: Product

Product aggregation Hundreds to thousands of individual items in production line

• Variations in product models and style• Same products are packaged in many sizes

Collecting all data and analyzing it is impractical

Aggregation by distribution pattern Place SKU’s into a source group

• A source group is a group of SKU’s all sourced from the same place to the same customers

Aggregate SKU’s by similar logistics characteristics• Weight, volume, holding cost, …

Aggregation by product type Different products might simply be variations in product style or

differ only in type of packaging


Aggregation by distribution pattern

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

0.000 0.010 0.020 0.030 0.040 0.050 0.060 0.070 0.080 0.090 0.100

Volume (pallets per case)

We

igh

t (l

bs

pe

r c

as

e)


Test case for product aggregation 5 plants 25 potential warehouse locations Distance-based service constraints Inventory holding costs Fixed warehouse costs Product aggregation

• 46 original products

• 4 aggregated products

• Aggregated products were created using weighted averages


Experimental results: cost difference < 0.05%

Total Cost:$104,564,000Total Products: 46

Total Cost:$104,599,000Total Products: 4

Data Aggregation

Recommended approach Aggregate demand points for 150 to 200 zones

• e.g. if customers are classified into classes according to their service levels or frequency of delivery, each class will have 150-200 aggregated points

Make sure each zone has an equal amount of total demand• Zone may be different geographic size

Place the aggregated point at the center of the zone Aggregate products into 20 to 50 product groups

In this case, the error is typically no more than 1%

Variability reduction Even if technology exists to solve problem with original data,

forecast customer demand at account and product level is usually poor

Impact of Aggregation on Variability

Measure of variability? Standard deviation (SD)

• Enough?

Which one has bigger SD than the other?

n

XXSD

n

XX ii

2

2 )(

0

15

30

0

200

400


Measure of variability Coefficient of variation

CVA CVB

X

SDCV

0

15

30

0

200

400

A B


Historical data for the two customers

Summary of historical data

Year 1992 1993 1994 1995 1996 1997 1998

Customer 1 22,346 28,549 19,567 25,457 31,986 21,897 19,854

Customer 2 17,835 21,765 19,875 24,346 22,876 14,653 24,987

Total 40,181 50,314 39,442 49,803 54,862 36,550 44,841

Average Standard deviation CoefficientStatistics annual demand annual demand of variation

Customer 1 24,237 4,658 0.192

Customer 2 20,905 3,427 0.173

Total 45,142 6,757 0.150

Transportation Rates

Constructing effective distribution network model We should consider reasonable transportation rates

Important characteristics of most rates Rates are almost linear with distance but not with volume

Rates of internal fleet Transportation cost for company-owned trucks Calculation of cost per mile per SKU

• Annual costs per truck, annual mileage per truck, annual amount delivered, truck’s effective capacity

Rate of external fleet Distinguish between truckload (TL) and less than truckload (LTL)


TL carriers Subdivision of country into zones Zone-to-zone table for cost Cost structure is not symmetric (why?)

• e.g. Shipping Illinois NY is more expensive than in reverse way

LTL industry Types of freight rates

• Class rate (standard)

• Classification tariff based on density, ease of handling, liability for damage

• Rate base number based on distance

• Exception rate

• Less expensive rate

• Commodity rate


Mileage estimation Straight line distance Dab in US from a to b

• Let lonx and latx be longitude of x and latitude of x

For long distances by correcting for earth’s curvature

22 )()(69 babaab latlatlonlonD

22

1

2sin)cos()cos(

2sin

sin)69(2

baba

ba

ab

lonlonlatlat

latlat

D

Warehouse Costs

Three main components Handling costs

• Labor costs, utility costs

• Fairly can be estimated

Fixed costs• Cost components that are not proportional to the amount of material

the flows through the warehouse

• Typically proportional to warehouse size (but not linear way)

Storage costs• Inventory holding cost that are proportional to average positive

inventory level

warehouse size

fixed

co

stInventory turnover ratio =

average inventory level

annual sales

Warehouse Capacities

Capacity estimation Calculating peak level by assuming regular shipment and deliver

y – twice average inventory level

Space for access and handling• For aisles, picking, sorting, processing facilities, AGVs, …

• Represented as a factor (>1)

ordersize

inve

ntor

yle

vel

time

average

Other Issues for Data Collection

Potential warehouse locations Geographical and infrastructure conditions Natural resources and labor availability Local industry and tax regulations Public interest

Service level requirements e.g. 95% of customers be situated within 200 miles of the

warehouses serving them

Future demand Network design is at strategic level and impacts on next 3~5

years Using scenario-based approach incorporating net present value

Other Issues for Data Collection

Example of scenario-based approach Determine demand and marketing cost of new product

Test market

Don’t testmarket

.60High local demand

.40Low local demand

Don’t marketnationally

Marketnationally

.85High demand

.15Low demand

.90Low demand

.10High demand

Marketnationally


.55High demand

.45Low demand

Marketnationally


Outline



Methodology Data Collection and Aggregation Modeling and Validation

Solution Techniques


Summary

Model and Data Validation

Model?

Data validation Ensuring data and model accurately reflect the network design

problem Done by reconstructing the existing network configuration using

the model and collected data comparing the output of the model to company’s accounting information

Can identify errors in the data, problematic assumptions, modeling flaws, …

• e.g. transportation cost estimated by model consistently underestimating actual cost become to find that effective truck capacity was only about 30%

Thus, validation process not only help calibrate parameters but also suggest potential improvement of existing network

Model and Data Validation

Sensitivity analysis Make local and small changes in model, and estimate impact on

costs and service level• Positing a variety of what-if question

• e.g. closing the existing warehouse, changing flow of materials

Can have good intuition about what the effect of small-scale changes

Can identify errors in model

In summary, model validation process involves answering the following questions: Does the model make sense? Are the data consistent? Can the model results be fully explained? Did you perform sensitivity analysis?

Solution Techniques

Techniques for optimizing configuration of logistics network Mathematical optimization techniques

• Exact algorithms: find optimal solutions

• Heuristics: find good solutions, not necessarily optimal

Simulation models• provide a mechanism to evaluate specified design alternatives

created by the designer

Heuristics and Exact Algorithms

E.g. a distribution system Single product Two plants p1 and p2 Plant p2 has an annual capacity of 60,000 units The two plants have the same production costs There are two warehouses w1 and w2 with identical warehouse

handling costs. There are three markets areas c1, c2 and c3 with demands of

50,000, 100,000 and 50,000, respectively Distribution cost per unit

Facilitywarehouse p1 p2 c1 c2 c3

w1 0 4 3 4 5

w2 5 2 2 1 2


A distribution system

D = 50,000

D = 100,000

D = 50,000

Cap = 60,000

$4

$5

$2

$3

$4

$5

$2

$1

$2

Production costs are the same, warehousing costs are the same

$0


Heuristic 1 For each market, choose the cheapest warehouse to source

demand. Then, for every warehouse, choose the cheapest plant.

D = 50,000

D = 100,000

D = 50,000

Cap = 60,000

$5 x 140,000

$2 x 60,000

$2 x 50,000

$1 x 100,000

$2 x 50,000

Total Costs = $1,120,000


Heuristic 2 For each market area, choose the warehouse such that the total

delivery costs to the warehouse and from the warehouse to the market is the smallest. (i.e. consider inbound and outbound costs)

D = 50,000

D = 100,000

D = 50,000

Cap = 60,000

$4

$5

$2

$3

$4

$5

$2

$1

$2

$0

P1 to WH1 $3P1 to WH2 $7P2 to WH1 $7P2 to WH 2 $4




Heuristic 2 For each market area, choose the warehouse such that the total

delivery costs to the warehouse and from the warehouse to the market is the smallest. (i.e. consider inbound and outbound costs)

D = 50,000

D = 100,000

D = 50,000

Cap = 60,000

$5 x 90,000

$2 x 60,000

$3 x 50,000

$1 x 100,000

$2 x 50,000

$0 x 50,000




Total Cost = $920,000


Exact algorithm (linear programming) xij: the flow from i to j

jix

xx

xx

xx

xxxxx

xxxxx

xx

xxxxxx

xxxx

ij

cwcw

cwcw

cwcw

cwcwcwwpwp

cwcwcwwpwp

wpwp

cwcwcwcwcwcw

wpwpwpwp

,0

000,50

000,100

000,50

000,60s.t.

22543

2450.min

3231

2221

1211

3222122221

3121111211

2212

322212312111

22122111

Total Cost = $740,000


Network configuration problem is generally formulated as integer programming Hard to obtain the optimal solution

Some typical types of network design model Uncapacitated facility location model Capacitated facility location model Network optimization model

Source: Camm et al. 1997


Uncapacitated facility location model Example

• Which DC will open and which customer zone will assign to which DC?

• cij: total cost of satisfying customer zone j demand from DC i

• k: number of DCs allowed

• I: index set of DCs

• J: index set of customer zones

• xij = 1 if customer zone j isassigned to DC i, 0 if not

• yi = 1 if DC i opens, 0 if not

JjIiyx

ky

JjIiyx

Jjx

xc

iij

Iii

iij

Iiij

Ii Jjijij

,}1,0{,

,

1s.t.

.min

Source: Camm et al. 1997


Capacitated plant location model Example: SunOil, a global energy company

• The world is divided into 5 different regions: N. America, S. America, Europe, Asia, Africa

• SunOil has regional demand figures, transport costs, facility costs and capacities

• We will ignore tariffs and exchange rate fluctuations for now, and assume all demand must be met (so we can focus on minimizing costs)

Question:• Where to locate facilities to service their demand

• What size to build in the region (small or large), should they locate a facility there

Source: Chopra and Meindl 2004


Capacitated plant location model n: number of potential plant location

• As we are considering two different type plants (small, large) for each region, n = 10

m: number of markets Dj: demand from market j

Ki: capacity of plant i

fi: fixed cost of keeping plant i open

cij: variable cost of sourcing market j from plant i

yi = 1 if plant is located at site i, = 0 otherwise

xij: quantity shipped from plant i to market j

niy

niyKx

mjDx

ts

xcyf

i

ii

m

jij

j

n

iij

n

i

m

jijij

n

iii

,,1for}1,0{

,,1for

,,1for

..

min

1

1

1 11


Network optimization model Example: TelecomOne merged with High Optic

• They have plants in different cities and service several regions

• Supply cities

• Baltimore (capacity 18K), Cheyenne (24K), Salt Lake City (27K), Memphis (22K) and Wichita (31K)

• Monthly regional demands

• Atlanta (demand 10K), Boston (6K), Chicago (14K), Denver (6K), Omaha (7K)

• They will consider consolidating facilities

Source: Chopra and Meindl 2004


Network optimization model n: number of plant location m: number of markets Dj: demand from market j

Ki: capacity of plant i

cij: variable cost of sourcing market j from plant i

xij: quantity shipped from plant i to market j

0

,,1for

,,1for

..

min

1

1

1 1

ij

i

m

jij

j

n

iij

n

i

m

jijij

x

niKx

mjDx

ts

xc


Assignment #3 Build an MIP model and solve it for the following problem using solver (either CP

LEX or LINDO). Submit the model, code, and solution in printed form. DryIce Inc. is a manufacturer of air conditioners that has seen its demand grow si

gnificantly. They anticipate nationwide demand for the year 2010 to be 180,000 units in the South, 120,000 units in the Midwest, 110,000 units in the East, and 100,000 units in the West. Mangers at DryIce are designing the manufacturing network and have selected four potential sites – New York, Atlanta, Chicago, and San Diego. Plants could have a capacity of either 200,000 or 400,000 units. The annual fixed costs at the four locations are shown in the table below, along with the cost of producing and shipping an air conditioner to each of the four markets. Where should DryIce build its factories and how large should they be?

New York Atlanta Chicago San Diago

Annualfixed cost

200,000 plant $6 million $5.5 million $5.6 million $6.1 million

400,000 plant $10 million $9.2 million $9.3 million $10.2 million

Production &transportationcost

East $211 $232 $238 $299

South $232 $212 $230 $280

Midwest $240 $230 $215 $270

West $300 $280 $270 $225

Simulation Models

Limitation of mathematical optimization technique Only dealing with static models – cost and demand do not

change over time

Simulation-based tools Taking into account the dynamics of system Being capable of characterizing system performance for a given

design

Simulation for micro-level analysis including individual ordering pattern specific inventory policy inventory movement inside warehouses

Simulation Models

Limitation of simulation Only evaluate costs associated with a pre-specified logistics

network design That is, simulation is not an optimization tool

• Not useful in determining an effective configuration from a large set of potential configurations

Some ways to use simulation for optimization• Employing search technique of determining good parameter for

simulation model

Two-stage approach1. Use optimization model to generate a number of least-cost

solution at macro-level

2. Use simulation model to evaluate solutions generated in the first phase


Flexibility Ability of system to incorporate a large set of preexisting network

characteristics One of key requirements of decision-support system (DSS) for

network design

Necessary to incorporate the following features Customer-specific service level requirement Existing warehouses (if lease have not expired, it cannot close) Expansion of existing warehouses Specific flow patterns should not be changed Warehouse-to-warehouse flow Bill of materials (BOM) (e.g. final assembly is done at a certain

warehouse)


Robustness of DSS Capability to deal with all issues with little or no reduction in its

effectiveness That is, relative quality of the solution generated by DSS should

be independent of specific environment, variability of data, or particular setting

Reasonable running time of DSS Also have to be robust

Summary

Issues important in design of logistics network Data collection, validation, solution techniques

Aggregation of data Problem size Forecast accuracy

Optimization-based decision-support system Considers complex transportation cost structure, warehouse

size, manufacturing limitations, inventory turnover ratios, inventory cost, service level

Can solve large-scale problem efficiently

Assignment #4

Discussion questions 3, 7 (pp. 41–42)

Logistics Network Configuration Designing & Managing the Supply Chain Chapter 2 Byung-Hyun Ha [email protected].

Documents

network design network

production capacity

network design sources

truck slide

network design tradeoffs

costs facility costs

outbound transportation

inbound transportation