Logistics Network Configuration

1
Logistics Network Configuration

• Designing Managing the Supply Chain
• Chapter 2
• Byung-Hyun Ha
• bhha_at_pusan.ac.kr

2
Outline

• Case Bis Corporation
• What is Logistics Network Configuration?
• Methodology
• Data Collection
• Modeling and Validation
• Solution Techniques
• Features of Network Configuration DSS
• Summary

3
Case Bis Corporation

• 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

4
Case Bis Corporation

• 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

5
Case Bis Corporation

• 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,

6
Case Bis Corporation

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

7
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

8
Introduction

• Components of logistics network
• Facilities
• Suppliers, warehouse, distribution centers,
  retail outlets
• Flows
• Raw material, work-in-process inventory, finished
  products

9
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)

10
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

11
Network Design

• Tradeoffs

12
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

13
Network Design

• Industry benchmarks average of warehouses

Food Companies
Chemicals
Pharmaceuticals
3
14
25
- High margin product - Service not important (or
easy to ship express) - Inventory
expensive relative to transportation
- Low margin product - Service very important -
Outbound transportation expensive relative to
inbound
Sources CLM 1999, Herbert W. Davis Co
LogicTools
14
Outline

• Case Bis Corporation
• What is Logistics Network Configuration?
• Methodology
• Data Collection and Aggregation
• Modeling and Validation
• Solution Techniques
• Features of Network Configuration DSS
• Summary

15
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

16
Data Aggregation

• Optimization model for the problem?
• Typical soft drink distribution system
  10,00020,000 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

17
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

18
Data Aggregation Customer

• Experimental results cost difference lt 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 Cost5,796,000 Total Customers 18,000
Total Cost5,793,000 Total Customers 800
19
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 SKUs into a source group
• A source group is a group of SKUs all sourced
  from the same place to the same customers
• Aggregate SKUs 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

20
Data Aggregation Product

• Aggregation by distribution pattern

21
Data Aggregation Product

• 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

22
Data Aggregation Product

• Experimental results cost difference lt 0.05

Total Cost104,564,000 Total Products 46
Total Cost104,599,000 Total Products 4
23
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

24
Impact of Aggregation on Variability

• Measure of variability?
• Standard deviation (SD)
• Enough?
• Which one has bigger SD than the other?

25
Impact of Aggregation on Variability

• Measure of variability
• Coefficient of variation
• CVA ? CVB

A
B
26
Impact of Aggregation on Variability

• 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 Coefficient
Statistics 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
27
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, trucks effective
  capacity
• Rate of external fleet
• Distinguish between truckload (TL) and less than
  truckload (LTL)

28
Transportation Rates

• 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

29
Transportation Rates

• 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 earths
  curvature

30
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

fixed cost
annual sales
Inventory turnover ratio
average inventory level
warehouse size
31
Warehouse Capacities

• Capacity estimation
• Calculating peak level by assuming regular
  shipment and delivery twice average inventory
  level
• Space for access and handling
• For aisles, picking, sorting, processing
  facilities, AGVs,
• Represented as a factor (gt1)

ordersize
inventorylevel
average
time
32
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 35 years
• Using scenario-based approach incorporating net
  present value

33
Other Issues for Data Collection

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

34
Outline

• Case Bis Corporation
• What is Logistics Network Configuration?
• Methodology
• Data Collection and Aggregation
• Modeling and Validation
• Solution Techniques
• Features of Network Configuration DSS
• Summary

35
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 companys
  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

36
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?

37
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

38
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

Facility
warehouse p1 p2 c1 c2 c3
w1 0 4 3 4 5
w2 5 2 2 1 2
39
Heuristics and Exact Algorithms

• A distribution system

0
D 50,000
3
4
5
D 100,000
5
2
4
1
2
Cap 60,000
2
D 50,000
Production costs are the same, warehousing costs
are the same
40
Heuristics and Exact Algorithms

• 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
5 x 140,000
2 x 50,000
1 x 100,000
2 x 60,000
Cap 60,000
2 x 50,000
D 50,000
Total Costs 1,120,000
41
Heuristics and Exact Algorithms

• 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)

0
D 50,000
3
P1 to WH1 3 P1 to WH2 7 P2 to WH1 7 P2 to WH
2 4
4
5
D 100,000
5
2
P1 to WH1 4 P1 to WH2 6 P2 to WH1 8 P2 to WH
2 3
4
1
2
Cap 60,000
2
D 50,000
P1 to WH1 5 P1 to WH2 7 P2 to WH1 9 P2 to WH
2 4
42
Heuristics and Exact Algorithms

• 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)

0 x 50,000
D 50,000
3 x 50,000
P1 to WH1 3 P1 to WH2 7 P2 to WH1 7 P2 to WH
2 4
D 100,000
5 x 90,000
P1 to WH1 4 P1 to WH2 6 P2 to WH1 8 P2 to WH
2 3
1 x 100,000
2 x 60,000
Cap 60,000
2 x 50,000
D 50,000
P1 to WH1 5 P1 to WH2 7 P2 to WH1 9 P2 to WH
2 4
Total Cost 920,000
43
Heuristics and Exact Algorithms

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

Total Cost 740,000
44
Heuristics and Exact Algorithms

• 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
45
Heuristics and Exact Algorithms

• 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

Source Camm et al. 1997
46
Heuristics and Exact Algorithms

• 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
47
Heuristics and Exact Algorithms

• 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

48
Heuristics and Exact Algorithms

• 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
49
Heuristics and Exact Algorithms

• 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

50
Heuristics and Exact Algorithms

• Assignment 3
• Build an MIP model and solve it for the following
  problem using solver (either CPLEX 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 significantly. 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
Annualfixed cost 400,000 plant 10 million 9.2 million 9.3 million 10.2 million
Production transportationcost East 211 232 238 299
Production transportationcost South 232 212 230 280
Production transportationcost Midwest 240 230 215 270
Production transportationcost West 300 280 270 225
51
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

52
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 approach
• Use optimization model to generate a number of
  least-cost solution at macro-level
• Use simulation model to evaluate solutions
  generated in the first phase

53
Features of Network Configuration DSS

• 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)

54
Features of Network Configuration DSS

• 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

55
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

56
Assignment 4

• Discussion questions 3, 7 (pp. 4142)