Logistics Network Configuration

1
Logistics Network Configuration
2
Outline

• What is it?
• Methodology
• Modeling
• Data Aggregation
• Validation
• Solution Techniques.

3
The Logistics Network

• The Logistics Network consists of
• FacilitiesVendors, Manufacturing Centers,
  Warehouse/ Distribution Centers, and Customers
• Raw materials and finished products that flow
  between the facilities.

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Customers, demand centers sinks
Field Warehouses stocking points
Sources plants vendors ports
Regional Warehouses stocking points
Supply
Inventory warehousing costs
Production/ purchase costs
Transportation costs
Transportation costs
Inventory warehousing costs
5
Logistics Design Decisions

• Determine the appropriate number of warehouses
• Determine the location of each warehouse
• Determine the size of each warehouse
• Allocate space for products in each warehouse
• Determine which products customers will receive
  from each warehouse

6
Decision Classifications

• Strategic Planning Decisions that typically
  involve major capital investments and have a long
  term effect
• 1. Determination of the number, location and
  size of new plants, distribution centers and
  warehouses
• 2. Acquisition of new production equipment and
  the design of working centers within each plant
• 3. Design of transportation facilities,
  communications equipment, data processing means,
  etc.

7
Decision Classifications

• Tactical Planning Effective allocation of
  manufacturing and distribution resources over a
  period of several months
• 1. Work-force size
• 2. Inventory policies
• 3. Definition of the distribution channels
• 4. Selection of transportation and
  trans-shipment alternatives

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Decision Classifications

• Operational Control Includes day-to-day
  operational decisions
• 1. The assignment of customer orders to
  individual machines
• 2. Dispatching, expediting and processing orders
• 3. Vehicle scheduling

9
Network Design Key Issues

• Pick the optimal number, location, and size of
  warehouses and/or plants
• Determine optimal sourcing strategy
• Which plant/vendor should produce which product
• Determine best distribution channels
• Which warehouses should service which customers

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Objective of Logistics Management

• Design or configure the logistics network so as
  to minimize annual system-wide cost subject to a
  variety of service level requirements

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Network Design Key Issues

• The objective is to balance service level against
  
• Production/ purchasing costs
• Inventory carrying costs
• Facility costs (handling and fixed costs)
• Transportation costs
• That is, we would like to find a
  minimal-annual-cost configuration of the
  distribution network that satisfies product
  demands at specified customer service levels.

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Network Design ToolsMajor Components

• Mapping
• Mapping allows you to visualize your supply chain
  and solutions
• Mapping the solutions allows you to better
  understand different scenarios
• Color coding, sizing, and utilization indicators
  allow for further analysis
• Data
• Data specifies the costs of your supply chain
• The baseline cost data should match your
  accounting data
• The output data allows you to quantify changes to
  the supply chain
• Engine
• Optimization Techniques

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Mapping Allows You to Visualize Your Supply Chain
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Displaying the Solutions Allows you To Compare
Scenarios
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Data for Network Design

• 1. A listing of all products
• 2. Location of customers, stocking points and
  sources
• 3. Demand for each product by customer location
• 4. Transportation rates
• 5. Warehousing costs
• 6. Shipment sizes by product
• 7. Order patterns by frequency, size, season,
  content
• 8. Order processing costs
• 9. Customer service goals

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Too Much Information

• Customers and Geocoding
• Sales data is typically collected on a
  by-customer basis
• Network planning is facilitated if sales data is
  in a geographic database rather than accounting
  database
• 1. Distances
• 2. Transportation costs
• New technology exists for Geocoding the data
  based on Geographic Information System (GIS)

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Aggregating Customers

• Customers located in close proximity are
  aggregated using a grid network or clustering
  techniques. All customers within a single cell or
  a single cluster are replaced by a single
  customer located at the centroid of the cell or
  cluster.We refer to a cell or a cluster as a
  customer zone.

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Impact of Aggregating Customers

• The customer zone balances
• 1. Loss of accuracy due to over aggregation
• 2. Needless complexity
• What affects the efficiency of the aggregation?
• 1. The number of aggregated points, that is the
  number of different zones
• 2. The distribution of customers in each zone.

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Why Aggregate?

• The cost of obtaining and processing data
• The form in which data is available
• The size of the resulting location model
• The accuracy of forecast demand

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Recommended Approach

• Use at least 300 aggregated points
• Make sure each zone has an equal amount of total
  demand
• Place the aggregated point at the center of the
  zone

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Testing Customer Aggregation

• 1 Plant 1 Product
• Considering transportation costs only
• Customer data
• Original Data had 18,000 5-digit zip code ship-to
  locations
• Aggregated Data had 800 3-digit ship-to locations
• Total demand was the same in both cases

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Comparing Output
Total Cost5,796,000 Total Customers 18,000
Total Cost5,793,000 Total Customers 800
Cost Difference lt 0.05
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Product Grouping

• Companies may have hundreds to thousands of
  individual items in their production line
• 1. Variations in product models and style
• 2. Same products are packaged in many sizes
• Collecting all data and analyzing it is
  impractical for so many product groups

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A Strategy for Product Aggregation

• Place all SKUs into a source-group
• A source group is a group of SKUs all sourced
  from the same place(s)
• Within each of the source-groups, aggregate the
  SKUs by similar logistics characteristics
• Weight
• Volume
• Holding Cost

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Within Each Source Group, Aggregate Products by
Similar Characteristics
Rectangles illustrate how to cluster SKUs.
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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

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Sample Aggregation TestProduct Aggregation
Total Cost104,564,000 Total Products 46
Total Cost104,599,000 Total Products 4
Cost Difference 0.03
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Minimize the cost of your logistics network
without compromising service levels
Optimal Number of Warehouses
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The Impact of Increasing the Number of Warehouses

• Improve service level due to reduction of average
  service time to customers
• Increase inventory costs due to a larger safety
  stock
• Increase overhead and set-up costs
• Reduce transportation costs in a certain range
• Reduce outbound transportation costs
• Increase inbound transportation costs

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Industry BenchmarksNumber of Distribution
Centers
Food Companies
Chemicals
Pharmaceuticals
Avg. of WH
3
14
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- 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
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A Typical Network Design Model

• Several products are produced at several plants.
• Each plant has a known production capacity.
• There is a known demand for each product at each
  customer zone.
• The demand is satisfied by shipping the products
  via regional distribution centers.
• There may be an upper bound on total throughput
  at each distribution center.

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A Typical Location Model

• There may be an upper bound on the distance
  between a distribution center and a market area
  served by it
• A set of potential location sites for the new
  facilities was identified
• Costs
• Set-up costs
• Transportation cost is proportional to the
  distance
• Storage and handling costs
• Production/supply costs

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Complexity of Network Design Problems

• Location problems are, in general, very difficult
  problems.
• The complexity increases with
• the number of customers,
• the number of products,
• the number of potential locations for warehouses,
  and
• the number of warehouses located.

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Solution Techniques

• Mathematical optimization techniques
• 1. Heuristics find good solutions, not
  necessarily optimal
• 2. Exact algorithms find optimal solutions
• Simulation models provide a mechanism to
  evaluate specified design alternatives created by
  the designer.

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Heuristics and the Need for Exact Algorithms

• Single product
• Two plants p1 and p2
• Plant P1 has an annual capacity of 200,000 units.
• 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.

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Heuristics andthe Need for Exact Algorithms
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Why Optimization Matters?
0
D 50,000
3
Cap 200,000
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
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Traditional Approach 1Assign each market to
closet WH. Then assign each plant based on cost.
D 50,000
Cap 200,000
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
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Traditional Approach 2Assign each market based
on total landed cost
0
D 50,000
3
Cap 200,000
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
40
Traditional Approach 2Assign each market based
on total landed cost
0
D 50,000
3
Cap 200,000
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
Market 1 is served by WH1, Markets 2 and 3 are
served by WH2
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Traditional Approach 2Assign each market based
on total landed cost
0 x 50,000
D 50,000
3 x 50,000
Cap 200,000
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
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The Optimization Model

• The problem described earlier can be framed as
  the following linear programming problem.
• Let
• x(p1,w1), x(p1,w2), x(p2,w1) and x(p2,w2) be the
  flows from the plants to the warehouses.
• x(w1,c1), x(w1,c2), x(w1,c3) be the flows from
  the warehouse w1 to customer zones c1, c2 and c3.
• x(w2,c1), x(w2,c2), x(w2,c3) be the flows from
  warehouse w2 to customer zones c1, c2 and c3

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The Optimization Model

• The problem we want to solve is
• min 0x(p1,w1) 5x(p1,w2)
  4x(p2,w1)
• 2x(p2,w2) 3x(w1,c1) 4x(w1,c2)
• 5x(w1,c3) 2x(w2,c1) 2x(w2,c3)
• subject to the following constraints
• x(p2,w1) x(p2,w2) ? 60000
• x(p1,w1) x(p2,w1) x(w1,c1) x(w1,c2)
  x(w1,c3)
• x(p1,w2) x(p2,w2) x(w2,c1) x(w2,c2)
  x(w2,c3)
• x(w1,c1) x(w2,c1) 50000
• x(w1,c2) x(w2,c2) 100000
• x(w1,c3) x(w2,c3) 50000
• all flows greater than or equal to zero.

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The Optimal Strategy