SALES AND LOGISTICS MANAGEMENT

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SALES AND LOGISTICS MANAGEMENT

• Winter 2000
• Prof. Dr. Füsun Ülengin

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Supply Chain Management and Analysis

• What is Supply Chain Management (SCM)?
• What is the difference (if any) between SCM and
  Business Logistics Management?
• Supply Chain Definition (G.C. Stevens, 1989) .
  . . a connected series of activities which is
  concerned with planning, coordinating and
  controlling materials, parts, and finished goods
  from supplier to customer. It is concerned with
  two distinct flows (material and information)
  through the organization.
• The Basic Problem Get the right amounts of the
  right products to the right markets at the right
  time in the most economical way.

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The Supply-Chain
VISA

Credit Flow
Materiall Flow
Consumer
Manufacturing
Supplier
Retailer
Retailer
Supplier
Wholesaler
Cash
Order
Schedules
Flow
Flow
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The Supply Chain
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Key Supply Chain Activities

• Customer Service Standards
• Cooperate with marketing to
• Determine customer needs and wants for logistics
  customer service
• Determine customer response to service
• Set customer service levels
• Transportation
• Mode and transport service selection
• Freight consolidation
• Carrier routing
• Vehicle scheduling
• Equipment selection
• Claims processing
• Rate auditing

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Key Supply Chain Activities

• Inventory management
• Raw materials and finished goods stocking
  policies
• Short-term sales forecasting
• Product mix at stocking points
• Number, size, and location of stocking points
• Just-in-time, push, and pull strategies
• Information flows and order processing
• Sales order-inventory interface procedures
• Order information transmittal methods
• Ordering rules
• Cooperate with production/operations to
• Specify aggregate quantities
• Sequence and time production output

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Supply Chain Support Activities

• Warehousing
• Space determination
• Stock layout and dock design
• Warehouse configuration
• Stock placement
• Materials handling
• Equipment selection
• Equipment replacement policies
• Order-picking procedures
• Stock storage and retrieval

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Supply Chain Support Activities

• Purchasing
• Supply source selection
• Purchase timing
• Purchase quantities
• Protective package design for
• Handling
• Storage
• Protection from loss and damage
• Information maintenance
• Information collection, storage, and manipulation
• Data analysis
• Control procedures

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Additional Factors to Consider

• Product design for manufacture and distribution,
  i.e., the constraints that product
  characteristics place on ease of manufacture and
  distribution.
• Product mix from a marketing standpoint, i.e.,
  which products the chain will carry.

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Evolution of Logistics

• Pre-1950 Dormant years
• 1950-1970 Development Years
• Shift in consumer attitude and demand pattern
• Cost pressure in industry
• Improvement in computer technology
• Experience of military logistics
• 1970-1980 The Take-off Years
• 1980- Vital importance
• Costs
• Supply and Distribution Lines are Lengthening
  (Toyota example)
• Logistics is Important to Strategy(Benetton
  example)
• Customers Increasingly Want Quick Customized
  Response
• Logistics in Non-manufacturing Areas(Service
  industry,Military etc.)

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Logistics Strategy and Planning

• Three objectives of logistics strategy
• Cost reduction (variable costs)
• Capital reduction (investment, fixed costs)
• Service Improvement (may be at odds with the
  above two objectives).
• Primary Logistics Planning Areasi
• Short term(operational level)
• How to load trucks for delivery
• How much stock to allocate to each warehouse from
  a current production run
• Vehicle routing, vehichle scheduling
• example(dispatching the trucks Sweep algorithm)
• Tactical level
• purchasing, production decisions, inventory
  policies, transportation strategies including the
  frequency with which the customers are visited
• Long term(Strategic level)
• Facility location
• Example(warehouse territory definition landed
  cost method)
• Specification of the customer service standards

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An Example to Operational PlanTSP Formulation

• Minimize
• Subject to

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Coincident Origin and Destination The TSP

• Often firms maintain one or more warehouses which
  stock goods and make periodic deliveries to
  customers. The firm maintains a fleet of
  vehicles at the warehouse and when customers
  require delivery the vehicle transports the goods
  to one or more customers and returns to the
  warehouse.
• If vehicle must travel to one customer and back,
  problem reduces to finding shortest path to the
  customer. If vehicle must deliver to two
  customers, then we only have two points to visit
  and the problem reduces to finding the shortest
  path to customer 1, finding the shortest path
  between customers 1 and 2, and then finding the
  shortest path between customer 2 and our depot
  (we assume that distances are symmetric).

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Coincident Origin and Destination The TSP

• If, however, the vehicle must deliver to more
  than two customers, we must decide the order in
  which we will visit those customers so as to
  minimize the total cost of making the delivery.
• We first suppose that any time that we make a
  delivery to customers we are able to make use of
  only a single vehicle, i.e., that vehicle
  capacity is not an issue. We need to dispatch a
  single vehicle from our depot to n - 1 customers,
  with the vehicle returning to the depot following
  delivery. This is the well-known Traveling
  Salesman Problem (TSP). The TSP has been well
  studied and solved for problem instances
  involving thousands of nodes. We can formulate
  the TSP as follows

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TSP Formulation

• In the TSP formulation if we remove the third
  constraint we have the simple assignment problem,
  which can be easily solved.
• The addition of the third constraint set,
  commonly called subtour elimination constraints,
  makes this a very difficult problem to solve.
• The subtour elimination constraints state the
  following take any strict subset U of the nodes
  in the network (where the depot and each customer
  represent a node) and let E(U) denote the set of
  all arcs with both ends touching nodes in the set
  U. If we sum over all of the arc flow variables
  corresponding to the arcs in E(U), their sum
  cannot exceed one less than the number of nodes
  in the set U (U denotes the number of nodes in
  the set U) or we have a subtour.

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Questions about the TSP

• Given a problem with n nodes, how many distinct
  feasible tours exist?
• How many arcs will the network have?
• How many xij variables will we have?
• How could we quantify the number of subtour
  elimination constraints?
• The complexity of the TSP has led to heuristic or
  approximate methods for finding good feasible
  solutions. The simplest solution is that of the
  nearest neighbor.
• Begin at the depot and calculate the distance to
  each of the remaining n 1 nodes. Select the
  closest node as the one you visit immediately
  after leaving the depot. Call this node 1. Next
  determine the distance from node 1 to each of the
  remaining n 2 nodes, and visit the node that
  minimizes this distance immediately following
  your visit to node 1. Continue choosing the
  nearest neighbor until the only remaining
  choice is to return to the depot.

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6 City TSP Network
Illustration of subtours
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TSP Heuristics

• The sweep heuristic will perform much better in
  the worst case then the nearest neighbor. The
  sweep heuristic basically attempts to make an
  outer loop around the nodes.
• To implement the sweep heuristic we need to
  create a map of the nodes we will visit. Then
  draw a straight line emanating from the depot
  (the direction of the line is not important).
  Next visualize the line as sweeping either
  clockwise or counter-clockwise through a circle
  of radius r. Each time the radius line
  intersects a customer location make that customer
  the next customer on the route. If we have ties,
  implement a tie breaking rule, such as that of
  first visiting the customer that is closest to
  the previous customer on the route.

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Illustration of VRP
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Sweep Heuristic
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Single Depot, Multiple Destinations, Vehicle
Capacities

• When the depot contains many vehicles and vehicle
  capacity constraints come into play, the problem
  becomes even more complex.
• If each customer has enough demand to receive a
  full truckload the problem is easy and we simply
  use the shortest path to get the single truck to
  each customer. Otherwise, we must decide which
  customers will receive deliveries from the same
  truck, and then decide how to route the trucks to
  the customers on the route.
• We will look at a mixed-integer programming
  formulation of the Vehicle Routing Problem (VRP).

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The Vehicle Routing Problem (VRP)

• VRP generalizes the TSP since we have K capacity
  constrained (homogeneous) vehicles at a depot,
  each of which must visit a subset of the n - 1
  customers once and return to the depot. No two
  vehicles may visit the same customer.
• This means that each vehicle must complete a
  Hamiltonian tour (a Hamiltonian tour is a
  feasible TSP solution). The objective is to
  determine the minimum travel cost required to
  serve all customers. Let A denote the set of
  pairs of cities, and let k index trucks, each
  with capacity u. Assume that customer i has
  demand equal to di.
• In this formulation we require two types of
  variables, one set that tells us if we use a link
  (xij, as before) and another set that assigns a
  truck to a link if we use the link . We
  formulate the VRP as follows (node 1 is the
  depot)

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VRP Formulation

• Minimize
• Subject to (i, j) ? A (V1)
• i 2, .., n, (V2)
• j 2, , n, (V3)
• (V4)
• k 1, , K, (V5)
• subsets U of 2, 3, , n, (V6)
• xij ? 0, 1 (i, j) ? A,
• (i, j) ? A, k 1, , K.

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VRP Formulation Comments

• TSP is a special case of the VRP where we have a
  single vehicle with infinite capacity (K 1, u
  ?).
• Constraints (V1) force assigning a truck to link
  (i, j) if we use the link.
• (V2) and (V3) force entering and leaving each
  city (customer) exactly once.
• (V4) force entering and leaving the depot K times
  (once for each truck).
• (V5) ensure that the demand of customers assigned
  to truck k does not exceed the truck capacity.
• (V6) provide subtour elimination for any subtours
  not including the depot (note that the depot will
  be included in exactly K subtours in every
  feasible solution).

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VRP Heuristic Principles

• 1. Try to assign customers in close proximity to
  the same truck.
• 2. Assign customers in close proximity (not on
  the same truck) to the same delivery day (to
  better manage capacity usage).
• 3. Build routes beginning with the farthest
  delivery and cluster around this delivery first.
• 4. Routes should form a teardrop pattern
  (similar to sweep heuristic for TSP).
• 5. Allocate largest vehicles to routes before
  small vehicles.
• 6. Plan pickups during deliveries, not after all
  deliveries have been made.
• 7. Outliers are candidates for alternate means
  of transport.
• 8. Avoid time windows if possible.

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VRP Heuristics

• Given the difficulties in solving the TSP, we
  cannot expect to have great success solving large
  VRP problems without heuristic approaches. We
  use several guiding principles in developing
  these heuristics.
• Note that the above formulation does not consider
  additional practical restrictions such as limits
  on driver time, time window delivery
  restrictions, or return of goods from customers
  to the depot.

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An Example To Strategic PlansWarehouse
Territory Definition

• Warehouse Warehouse cost Transportation Cost
• (/ton) Fixed((/ton) Variable(/ton.km)
• A 2.06 6.05 0.0050
• B 1.1 2.86 0.0082
• C 1.92 6.36 0.0042
• D 2.38 5.76 0.0045