Presentation on theme: "Basic Optimization Training LLamasoft, Inc October 2008."— Presentation transcript:
Basic Optimization Training LLamasoft, Inc October 2008
Optimization Course Overview Course Goal: Understand the basics of supply chain optimization through lecture, computer exercises, and coaching Course Objective: Students will be able to use the software, with minimal assistance, to correctly build a supply chain model; add the basic components of optimization to the model; perform an optimization; interpret the results through outputs; and perform an infeasibility analysis.
Agenda Overview of Model Components Optimization Basic Components –Structure –Cost –Constraints Infeasibility Analysis Optimization Results Review
Understanding Optimization Evaluate millions of alternatives, find the global optimal structure: lowest cost structure that meets the constraints Determine the optimal supply chain network structure using MIP/LP programming What is the optimal network structure? Thousands of possibilities… …One Optimal Answer!
Learning Objective- Structure Explain the basic components to modeling in the software Use the components to create a supply chain network Perform a network optimization Goal: Successfully design a new optimal supply chain network using the software
Saving Models and Projects Save Model in Training Folder: C://Llamasoft/Training/Optimization Save Model As: Opt_Training_Basic Save Project As: Guru_Training
Site Types Existing Facility –Indicates this site presently exists in the supply chain Potential Facility –Indicates this site presently does not exist in the supply chain Customer Site –Only customer sites can have demand –Customer sites have sourcing policies but NO inventory policies –They do not ship to other sites or produce any products –Flow into customer sites = $$$ (revenue!) Type, and Choice combine to determine which costs to apply –Open Potential Facility = Add Startup Cost –Closing Existing Facility = Add Closing Cost –Always incur Fixed Operating Costs if flow exists
Creating the Opt_Training_Basic Model 4 Sites MFGMFG DC 1 CZCZ DC 2 SP TP SP IP Product A Product B 2 Products
Opt_Training_Basic Model: Sites Name Location (Address, City, State, Country, Postal Code, Latitude, Longitude) Capacity Period, Capacity Basis Fixed Startup Cost/Cap, Fixed Operating Cost/Cap Closing CostCZCZ DC 1 MFGMFG DC 2
Opt_Training_Basic Model: Add Customer Site Add Customer Site –Name: CZ –City: New York –State: New York –Type: Customer –Graphic: Circle –Graphic Color: Green –Leave all other fields default Open the Sites Table
Opt_Training_Basic Model: Add Manufacturer Add Manufacturer: –Name: MFG –City: Los Angeles –State: California –Type: Existing Facility –Graphic: Square –Graphic Color: Red
Opt_Training_Basic Model: Column Update Place cursor in the Graphic Size Field Right Click or Select the Column Update Button on the toolbar Select 10 from the drop down menu and apply the update
Opt_Training_Basic Model: Layout Map Display the Sites on the Layout Map
Opt_Training_Basic Model: Products Name/ SKU Inventory Valuation Price Weight, Cubic Status Product A Product B
Opt_Training_Basic Model: Add Products Open the Products Table Add (2) Product Records Product_B –Value: 10 –Price: 20 –Weight: 10 –Cubic: 10 –Status: Include Product_A –Value: 5 –Price: 10 –Weight: 5 –Cubic: 5 –Status: Include
Opt_Training_Basic Model: Demand Customer Site Product Quantity Occurrences Time Between Orders Due Date Price
Opt_Training_Basic Model: Add Demand Record 2 –Customer Site: CZ –Product Name: Product_B –Quantity: 100 –Order Time: 0 Open the Demand Table Add (2) Demand Records Record 1 –Customer Site: CZ –Product Name: Product_A –Quantity: 100 –Order Time: 0
Shipments Vs. Demand Shipments are typically modeled through the Transportation Policies table Shipments table allows you to model shipments outside of the Transportation Policies table There are no cost fields in the Shipments data table- the costs in the Transportation Policies table are used to calculate the transportation costs Allows for accurate modeling of a “Push” system, instead of a demand driven supply chain
Opt_Training_Basic Model: Sourcing Policy Identifies which sites to send replenishment and customer orders, and whether product is ordered from an outside source or made at that site MFGMFG DC 1 CZCZ DC 2 Product_A, Product_B
Types of Sourcing Policies Single Source Single Source (Select Closest) Multiple Sources (Most Inventory) Multiple Sources (Order of Preference) Multiple Sources (Probability) Source by Transfer Make Make by Schedule Make (Single Process) Make (Order of Preference) Make (Probability) Hint: Use the Quick Reference Card for descriptions of these policies!
Opt_Training_Basic Model: Adding Sourcing Policies Open the Sourcing Policies Table Add a total of (10) Sourcing Policies –4 Multiple Sources (Most Inventory) CZ can source Product_A from DC_1 and DC_2 CZ can source Product_B from DC_1 and DC_2 –4 Single Source Each DC can only source from the MFG for Both Products –2 Make 1 for each product at the Manufacturer
Opt_Training_Basic Model: Layout Map Display the Sourcing Policies on the Map
Opt_Training_Basic Model: Transportation Policy There must be at least one Transportation Policy that applies to source and destination which is not “Make” Each Transportation Policy defines a “Flow” –Source Site –Destination Site –Product (all applicable products if not explicitly entered) –Mode (1 if not explicitly entered) MFGMFG DC 1 CZCZ DC 2
Types of Transportation Policies Parcel LTL (Less than Truckload) Full TL (Full Truckload) Air, Rail and Ship Daily or Weekly Shipment Periodic Shipment Pooled Outbound/ Pooled Inbound Pooled Periodic Outbound/ Pooled Periodic Inbound Flow (Optimization Only) Link To Lane Aggregate Container Disaggregate Container
Opt_Training_Basic Model: Add Transportation Policies Add (4) Transportation Policies One for each Source-Destination combination defined in the Sourcing Policies -Source Sites: MFG, DC_1, DC_2 -Destination Sites: DC_1, DC_2, CZ -Leave all other fields at default value Open the Transportation Policies Table
Opt_Training_Basic Model: Inventory Policy Defines –Initial Inventory Levels –Safety/ Cycle Stock Levels –Associated Costs One Inventory Policy is optional for each product at the facility sites DC 1 DC 2 MFGMFG Product_AProduct_B Product_AProduct_BProduct_AProduct_B
Opt_Training_Basic Model: Add Inventory Policies Add (6) Inventory Policies –One for each Facility (non-Customer Site) and Product combination Sites: MFG, DC_1, DC_2 Product Name: Product_A, Product_B Leave all other fields at default value Open the Inventory Policies Table
Opt_Training_Basic Model: Add Costs Open the Sourcing Policies Table Add a sourcing cost to one lane (2 policies) –Use the filter bar to view only DC_1 –Update the Average Unit Cost to 1 and clear the filter How do you think this will affect the optimization results?
Opt_Training_Basic Model: Model Options Go to Tools Model Options (F3) Review Optimization Period –Start Date / Time –End Date / Time
Opt_Training_Basic Model: Run the Optimization Optimize the Supply Chain Save the Project and the Model
Check on Learning- Structure You should be able to: –Open Supply Chain Guru TM –Add a new model –Save a project –Save a model –Open a table –Understand differences between sites and customers –Use the filter bar –Move from field to field –Open the layout map –Change settings on the layout map –View sites and policies on the layout map –Understand the types of Sourcing policies –Understand the types of Transportation Policies –Understand when to use shipments vs demand –Understand the types of Inventory Policies –Enter data into tables –Access the help system –Set Optimization Options –Run a simple optimization –Run the error check on a model –Access Optimization Outputs
Learning Objective- Cost Distinguish between the different costs used in Supply Chain Optimization with the software Apply costs to the network built in Exercise 1 Goal: Successfully optimize the network with the new costs included
Three Basic Components of Optimization Structure Costs Constraints
Basic Costs for Optimization Site Costs –Fixed operating –Fixed startup –Closing Transportation Costs –Average Cost –Duty Rate –Discount Rate –Return Trip Cost –Transportation Asset Costs Unit Fixed Cost Inventory Costs –Site inventory –In-transit inventory –Inbound and Outbound Warehousing Production Costs –Work Center Costs Fixed Operating Fixed Startup Closing –Work Resource Cost
Site Costs Fixed Operating Fixed Startup Closing
Site Costs: Fixed Operating Costs associated with the day to day operations of the facility Enable use of a step- function to associate the operating cost based upon operating capacity
Site Costs: Fixed Operating Facility is closed/not used if the throughput is zero. Facility is open at Level 1 if the throughput is between 0 and 5,000 pounds. Facility is open at level 2 if throughput greater than 5,000 pounds
Site Costs: Fixed Start-Up Costs to open and begin operating a new facility Only applies to Potential Facilities No thoughput constraints Ability to use step- function to associate start up cost with operating capacity
Site Costs: Closing Cost to end operations at an Existing Facility Does not apply to Potential Facilities or Customers
Exercise 2a: Create New Cost Model From the Project Explorer, right click on the Opt_Training_Basic Model Select Copy Model Right Click on the copied model Select Save Model As Save model as: Opt_Training_Cost
Exercise 2a: Add Fixed Operating Costs Open the Sites Table Enter the following Fixed Costs: DC_1 CapacityCost 0100500 DC_2 CapacityCost 050 500250 Open the Field Guru
Exercise 2a: Complete Fixed Operating Costs Now run the optimization and view the results!
Transportation Costs: The Concept of “Flow” In Optimization, there are no individual shipments Instead it is the total amount shipped, as determined by the optimizer This total amount is the “Flow”. Site ASite B 10,000 Units
Transportation Cost: Average Cost Related to Cost Basis Transportation Cost per Cost Basis Unit Associated Field Guru
Transportation Cost: Cost Basis Weight = Avg Cost * Weight of Flow Qty = Avg Cost * Number of Units of Flow Cubic = Avg Cost * Volume of Flow Distance = Cost per Mile Fixed = Fixed Cost per Shipment Weight-Distance = Cost per Pound per Mile Qty-Distance = Cost per Unit per Mile Cubic-Distance = Cost per Volume per Mile
Transportation Cost: Distance and Fixed Cost Basis In order to cost these correctly the optimizer needs to approximate the number of shipments made Site A Site B 10,000 Units Total Flow
Transportation Cost: Shipment Weight Since the optimizer only knows the flow (sum of all shipments), the only way to cost at the “shipment” level is to approximate the shipment size. If the flow is 10,000 pounds, and the average shipment weight is 1000 pounds that corresponds to 10 shipments.
Transportation Cost: Distance Calculate using Straight Line –Based on latitude and longitude of source and destination sites –Adds a circuity factor (17%- in Model Options) Calculate Using Mappoint Routing –Interfaces with Microsoft Mappoint to determine actual road distance –Must have Map Point installed on the same computer
Transportation Cost: Transportation Assets Total cost of owning or using each unit of this asset This is a fixed cost, not used to calculate profits or expenses in the network operation Included on the summary report, can be used to compare various scenarios
Exercise 2b: Add Transportation Costs to Cost Model
Exercise 2b: Add Transportation Costs Copy Opt_Training_Cost Model Save as Opt_Training_Cost_Transpo Add the following costs: –MFG facility always costs 2.00 per unit shipped to any location –DC_1 costs 10.00 per unit, per mile to ship to the customer –DC_2 costs 20.00 per unit, per mile to ship to the customer
Exercise 2b: Results Now run the optimization and view results!
Inventory Costs: Facility Inventory Holding Avg Inv = Average Inventory Product Value = Value in Products Table i = Annual inventory carrying cost % T = Optimization period in days Inv Holding Cost = Avg Inv * Product Value * (i/365) * T
Inventory Costs: Average Inventory Calculation Method of Calculation –Inventory Turns OR –Constituent Parts Safety Stock Inventory Cycle Stock Inventory Pre-Build Inventory
Facility Inventory Level Determination Factors which affect inventory levels –Volume/ Qty of product throughput (Tput) –Number of facilities in the network As the number of facilities decreases, the average inventory in the remaining facilities increases due to increased throughput, but at a decreasing rate. Total Facility Inventory
Average Inventory Calculation 1: Inventory Turns Ratio of inventory throughput to average inventory Increasing Inventory Turns reduces Facility Holding Costs Must balance turnover with safety stock to avoid stockout Also called Stock turns, turns, stock turnover
Inventory Turns: Linear Approximation Avg Inv = m * Tput m = Inverse of Inventory Turns Tput = Volume of Product Throughput Inv Turns = 8
Inventory Turns: Pooling Effect Piecewise linear approximation Used in locations with considerable amounts of product, typically called a distribution center Average Inventory can defined over multiple ranges of throughput. Format for the relationship is a series of pairs
Average Inventory Calculation 2: Constituent Parts Pre-build Safety Stock Cycle Stock
Constituent Parts: Pre-Build Inventory Results from demand exceeding production capability in one period, but excess production completed in the previous period Example –In a 2 period model MFG has a production capacity of 50 units –The demand is 20 and 80 units in Periods 1 and 2 respectively –The MFG site produces 50 units each period –In the first period the 30 excess units produced are stored as Pre-Build Inventory Viewed in the Optimization Output- Inventory Table
Constituent Parts: Safety Stock Held excess product Also called a buffer The model may tap into the safety stock when necessary
Constituent Parts: Cycle Stock Portion of inventory allocated to meet anticipated demand In a simple model where demand is constant, cycle stock equals half the order size The blue line refers to actual cycle inventory The red line refers to the average cycle stock The order size is 2 units and occurs once per unit time
Average Inventory Calculation: Constituent Parts Sum of pre-build inventory, safety stock and the cycle stock
Inventory Costs: Facility Inventory Holding Inv Holding Cost = Avg Inv * Product Value * (i/365) * T Avg Inv = Average Inventory Product Value = The Product’s Value in the Products Table i = Annual inventory holding cost % T = Optimization period in days Why determine Average Inventory? Avg Inv is used to calculate Facility Inventory Holding Costs in the Optimization
Inventory Costs: In-transit Inventory Total Cost due to value of products being transported and transport time Q = Quantity of Products in-transit Product Value = Value in Products Table i = Capacity Cost % in Model Options T = Transport Time in Days In-transit Inventory Cost = Q * Product Value * (i/365) * T
In-transit Inventory Costs: Example A to C 1000 * $500 * (15%/365) * 10 = $2,055 B to C 1000 * $500 * (15%/365) * 90 = $18,493 In-transit Inventory Cost = Q * Product Value * (i/365) * T Customer Demand = 1000 units 10 days 90 days DC_B_Overseas DC_A_Local Product Value = $500
Inventory Costs: Inbound and Outbound Warehousing Inbound Warehousing Cost: activity cost of handling and moving one unit of product from receiving dock to inventory Outbound Warehousing Cost: activity cost of removing one unit of this product from inventory to the shipping dock Includes such costs as paper tracking procedures, handling equipment, and personnel Does not include Transportation Costs
Learning Objective- Constraints The learner will be able to explain the different constraints involved in the software, identify potential constraints to a supply chain model, apply constraints to a practice model, and successfully perform an optimization on the model
Constraints in Optimization Basics of Constraints Aggregate Constraints –Flow –Inventory –Production –Site Service Constraints –Max Sourcing Distance –Due Date –End to End –Bundled Demand
The Basics of Constraints: Definition Restrictions placed upon the model Aggregate Constraints: restriction defined for a sum over multiple objects, with at least one object having two or more values –Flow –Inventory –Production –Site Service Constraints: restriction placed on the service to a customer
The Basics of Constraints: Use in the Software Types of Constraints –Minimum –Maximum –Fixed –Conditional Minimum Constraint Variable Inputs –Specific: Refers to one site/ product/ time period/ mode –Set: Refers to a group of sites/ products/time periods/ modes –All: Refers to all sites/ products/ time periods/ modes
Aggregate Constraints Throughput Flow Inventory Production Site
Aggregate Constraints: Throughput Site is restricted by the amount of flow (basis)in the model during the specified period Step function depicts capacity limit with INF
Aggregate Constraints: Flow Restricted by 5 elements: Site, Destination, Mode, Product, or Time Period DC 1CZ 1 Flow requirement, flow requirement type, flow requirement basis, and time period that the restriction occurs
Aggregate Constraints: Flow Places a restriction on the product flow over a set of time periods, between source and destination sites, for products or when using a specified mode
Aggregate Constraints: Flow Count Sets up intricate constraints in the model linking the following 5 variables; Source, Destination, Product, Mode and Period By aggregating the Destination Sites, Products and Modes it disregards the various possible flows that are due to these variables
Aggregate Constraints: Inventory Restricted by 3 Elements –Site –Product –Time Period
Aggregate Constraints: Inventory Allows the specification of additional rules regarding inventory Defines aggregated quantities over sites, products, and time periods
Aggregate Constraints: Inventory Count Similar to aggregate flow count Can utilize the “Set” feature of the Groups Table
Aggregate Constraints: Production Restricted by 4 elements –Site –Process –Product –Time Period
Aggregate Constraints: Production Defines aggregated productions that need to be restricted by a plant, or set of plants and by products, or set of products
Aggregate Constraints: Production Count Similar to Aggregate Flow Count, but pertains to Productions
Aggregate Constraints: Site Defines the minimum and maximum number of open sites allowed in a set of periods
Aggregate Constraints at Sites Allows the user to customize the number of sites that can be used in a specific time period
Service Constraints Maximum Sourcing Distance Due Date End to End Bundled Demand
M2 M3 M1 CZ_1 CZ_2 CZ_3 CZ_4 WH2 WH3 WH1 Consider a network with manufacturing, warehousing, and customer echelons. All flows between two successive echelons are permitted. WH1WH2WH3 M1500800120 M26001000200 M3300500750 CZ_1CZ_2CZ_3CZ_4 WH1180720340600 WH2700150280100 WH315020070640 DISTANCES Service Constraints: Maximum Sourcing Distance
M2 M3 M1 CZ_1 CZ_2 CZ_3 CZ_4 WH2 WH3 WH1 WH2WH3 M1500800120 M26001000200 M3300500750 CZ_1CZ_2CZ_3CZ_4 WH1180720340600 WH2700150280100 WH315020070640 If the maximum sourcing distance is 200 miles for customers and 500 for the warehouses, the network is reduced to the following flow alternatives. DISTANCES Maximum Sourcing Distance
Between the end site and the source node Distance Based Can be set in either the Sourcing Policy Table or the Service Requirements Table
Customer due date-driven service constraints force the demand to be classified by customer lead times. Suppose P1 demand at each customer is 100 units. CZ_1 CZ_2 CZ_3 WH2 WH1 P1 in 7 days=75 P1 in 3 days=25 P1 in 5 days=50 P1 in 1 days=50 P1 in 6 days=40 P1 in 5 days=60 4 3 2 4 6 57 3 1 1 1 2 3 5 Air Truck Rail Classified demand Service Constraints: Customer Due Date
All supply alternatives are feasible for the first demand classification, but only the following alternatives are feasible for the second classification Air Truck Rail CZ_1 CZ_2 CZ_3 WH2 WH1 P1 in 3 days P1 in 1 days P1 in 5 days 4 2 4 5 3 1 1 1 3 5 Customer Due Date
Only from the last echelon site to the customer Time- based Set in the Demand Table
M2 M1 CZ_1 CZ_2 WH2 WH3 WH1 End-to-end service requirements are given from a make-node to a customer node. Time from M1 to CZ_1 for Product1 <= 5 days Time from M2 to CZ_1 for all products <= 7 days Distance from M1 to CZ_2 for Product2 <= 250 miles Service Constraints: End-to-End
End to End Constraints Source Site does not have to directly deliver to the customer; there may be other facilities in the network where the order will pass through Specified by maximum time for an order to leave the facility and reach the customer OR by maximum allowable distance between the facility and the customer Set in Service Requirements Table
Service Constraints: Bundle Demand When choosing to bundle demand, demand for all products at one customer site will be sourced from one or multiple facilities at the same ratio WH1 WH2 CZ_1 Demand CZ_1(P1) = 600 Demand CZ_1(P2) = 100 450 75 150 25
Bundled Demand Check this box to aggregate all the demand by customers When a customer demands multiple products, these are sourced in equal ratios from one or multiple sites (proportional to the demand quantities for these products)
Exercise 3a: Constraining the Optimization Model
Unconstrained Model Al Five DCs in Use Houston Processing Plant supplies only DC_KC
With Aggregate Flow Constraints Max Flow Reqt Type means at most 500 units of flow can go through DC_Albany. Cond Min Flow Reqt Type means we either have at least 1000 units flow through DC_Portland or none at all. How does this change our optimized results?
With Aggregate Flow Constraints DC_Portland not used, customers now served by DC_Phoenix Fewer CZs in Northeast are served by DC_Albany, more by DC_Atlanta
With Aggregate Production Constraints Max Flow Requirement Type means that at most 1000 units can be produced at Norfolk. Min Flow Requirement Type means at least 850 units must be produced at Reno. How does this change our optimized results?
With Aggregate Production Constraints Fewer CZs in Midwest served by DC_Atlanta, more by DC_KC.
With Aggregate Site Constraints Create Group that contains all five DCs. Constrain Optimizer to select between one and three sites from within that group. How does this change our optimized results?
With Aggregate Site Constraints Portland and Phoenix DCs are unused, KC picks up the slack.
With Aggregate Inventory Constraints Open the Optimization Output Inventory table and note the inventory costs at DC_KC. Set Minimum Inventory at DC_KC to 100 units. Optimize the model. How does this change inventory costs at DC_KC?
Copy the Final Cost Model Save as Opt_Training_Constraints Add the following Constraint: –DC_1 can only ship a maximum of 50 units of Product_A to CZ_1 for the entire model period (Horizon) Now run the model and view the results! How this affect the network design?
Check on Learning- Constraints You should be able to: –Define aggregate constraints –Define service constraints –Open the service requirements table –Open the aggregate constraint tables –Create service constraints –Create aggregate constraints –Define and distinguish between serve and aggregate constraints –Explain the constraint requirement types –Apply aggregate constraints to a model –Apply service constraints to a model –Understand aggregate constraints sum and objects
Guru Infeasibility Analysis Sometimes the optimization solver returns with a “Problem Infeasible” error message Infeasibility refers to a problem with input data- there is no solution that fulfills all the constraints Guru provides the following tools to help the user identify the source of infeasibility –Check for supply-demand imbalance –Check for logic errors in defining the network structure –Remove all or some hard constraints and solve again
Infeasibility Analysis Select the hard constraints to impose