Inventory Optimization-Introduction

What is Inventory Optimization Module? Strategic analysis tool that determines the best cost effective fill rate and optimal stock level for each part/loc Budget analysis tool to calculate “best” performance given a budget constraint Enhanced algorithm to fully explore multi-echelon solution space while maintain scalability.

The Inventory Optimization Challenge The Business Challenge: For more critical or expensive parts, how do we insure that we consider all available information relevant to planning a part within the network on a case-by-case basis, such as: where to put those parts, and what levels to invest in to get the maximum “bang for the buck”? Opportunity: Use the Servigistics Inventory Optimization to do the additional analysis required to refine initial single item modeling results.

The “Big Picture” Inventory Optimization Module Is intended as a “strategic” solver, used to determine stocking strategy on an infrequent (monthly or less) basis. To envision the way the Inventory Optimization module works, suppose you have 1000 valuable or critical parts, and 50 stocking locations. That’s 50,000 stocking alternatives. The first stage this module will perform “Marginal Analysis” to pick the part and location that gives the best “bang for the buck” in satisfying customer demand. Then it will conduct “Multi-echelon optimization” to fully explore the solution with the implication of multi-echelon replenishment hierarchy and identify the best solution, The last stage is searching for sub-optimal solution but compliant with budget constraint. Inventory Optimization expresses its results as ideal ReOrder Point for every part/location pair considered.

Part/Loc target is an given input Item Based Planning Item Based Planning: Part/Loc target is an given input Ignores tradeoff between parts (price) Ignores tradeoffs between echelons Ignores “customer wait times” Net results: Too much inventory is held for expensive parts More inventory is held at less crucial location and vice versa

System Level Optimization Field Location Central Location Vendor Field Location Central Location Vendor Inventory Optimized Inventory Optimized Planning across multiple levels and multiple parts, providing lowest possible inventory for a target service level Optimize each part across all locations & all levels, to remove redundancy Optimize across parts, to limit the investment in slow-moving parts across the chain Determined by field part’s end-of-support-chain fill rate goal Based on lowest support chain overall inventory Results in sub-optimization when used with multiple levels Optimize a single part for each location Each stocking location evaluated independently, in sequence Determined by central & filed fill rate goals Based on central part’s fill rate goal, demand and LT forecasts, & Safety stocks

Inventory Optimization Benefits Multi-Echelon, Multi-Item planning provides several benefits that most current service parts planning systems are unable to achieve: For certain types of inventory situations, significant incremental inventory reduction beyond traditional solutions. Reductions in expediting costs and increases in planner productivity (because higher service levels can be achieved with the same inventory) by price trade off within single echelon, and then echelon trade off when in multi echelons) 7 7

Simply put, what happens at the margin Marginal Analysis Simply put, what happens at the margin What are the consequences of spending an additional dollar on spares For each item at each location calculate Bang4Buck = ΔEBO/$ Benefit divided by price. This is simplified Choose highest B4B value alternative Repeat until objective is achieved

Use the loss function to determine EBO Expect Back Order Use the loss function to determine EBO If s (inventory position) is 0, then EBO is demand within lead time The reduce of EBO for adding one unit, is the “benefit” of adding one unit. Inventory Position is equally distributed between ROP + 1 to ROP + Q (if Q > 1) Get EBO derivation 9 9

Trade Offs Two trade offs need to be understood clearly Price trade off Echelon trade off Trade offs are also the driver of benefits two sides of the sword, need to be managed.

Multi-Item Optimization 97% 96% 92% 99% 0% Considers the following variables: Part Cost Demand Lead-Time Demand Variability Lead-Time Variability Minimum/Maximum DS Multi-Item Optimization determines the best tradeoff between every part’s performance and investment in a location

If “benefit” is the same, then cheaper part will yield better b4b. Price Trade-Off If “benefit” is the same, then cheaper part will yield better b4b. If the difference of price for pairs in a scheme is huge, then the trade off will be significant Say if the most expensive part is 1000 time more than cheapest part. Then expensive part requires 1000 times of benefits before it can be picked before the cheap part The result of the recommended fill rate/availability could be 1000 times different if all other factors are the same It is more a business question whether the magnitude of trade off is acceptable.

Echelon Trade Off It should be understood that trade off could increase/decrease either upstream or downstream fill rate/availability dramatically while maintaining the same overall performance Sometimes, central with low or no demand still could be planned with 99% fill rate while yield overall lower inventory at field locations Vice versa, due to different lead time and dependent demand pattern, central could only have 50% DS while field locations have 99% will majority of the stock Even the phenomena could be in smaller magnitude than price trade off, it could still be the major source of saving.

Optimal Inventory Solution: Strategic and Tactical Multi-echelon optimization Multi-part optimization Multi-indenture (availability/SLA) optimization Calculate service level for every part and location Inventory Optimization Service Level Targets Determine ASL for each location Safety level calculation formulas (type and volume) Complex part chaining Pooling Planning Stocking Plan (ASL/Levels) Others, like Subaru of America, have the same situation. With disparate transaction systems and silo planning systems, there is a great opportunity for improvement. Shortage and Excess thresholds ASL change management Balancing Fair share allocation Execution / Management Actions (buy, repair, move, expedite, cancel, etc.) Tactical

Summary Built on existing Servigistics Platform Works in combination with Servigistics Planner to provide an optimal strategic and tactical solution Handles the following real world scenarios: Multi Echelons of Support Multi Items Highly scalable Applies modeling techniques Models the entire network Marginal Analysis (“bang for the buck”) Incorporates Sherbrook and Muckstadt ‘s Metric Theory Applies the appropriate statistical distribution to model demand during lead times. Poisson, Negative Binomial and Normal Simultaneously considers many MOEs Type I/II Fill Rate Budget Constraints - Segment and Pair level override