IBM ILOG Inventory and Product Flow Analyst (IPFA)

IBM ILOG Inventory and Product Flow Analyst (IPFA) Mozafar Hajian, Ph.D, CITP Client Technical Professional, ILOG Optimization and Supply Chain

Importance of Supply Chain Planning Strategic Strategic Supply Chain Planning 80% The majority of a supply chain s lifecycle costs are locked-in at the start Such up-front decisions include: Tactical Advanced Planning and Scheduling Distribution network Inventory Locations MRP/ERP Supplier network Operational Decisions Execution Solutions 20% Value Inventory Levels Logistics suppliers Source: AMR Research, BCI 2

Value of Inventory: inventory touches every aspect of the business and a more efficient inventory process benefits many parts of the business. CEO s Permanently reducing inventory can increase share price. CIO s can take advantage of the systems already in place by complementing them with a high value-added inventory solution CFO s can free up cash and improve the firm s liquidity Supply Chain Professionals can run a more efficient supply chain and have a process for determining the correct inventory targets 5-20% Reduction in Working Capital

Traditional Local Inventory Planning Replen. time from plant = 10 days DC Net Exposure= 6 days Response time to customer = 4 days Assumes every location needs to hold buffer stock No coordination of raw material, WIP and finished goods Focuses on replenishment time from nearest inbound location Ignores the impact of inbound and outbound dynamics Can lead to: High Inventory Poor Service Levels

Moving from local to global inventory Optimization Global inventory optimization is often called multi-echelon inventory optimization and has the following features: Strategically positions raw materials, work in progress (WIP) and finished goods inventory across the supply chain. This improves inventory turns, free up working capital and increase cash flows. Handles both inbound/outbound and distribution-focused business models. This allows companies to determine the right inventory policies and strategic positioning of inventory at locations. Ongoing setting safety stocks and inventory levels in operational environments. Requires sophisticated stochastic optimization and is therefore very specialized and offered only by experts in this area. Provides end-to-end functionality for manufacturers, retailers and distributors.

Inventory Cost $ Global optimization enables your supply chain to move towards the efficient frontier Using currently available global optimization techniques, it is possible to achieve lower inventory cost, while improving customer service Optimize locally to achieve some inventory benefits Optimize globally to move towards the efficient frontier 70% 80% 90% 100% Service Level

What is Inventory and Product Flow Analyst? IBM ILOG Inventory and Product Flow Analyst is an optimization-based software solution for supply chain inventory management handling inventory optimization for manufacturers, retailers and distributors. Identify key drivers of inventory Reduce inventory and improve service level Decrease lead time to customers Optimize raw materials, WIP and finished goods Optimize supplier selection inventory Calculate total inventory cost Service customers based on cost of goods sold Systematic approach to setting inventory across the supply chain

Inventory and Product Flow Analyst Inventory Questions: 1. How much per SKU? 2. Where? Objective Questions we answer 1. Maximize Service Level 2. Minimize Inventory Cost Procurement Manufacturing Packaging & Distribution Customer Service What impact does each supplier have on the entire supply chain? Which facilities should be make to order or make to stock? How should shipments and policies be coordinated? How should I take advantage of centralization to reduce inventory?

Ease of Use Clean design Menus and data tables organized to facilitate understanding of the model Easy to build simple models from which complexity can be added Tight Integration with Excel and Access Import wizards to help you that pull data from Excel or Access files Edit data in Excel within the application Easy data export to Excel and Access for further analysis Useful error messages, debugging (including constraint relaxation) functionalities User-defined units of measure Fast ROI and low total cost of ownership: You will have the ability to model large and complex supply chains You will have the ability to quickly solve the problems at hand You will be able to use the tool on more projects and for a longer time You will have the needed support All of this is backed by IBM's financial, software development and leadership resources 9

Annual Sales Inventory and Product Flow Analyst Solution- From Desktop To Enterprise 2. Work Flows, Integration to SAP Enabled for your Enterprise 3. Data maintenance; Auto-review 1. User home-page with alerts and reports; 1-page view for planners SKU Plot for a Location FR >99% (SKUs are sized by Gross Margin) FR 95-99% 3,500 FR < 95% 3,000 2,500 2,000 1,500 1,000 500-0.0 2.0 4.0 6.0 8.0 10.0 CV (Forecast Error) 6. Fill Rate Optimization Multi-echelon inventory: The right inventory levels for each SKU at each location Strategic Stand-Alone 5. Demand forecasting 4. Configurable reporting for creating and saving reports

Different Ways to Implement Inventory and Product Flow Analyst 1. Stand Alone Used by business analyst Value: Reduce inventory levels by addressing root causes and underlying assumptions Nice way to quickly reduce working capital Good tool to drive overall operational improvements 2. Loosely Integrated Data loaded through Scenario Creation Wizard Value: Quick implementation and easy to change 3. Fully Integrated with Existing Systems Tightly linked to existing systems Value: Inventory levels are maintained in a systemic manner

Inventory and Product Flow Analyst Use Cases 1. Multi-Echelon Inventory Optimization Overall inventory, working capital, and inventory turns for a given scenario Optimal inventory levels for each SKU at each location Includes safety stock, cycle stock, Work in Progress (WIP), and in-transit 2. Buffer Location Optimization Define which locations can and can/should not hold inventory For those that can/should hold inventory, which type of inventory? 3. Service Optimization Fill Rate Customer service levels are an output as opposed to an input Minimize cost, maximize revenue, or maximize profit; working capital constraints Maximize margin subject to working capital constraints Minimize working capital while meeting an overall fill rate objective Committed Service Time 4. Flow Path Optimization Optimize the routes by which products or product groups flow

1. Multi-Echelon Optimization Optimised inventory levels set through the supply chain to deliver business goals Inventory Analyst can expose areas where too much inventory is being carried. Opportunities to reduce by 20% 30% in targeted areas Spotlight the high impact opportunities Avoid time and effort on low gain activities Understanding of the trade-offs between inventory, service and costs. Quantification of impacts on inventory from long-range scenarios/supply chain initiatives under consideration. Proposed changes in distribution network Alternative supply choices (Longer lead time versus product cost trade-offs) Higher customer service targets

1. Multi-Echelon Optimization Manager/Reviewer Role - Dashboard Review/Approve Review Alerts/Exception Override Recommendations Configurable Summary Reports accessible from Dashboard 14

1. Multi-Echelon Optimization Auto Review Functionality Purpose Allows planners to focus on exceptions only New recommended targets are Auto-Approved or Auto-Ignored based on user set tolerances % Diff between old target and new recommended target Auto-Approve Auto-Ignore Auto-Ignore Auto-Approve -15% -5% 0% +5% +15% 15

2. Buffer Location and Postponement Optimization (Pharma) 265-425 days 90-120 days 40-90 days 30-60 days 40-60 days 30-60 days 35 days Raw Materials Intermediate (Photo) Intermediate (Crude) API Dosing Site Packaging Site & DC Markets/ Customers Antwerp Raw Materials Raw Materials Ireland 1 Photo Photo Belgium Crude Crude Ireland 1 API API Indiana Bulk Bulk FG US (United States) PR (Puerto Rico) FG Belgium Germany Ireland 2 Pack Process FG DC Puerto Rico Puerto Rico Japan Switzerland Switzerland GU (Guyana) = 2005 SC, changed in 2006 Puerto Rico CA (Canada) = No Buffer Stock = Buffer Stocks Support and complement current planning practices Identify key drivers of inventory Provide strategic decision support Evaluate alternative scenarios and processes Understand global vs. site implications of inventory policies Identify costs of current practices i.e. push vs. pull 16 AU (Australla) Bulk items only

2. Buffer Location and Postponement Optimization Baseline Model vs. Optimized Targets Millions $30 $25 24 $20 19 19 17 20 Actual Inv = $75M $15 Optimized = $59M $10 10 7 9 9 Pot Savings = $16 M $5 4 2 $0 Raw Materials Photo Crude API FG 2005 Average Inventory Optimized Working Capital 17

2. Buffer Location and Postponement Optimization Sensitivity Analysis - Customer Service Level Set Customer Service Targets for all FG items to 99.9%, 96%, 93%, 90% FG Inventory is primarily affected Millions $70 $60 $50 $40 $30 $20 $10 $0 $67 $59 $56 $54 $52 Base 99.90% 96.00% 93.00% 90.00% 98.4 % Customer Service Level Millions $30 $25 $20 $15 $10 $5 $0 Raw Materials Photo Crude API FG 90.0% 93.0% 96.0% 99.9% Base 98.4 % ILOG, All rights reserved 18

2. Buffer Location and Postponement Optimization Sensitivity Analysis Forecast Error Baseline Model Coefficient of Variance values vary between 0.38 and 2 $2,000,000 $1,800,000 $1,600,000 $1,400,000 $1,200,000 Monthly Demand$1,000,000 $800,000 $600,000 $400,000 $200,000 ` Typical trend that is encountered. High Demand = Lower Variability $0 0 0.5 1 1.5 2 2.5 Coefficient of Variance 19

2. Buffer Location and Postponement Optimization Sensitivity Analysis Forecast Error Increased/Reduced each Forecast Error Total Working Capital in Supply Chain Millions $80 $70 $60 $50 $40 $30 $20 $10 $0 72 65 59 46 52-50.00% -25.00% Base 25.00% 50.00% Forecast Error Adjustment Millions $30 FG and API primarily affected by Forecast Error variation $25 $20 $15 $10 $5 $0 Raw Materials Photo Crude API FG Base -25.0% -50.0% 50.0% 25.0% 20

Buffer Location and Postponement Optimization Sensitivity Analysis Supplier Uncertainty Reduced Supplier Variability by 1 day 2 days 3 days Set to 0 Total Working Capital in Supply Chain Millions $60 $58 $56 $54 $52 $50 $48 $46 $44 59 57 55 54 50 Base 1 day less 2 days less 3 days less = Zero' Supplier Uncertainty Reduction 21

2. Buffer Location and Postponement Optimization Supply Chain example Raw Materials Intermediate (Photo) Intermediate (Crude) API Dosing Site Packaging Site & DC Markets/ Customers Antwerp Raw Materials Raw Materials Ireland 1 Photo Photo Belgium Crude Crude Ireland 1 API API Indiana Bulk Bulk FG US (United States) PR (Puerto Rico) FG Belgium Germany Ireland 2 Pack Process FG DC Puerto Rico Puerto Rico Japan Switzerland Switzerland GU (Guyana) = 2005 SC, changed in 2006 Puerto Rico CA (Canada) = No Buffer Stock = Buffer Stocks Create a JIT pack to order process versus current pack to stock Orders filled as received Propose buffer stocks for bulk items at Indiana AU (Australla) Bulk items only 22

Millions 2. Buffer Location and Postponement Optimization Postponement Strategy Results Decrease FG Inventory Postponement Strategy Results $25 $20 17 17 20 18 Baseline Optimized Postponement Strategy $15 $10 7 7 9 9 Increase in Bulk inventory $5 4 4 $- 2 2 Raw Materials 1 0 Photo Crude API AU Bulk Bulk FG Baseline = $59M Postponement = $58M Pot Savings < $1M 23

Annual Sales 3. Fill Rate/Service Level Optimization Plot of SKU s by Sales, CV, and Gross Margin SKU Plot for a Location (SKUs are sized by Gross Margin) 3,500 3,000 2,500 2,000 1,500 1,000 500-0.0 2.0 4.0 6.0 8.0 10.0 CV (Forecast Error)

Annual Sales 3. Fill Rate/Service Level Optimization Mix Optimization Results by SKU 3,500 3,000 2,500 2,000 1,500 1,000 500 - SKU Plot for a Location (SKUs are sized by Gross Margin) 0.0 2.0 4.0 6.0 8.0 10.0 CV (Forecast Error) FR >99% FR 95-99% FR < 95% 5% Less Inventory, Same Overall Fill Rate

4. Product Flow Optimization Case Study: Company s Existing Network Domestic Demand International Suppliers 26

4. Product Flow Optimization Case Study: Traditional and Future State TRADITIONAL NETWORK Product flows in one pathway to stores FUTURE STATE Peak Season Product can flow either directly from the vendor to the Regional DC OR through centralized distribution OR directly to Stores via X-dock Vendor Vendor Central DC Regional DC(s) Central DC Delivery Agent Delivery Agent Delivery Agent Delivery Agent Stores Stores Stores Stores 27

COGS 4. Product Flow Optimization Results 200 180 160 140 SKUs with low demand variability were regionally distributed CDC Only CDC - RDC1 CDC - RDC1 - RDC2 CDC - RDC2 120 100 80 60 40 SKUs with high demand variability are best fulfilled from the Central DC 20-0% 10% 20% 30% 40% 50% 60% 70% 80% Product Demand Variability (Coefficient of Variation) ILOG, All rights reserved 28

Product Flow Optimization Case Study: Network Design and Inventory Play an Important Role Baseline Model Model Recommendation 29

Avg Weekly Sales 4. Product Flow Optimization Case Study: Inventory Optimization Can Even Determine Hub and Spoke Strategies by SKU 5,000 Sample Plot of Each SKU by Volume and By Demand Variability 4,000 3,000 2,000 1,000 0 0.0 0.2 0.4 0.6 0.8 1.0 Demand Variability This is for a product family of SKU s within the 40,000 SKU s. Each circle represents a SKU Other drivers include supplier lead time, lead time variability, review period 30

Avg Weekly Sales 4. Product Flow Optimization Case Study: Inventory Optimization Can Even Determine Hub and Spoke Strategies by SKU Inventory Optimization determines that 90% of these SKU s are stored at the spokes 5,000 4,000 3,000 2,000 1,000 Inventory Optimization determines that 55% of these SKU s are stored at the hubs Sample Plot of Each SKU by Volume and By Demand Variability 0 0.0 0.2 0.4 0.6 0.8 1.0 Demand Variability Inventory Optimization determines that 80% of these SKU s are stored at the hubs This is for a product family of SKU s within the 40,000 SKU s. Each circle represents a SKU Other drivers include supplier lead time, lead time variability, review period 31

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Sales ($ billions) 4. Product Flow Optimization Case Study: A large US-based retailer Network of over 3000 stores served out a Central DC (CDC) and 2 Regional DCs (RDC) Objectives Provide better service to stores Reduce peak-congestion at CDC Minimize incremental cost Improve network capability to fulfill and react to demand signal Minimize trade-off between service and cost Incorporate inventory investment as a decision variable $4.500 $4.000 $3.500 $3.000 $2.500 $2.000 $1.500 $1.000 $0.500 $0.000 36.1% of Annual Sales in 4 th Qtr 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr 33

Business Background & Objectives: Case Study 2 Business Background: A large retail company has its own multi-echelon distribution network. They offer a broad range of products to their customers including fresh and non-perishable food items, and household supplies. Now it plans to enlarge its business scope in the beverage category and optimize the inventory level. Project Objective: New merchandise item introduction To determine the most optimal distribution flow based demand and SL. How to allocate the distribution flow of new product To set up the optimized the inventory level for the product in network Optimize the inventory level for all the products in network based demand and SL Current Distribution Network: Supplier CDC RDC Store Convenience store Retail complexity is captured : Multiple suppliers for single product Multiple physical delivery options through own DC network DSD (Direct Store Delivery from supplier) Stores are supplied by multiple DC combination Large stores can act as supplier for smaller C- stores 34 Confidential 7/2/2013

This retail company needs two phase to optimize its product flow and inventory level Phase One: Product flow Configuration / Optimization PFO was used to decide whether beverage should go through DC 1B 1A Enterprise Supply Chain Data Phase Two: Inventory Minimization Inventory Analyst was used to select the inventory position Unit transportation cost and Inventory holding cost rate, demand forecast, etc. Optimize the product distribution flow and stock level; new product introduction PFO Modeling Optimize the stock level, based on the current product distribution flow 2 3 Optimal Product Flows Model transportation and distribution flows and costs. For beverage: Divert flow from CDC to reduce congestion or directly go to RDC to reduce cost Inv. Analyst Inventory Modeling Determine inventory levels and positioning within the whole network to minimize total inventory holding cost. 4 SKU Deployment By Location 35 Confidential 7/2/2013

Two Product Flow Options for Beverages Phase One Option1: Centralized storage Option2: Decentralized storage Default Default 36 Confidential 7/2/2013

Compare the total cost of two options after PFO calculation, this retail company will transport beverage directly to RDC in order to save money Phase One There are three kinds of cost considered in calculation Transportation cost Inventory holding cost Receiving cost Option1 Option2 Logistics cost 2,727,147 2,522,147 Holding cost 174,484 156,328 Total cost 2,901,631 2,678,475 0.2 million saving per year 37 Average stock level = Cycle stock level + Safety stock level Confidential 7/2/2013 Extrapolating the above situation to total active SKU assortment of ~40,000 and assuming 50% of items can be optimized means a savings potential of approx.500m USD

Based on the result of PFO, build use IA to determine optimal inventory for each item at each facility and in each period Phase Two Optimal inventory level for beverage Optimized Network inventory situation for beverage Optimized Average stock level for beverage 2500 2,226 2000 1500 1,577 1000 561 500 376 278 333 0 8 7 RDC1 RDC2 Store1 Store2 Store3 Store4 C-Store1 C-Store2

Inventory Analyst provides detailed results output Phase Two 500 450 Optimized Average stock level vs. Baseline Average stock level 438.4 400 350 Optimized 300 250 Baseline 230.6 Optimized average inventory level is Less than baseline average stock level. 200 150 100 130.7 109.9 98.6 70.6 189.0 The cost saving at inventory holding cost is about 0.07Million 50 0 Beverage Commodity Fresh T-shirt

Average stock level of Store 2 is much higher because of large demand. Improving the reorder frequency will reduce the average stock in Store 2 Phase Two The reorder period for each store is 2 days. But the demand of Store 2 is much higher than others, in order to keep a lower average stock level at Store 2, the reorder frequency of Store 2 is increased to be one order each day What is the optimal inventory level for Store 2 while changing the reorder frequency? Optimized Average stock level with 1 day of reorder period for Store 2 2500 For store2 2000 1500 1722.2 Cycle stock level Safety stock level Safety stock level is increased to 202 from 142 but cycle stock level is reduced to 227 from 419 Average stock level is reduced to 423 from 561 For RDC1 Average stock level is increased to 2310 from 2226 1000 1104 500 0 588 528 283.3 93 226.9 202 212.9 247.3 65 86 6.9 1 6.6 1 RDC1 RDC2 Store1 Store2 Store3 Store4 C- Store1 C- Store2 Accelerating the reorder frequency not only reduces the average stock level in store2, but also causes 717 saving of total holding cost per year

Investment on increasing performance will influence the inventory level as well as the holding cost, IA can help to make quantitative analysis Phase Two Scenario 1: Analyze the relationship between inventory cost and fill rate The current fill rate is 95% and we want to increase the fill rate to 98% for beverage to improve revenue. Safety stock level of 95% FR vs. 98% FR Summary of 95% FR vs. 98% FR 300 250 251 Inventory holding cost increase: 2,338 200 150 165 142 151 FR 95% FR 98% 116 100 93 65 86 Revenue increase: 442,380 50 0 1 2 1 Strore1 Strore2 Strore3 Strore4 C- Strore1 2 C- Strore2 Changes of FR will directly influence the safety stock level at store level. 2,338 is needed to invest to achieve 98% fill rate, but there will be 442,380 revenue increase per year.

Faster response time will greatly influence the inventory level in the each facility of distribution network Phase Two Scenario 2: Analyze the proper stock level for different response time The current committed service time (CST) in the distribution network is 3 days. This retail company would like to improve customer response time. 2500 Stock level for each facility (CST=3 Days) 2500 Stock level for each facility (CST=2 Days) 2000 Cycle stock level 2000 Cycle stock level 1500 1722.17 Safety stock level 1500 1722.2 Safety stock level 1000 1103.99 1000 1104 500 0 419.29 504 473 283.33 212.93 247.27 93 142 65 86 6.88 1 6.55 1 RDC1 RDC2 Store1 Store2 Store3 Store4 C- C- Store1 Store2 In order to achieve 2 days of CST, higher safety stock is needed. 500 0 588 528 283.33 103 419.29 250 212.93 247.27 98 96 RDC1 RDC2 Store1 Store2 Store3 Store4 C- Store1 1 day reduced in CST, about 15% increase of safety stock at RDC level and Store level 6.88 2 6.55 2 C- Store2

Using Inventory and Product Flow Analyst Types of Users Business Analyst/Super User Deep knowledge of IA modeling concepts, inputs, and analysis Evaluate changes to supply chain processes and structures Push/Pull analysis Evaluate strategic changes and what-ifs Project-based work Inventory Planners Needs to know the recommended inventory levels on periodic basis Needs to be able to see exceptions and understand what drove exceptions Needs to be able to review, override, and publish results

Typical user workflows and roles for Inventory Optimization (Note: One person may assume one or more of the three roles specified below) Super User Business Analyst Inventory Planner Master Data Store ERP Master Data Data Maintenance Auto Review Tolerances Manage roles/permissions What if Scenarios New inventory targets generated Review Exceptions/Alerts Override/Approve Recommendations Track Plan vs Actual Summary Reports Key Performance Indicators Master Data Files ECC/APO/BI Inv Targets