Warehouse Stocking Policy and Delinquency Author(s) Names: Sidhartha Shishoo and Prashanth Navalgund

Transcription

1 Warehouse Stocking Policy and Delinquency Author(s) Names: Sidhartha Shishoo and Prashanth Navalgund Abstract Inventory optimization of spare parts is indispensable to present day organizations, as high levels of inventory could increase holding costs and stock outs could affect product delivery. The objective of this paper is to identify and analyze the various factors, which effect the inventory policy of a company and thereby also study its impact on sales. Introduction A support service constitutes a significant portion of every economy, often generating almost twice the profit as much as the sales of the original product. For example, a 2003 study by Accenture found that $9B in aftersales revenues produced $2B in profits for General Motors, which is a much higher rate of profit than its $150B in car sales generated over the same time period. According to the same study, aftersales services and parts contribute only 25% of revenues across all manufacturing companies but are responsible for 40%50% of profits. For this reason companies around the world give attention to After Market Sales. However in this process, companies tend to over invest in stocking their spares products. This paper recommends the use of sixsigma tools and techniques for defining the stocking policy of the spare parts. Supply Chain Analytics is being implemented as the driving force for delivering effective decisions in various industries. Supply Chain Analytics combines Information; Technology & Human Resources coherently to analyze the various data streams through trend, seasonality, patterns and demographics across the entire value chain of manufacturing. These include 1. Raw material sourcing 2. Inbound logistics 3. Warehouse inventory stocking 4. Manufacturing 5. Quality Assurance 6. Marketing & Sales 7. Out bound Logistics 8. Reverse Logistics 9. After sales service Raw material sourcing & suppliers: To operate and plan efficiently, the suppliers always expect stable demand and large orders. Yet at the same time they also prefer flexible delivery times, so that they can Page 1 of 10

2 satisfy multiple client bases. Achieving economies of scope and scale is the prime objective in these situations. The Materials, Warehousing and Logistics management also have their own set of requirements. These would include minimizing inventory levels, minimizing transportation costs, and improvement in inventory turns. Retailers or Dealers need shorter delivery times and accurate order delivery. Designing a Supply Chain, which can fulfills all these constraints, is complicated. Hence, trade offs are generally necessary to arrive at an optimized value chain. For instance, there could be 1. The Lot SizeInventory trade off 2. The InventoryTransportation Cost trade off 3. The Lead timetransportation Cost 4. The Product VarietyInventory Trade off 5. The CostCustomer Service Trade off All the above are examples of the cost customer service trade off. Customer Service here refers to the ability of the retailer /warehouse dealer to meet a customer s demand from the stock on time. Herein forward it is referred to as service level. Service Level Exchange Curve: An exchange curve, by definition shows a relative trade off between two areas of interest; refer (Fig1). In this case, Service Level and investment in safety stock. We will discuss this for a single item, but it can be extended to multiple items. Assumptions: The lead time demand is normally distributed Β, service level is the fill rate. The proportions of the annual demand (in units, customer orders or in dollars) are instantaneously filled from the stock. For every value of service level (β), two measures can be calculated. These are Expected number of units short per year = (1β)D Investment in safety stock = czσ lt Where, β = Service Level D = Mean of the demand distribution c = Standard Cost of the component z = Normal standard deviate Page 2 of 10

3 σ lt = Standard deviation of lead time demand. Fig1 Let us now look at a multiechelon scenario as shown in Fig2: Fig2 Page 3 of 10

4 Supplier Flow of Demand Customer 1 Supplier Customer 2 Supplier WareHouse1 1. RLQ Warehouse2 Customer 3 2. POS 3. 2 Bin System 4. Overhaul Supplier Customer n Assembly 1 Assembly 2 Assembly n Scenario Explanation: Warehouse1: This warehouse is the mother warehouse. It stocks parts for the demand coming from various streams. These are Independent Demand: This demand is generally referred to as Spares Demand or After Market Demand. This demand stream is a forecasted demand. The forecast is driven from historic demand at a Stock Keeping Unit (SKU) level. The order for this stream is in multiples of Replenishment Lot Quantity (RLQ). This demand is triggered from Warehouse 2, which is customer facing. There is another component to this independent demand, which is triggered from the Over haul shops. These Overhaul shops stock parts on need basis and are generally not captured as a totally different demand stream. Dependent Demand: This demand is generally referred to as Assembly Demand. As the name suggests, it is a demand driven by the Bill of Material (BOM) based on the product delivery schedule. The order for this stream is driven on two modes of replenishment, they are Period of Supply POS 2 Bin replenishment system. This demand is triggered from the respective production or assembly shops. Warehouse2: This warehouse is customer facing. It supports demands for the various product portfolios across the entire material class and also make/buy parts. The demand is purely driven by Page 4 of 10

5 after market parts. Hence this demand is forecasted. The stocking for parts at this warehouse is done statistically using various soft wares. Customers: The customers can be broadly classified into two categories Internal Customers These customers are internal company customers. External Customers These Customers are those who contribute to more than revenues. Suppliers: The suppliers are those who supply Buy parts to the Warehouse1. There are roughly around more than 275 suppliers, who provide materials to warehouse1. Most of the components that are procured are Make to Order or Assemble to Order, hence the leadtimes for these parts vary from 1 day to as much as 800 days for a few parts. We would be concentrating on the stocking policies and delinquency (Stock out) analysis from Warehouse1 perspective. Delinquency Analysis: Data: The data was extracted out of the various legacy systems for the past 2 years. The data was treated for missing values with the help of process experts. The dependent variables that were considered include: Findings: Commodity Type Supplier Forecast type Lead Time Standard Cost Safety Stock Replenishment Lot Quantity (RLQ) 2 Bin Quantity Period of Supply Quantity 1. Higher the cost of commodities, higher the probability of them getting delinquent. Conversely, lower the lead times for machined parts, higher the probability of parts getting delinquent. 2. Through various analyses, it was evident that specific suppliers were poor performers compared to others when it came to specific commodities. We determined that the average Service level across suppliers was as low as 31%. From the value chain (WH1 to WH2) perspective, 3. A significant number of parts, which caused production stoppages, had lower Safety Stock (SS) & Lot ordering Quantity. Significant percentages of parts at Warehouse1 do not have a Safety stock defined for them and have a RLQ greater than Warehouse1 Safety Stock. Page 5 of 10

6 4. We were able to statistically prove that Warehouse1 outbound service level is directly proportional to Inbound Service level. Strategy: Based on the various statistical tools like Corelation analysis and Paretto, the following 2paced strategy was finalized upon 1. Review Safety Stock Setting and Order Policy setting for parts based out of Warehouse1. 2. LeadTime not to be scoped in the analysis. Order Policy: Safety Stock Calculation: 1. Check for normality of data. (P >=0.05). 2. ABC classification based on multiple criteria. (Refer Table1) Traditional Approach Standard Cost * Total Demand Pitfalls: 1. No weights attached to MonthMonth demand Consistency 2. Actual MonthMonth Demand, Not considered Table1 Our Approach Non Normal Data Class C Std Cost * Median Demand * Number of Non Zero Demand Points Advantages: Median Demand: Central demand in the data collection. Most likely demand. NonZero demand point: Consistency in Demand over a longer time horizon. Parts having less than 3 demand hits in the past 2 years, are categorized as Class C Page 6 of 10

7 ABC Curve was drawn based in this analogy: (Refer Fig6) Fig6 ABC Curve for parts sourced out of Warehouse1 Page 7 of 10

8 The ABC curve was validated against the actual outbound delivery from Warehouse1. The details of the same have been referred to in table2 Parts Classification Data A Parts Delivery Activity in % 98% Count of Line Items in % 31% Activity Cost in dollars % 68% B Parts Delivery Activity 96% Count of Line Items 24% Activity Cost in dollars % 15% C Parts Delivery Activity 60% Count of Line Items 45% Activity Cost in dollars % 17% Percentages Table2 This clearly demonstrated that Class A Provided more dollar usage from the business perspective. However, Class C Provided more Line Items. Ignoring Class C would affect Service Level. Solution: Several Statistical & Heuristic approaches, which were appropriate for the specific business, were put into practice to arrive at an optimal solution for Inventory and Service Level. Replenishment Logic: The analysis proposes the usage of FOQ & POS based on the requirements of the case. FOQ is considered to be a derivative of Economic Order Quantity (EOQ) and POS is a derivative of dynamic lot sizing. The basic assumptions for the logic being that 1. For EOQ, the demand is uniform and is deterministic. 2. Leadtime is zero. Page 8 of 10

9 We conducted Regression analysis and One way analysis of variance to determine that 1. Parts with 12 demand points, we use EOQ. 2. For remaining parts, we used dynamic lot sizing (POS). Conclusion: Parts having Lower replenishment frequency to supplier would have lower probability of customer order delinquency. Based on the analysis, to reduce replenishment frequencies for delinquent parts we came up with the logic for FOQ setting. Group A: Order Quantity = Calculated EOQ Group B: Order Quantity = POS = 20 days Group C: Order Quantity = POS = 65 Days for first 5000 Parts (rest all at 6 months of Demand) We suggested capping on the calculated FOQ based on some business constraints and logic. Impact Analysis: We were able to recommend improvements across the parts portfolio sourced out of ware house1. Approximately 30,000 parts were reclassified into A, B or C based on the criteria proposed in table1. Also we found that the recommended strategy resulted in 1. An increment of service level by 10% by adding inventory appropriately across different parts portfolio % parts now had a defined Safety stock as against the earlier 74% % parts had an increase in Safety Stock against the earlier Safety Stock % (Weighted Average) increased coverage for delinquent parts. Conclusion: In a specific Supply Chain situation, the fall in service level is caused by various factors ranging from Suppliers to Stocking policies and ordering policies. The business needs to understand all the factors that influence this issue, in a detailed fashion. Supply Chain Analytics plays a critical role in understanding these factors and suggests ways to tackle them, using statistics as a driver. Page 9 of 10

10 About the Authors: Sidhartha Shishoo is Vice President and Master Black Belt at Genpact. He has nearly ten years of industry and consulting experience and has helped several Fortune 500 customers redesign their supply chain processes. Prashanth Navalgund is a senior consultant in the Supply Chain Services Group, Genpact. He has nearly six years of experience in various Supply Chain functions & Project Management. About Genpact: Genpact (NYSE: G ) provides a wide range of services, including Finance & Accounting, Collections and Customer Service, Insurance, Supply Chain & Procurement, Analytics, Enterprise Application, IT Infrastructure and Management. Genpact manages complex processes in multiple geographic regions, delivering its services from a global network of more than 30 operations centers in nine countries. Genpact global delivery centers are located in India, China, Hungary, Mexico, the Philippines, the Netherlands, Romania, Spain and the United States. References: Performance Contracting in AfterSales Service Supply Chains, by SangHyun Kim, Morris A. Cohen, and Serguei Netessine, The Wharton School, University of Pennsylvania. Accenture Service Management: Building Profits after the Sale. Supply Chain Management Perspectives. Designing and Managing the Supply Chain. (ISBN: ) David SimchiLevi (Northwestern University, Evanston, Illinios) Philip Kaminsky (University of California, Berkley) Edith SimchiLevi (LogicTools,Inc.,Northbrook,Illionis) Production: Planning, Control, and Integration. (ISBN: x) Daniel Siper (Dept. of IE, Tel Aviv University) Robert L. Buffin, Jr (Dept. of Industrial and Systems Engg. Auburn University) Business Intelligence Delivering Business Value through Supply Chain Analytics, by Sandeep kumar and Sourabh Deshmukh Page 10 of 10