Role Of Inventory In The Supply Chain

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  • Role Of Inventory In The Supply Chain

    1. 1. Role of Inventory in the Supply Chain <ul><li>Overstocking: Amount available exceeds demand </li></ul><ul><ul><li>Liquidation, Obsolescence, Holding </li></ul></ul><ul><li>Understocking: Demand exceeds amount available </li></ul><ul><ul><li>Lost margin and future sales </li></ul></ul><ul><li>Goal: Matching supply and demand </li></ul>
    2. 2. Understanding Inventory <ul><li>The inventory policy is affected by: </li></ul><ul><ul><li>Demand Characteristics </li></ul></ul><ul><ul><li>Lead Time </li></ul></ul><ul><ul><li>Number of Products </li></ul></ul><ul><ul><li>Objectives </li></ul></ul><ul><ul><ul><li>Service level </li></ul></ul></ul><ul><ul><ul><li>Minimize costs </li></ul></ul></ul><ul><ul><li>Cost Structure </li></ul></ul>
    3. 3. Cost Structure <ul><li>Order costs </li></ul><ul><ul><li>Fixed </li></ul></ul><ul><ul><li>Variable </li></ul></ul><ul><li>Holding Costs </li></ul><ul><ul><li>Insurance </li></ul></ul><ul><ul><li>Maintenance and Handling </li></ul></ul><ul><ul><li>Taxes </li></ul></ul><ul><ul><li>Opportunity Costs </li></ul></ul><ul><ul><li>Obsolescence </li></ul></ul>
    4. 4. EOQ: A View of Inventory* Time Inventory Order Size Note: • No Stockouts • Order when no inventory • Order Size determines policy Avg. Inventory
    5. 5. EOQ:Total Cost* Total Cost Order Cost Holding Cost
    6. 6. EOQ: Calculating Total Cost* <ul><li>Purchase Cost Constant </li></ul><ul><li>Holding Cost: (Avg. Inven) * (Holding Cost) </li></ul><ul><li>Ordering (Setup Cost): Number of Orders * Order Cost </li></ul><ul><li>Goal: Find the Order Quantity that Minimizes These Costs: </li></ul>
    7. 7. Fixed costs: Optimal Lot Size and Reorder Interval (EOQ) <ul><li>R: Annual demand </li></ul><ul><li>S: Setup or Order Cost </li></ul><ul><li>C: Cost per unit </li></ul><ul><li>h: Holding cost per year as a fraction of product cost </li></ul><ul><li>H: Holding cost per unit per year </li></ul><ul><li>Q: Lot Size </li></ul><ul><li>T: Reorder interval </li></ul>
    8. 8. Example <ul><li>Demand, R = 12,000 computers per year </li></ul><ul><li>Unit cost, C = $500 </li></ul><ul><li>Holding cost, h = 0.2 </li></ul><ul><li>Fixed cost, S = $4,000/order </li></ul><ul><li>Q = 980 computers </li></ul><ul><li>Cycle inventory = Q/2 = 490 </li></ul><ul><li>Flow time = Q/2R = 0.49 month </li></ul><ul><li>Reorder interval, T = 0.98 month </li></ul>
    9. 9. EOQ: Another Example <ul><li>Book Store Mug Sales </li></ul><ul><ul><li>Demand is constant, at 20 units a week </li></ul></ul><ul><ul><li>Fixed order cost of $12.00, no lead time </li></ul></ul><ul><ul><li>Holding cost of 25% of inventory value annually </li></ul></ul><ul><ul><li>Mugs cost $1.00, sell for $5.00 </li></ul></ul><ul><li>Question </li></ul><ul><ul><li>How many, when to order? </li></ul></ul>
    10. 10. EOQ: Important Observations* <ul><li>Tradeoff between set-up costs and holding costs when determining order quantity. In fact, we order so that these costs are equal per unit time </li></ul><ul><li>Total Cost is not particularly sensitive to the optimal order quantity </li></ul>
    11. 11. The Effect of Demand Uncertainty <ul><li>Most companies treat the world as if it were predictable: </li></ul><ul><ul><li>Production and inventory planning are based on forecasts of demand made far in advance of the selling season </li></ul></ul><ul><ul><li>Companies are aware of demand uncertainty when they create a forecast, but they design their planning process as if the forecast truly represents reality </li></ul></ul><ul><li>Recent technological advances have increased the level of demand uncertainty: </li></ul><ul><ul><li>Short product life cycles </li></ul></ul><ul><ul><li>Increasing product variety </li></ul></ul>
    12. 12. Example: SnowTime Sporting Goods <ul><li>Fashion items have short life cycles, high variety of competitors </li></ul><ul><li>SnowTime Sporting Goods </li></ul><ul><ul><li>New designs are completed </li></ul></ul><ul><ul><li>One production opportunity </li></ul></ul><ul><ul><li>Based on past sales, knowledge of the industry, and economic conditions, the marketing department has a probabilistic forecast </li></ul></ul><ul><ul><li>The forecast averages about 13,000, but there is a chance that demand will be greater or less than this. </li></ul></ul>
    13. 13. SnowTime Demand Scenarios
    14. 14. SnowTime Costs <ul><li>Production cost per unit (C): $80 </li></ul><ul><li>Selling price per unit (S): $125 </li></ul><ul><li>Salvage value per unit (V): $20 </li></ul><ul><li>Fixed production cost (F): $100,000 </li></ul><ul><li>Q is production quantity, D demand </li></ul><ul><li>Profit = Revenue - Variable Cost - Fixed Cost + Salvage </li></ul>
    15. 15. SnowTime Scenarios <ul><li>Scenario One: </li></ul><ul><ul><li>Suppose you make 12,000 jackets and demand ends up being 13,000 jackets. </li></ul></ul><ul><ul><li>Profit = 125(12,000) - 80(12,000) - 100,000 = $440,000 </li></ul></ul><ul><li>Scenario Two: </li></ul><ul><ul><li>Suppose you make 12,000 jackets and demand ends up being 11,000 jackets. </li></ul></ul><ul><ul><li>Profit = 125(11,000) - 80(12,000) - 100,000 + 20(1000) = $ 335,000 </li></ul></ul>
    16. 16. SnowTime Best Solution <ul><li>Find order quantity that maximizes weighted average profit. </li></ul><ul><li>Question: Will this quantity be less than, equal to, or greater than average demand? </li></ul>
    17. 17. (s, S) Policies <ul><li>For some starting inventory levels, it is better to not start production </li></ul><ul><li>If we start, we always produce to the same level </li></ul><ul><li>Thus, we use an (s,S) policy. If the inventory level is below s , we produce up to S . </li></ul><ul><li>s is the reorder point, and S is the order-up-to level </li></ul><ul><li>The difference between the two levels is driven by the fixed costs associated with ordering, transportation, or manufacturing </li></ul>
    18. 18. Notation <ul><li>AVG = average daily demand </li></ul><ul><li>STD = standard deviation of daily demand </li></ul><ul><li>LT = replenishment lead time in days </li></ul><ul><li>h = holding cost of one unit for one day </li></ul><ul><li>SL = service level (for example, 95%). This implies that the probability of stocking out is 100%-SL (for example, 5%) </li></ul><ul><li>Also, the Inventory Position at any time is the actual inventory plus items already ordered, but not yet delivered. </li></ul>
    19. 19. Analysis <ul><li>The reorder point has two components: </li></ul><ul><ul><li>To account for average demand during lead time: LT  AVG </li></ul></ul><ul><ul><li>To account for deviations from average (we call this safety stock ) z  STD   LT where z is chosen from statistical tables to ensure that the probability of stockouts during leadtime is 100%-SL. </li></ul></ul>
    20. 20. Example <ul><li>The distributor has historically observed weekly demand of: AVG = 44.6 ; STD = 32.1 Replenishment lead time is 2 weeks, and desired service level SL = 97% </li></ul><ul><li>Average demand during lead time is: 44.6  2 = 89.2 </li></ul><ul><li>Safety Stock is: 1.88  32.1   2 = 85.3 </li></ul><ul><li>Reorder point is thus 175, or about 3.9 weeks of supply at warehouse and in the pipeline </li></ul>
    21. 21. Model Two : Fixed Costs* <ul><li>In addition to previous costs, a fixed cost K is paid every time an order is placed. </li></ul><ul><li>We have seen that this motivates an (s,S) policy, where reorder point and order quantity are different. </li></ul><ul><li>The reorder point will be the same as the previous model, in order to meet meet the service requirement: s = LT  AVG + z  AVG   L </li></ul><ul><li>What about the order up to level? </li></ul>
    22. 22. Model Two : The Order-Up-To Level* <ul><li>We have used the EOQ model to balance fixed, variable costs: Q=  (2  K  AVG)/h </li></ul><ul><li>If there was no variability in demand, we would order Q when inventory level was at LT  AVG . Why? </li></ul><ul><li>There is variability, so we need safety stock z  AVG *  LT </li></ul><ul><li>The total order-up-to level is: S=max{Q, [LT  AVG+ z  AVG *  LT]} </li></ul>
    23. 23. Model Two : Example* <ul><li>Consider the previous example, but with the following additional info: </li></ul><ul><ul><li>fixed cost of $4500 when an order is placed </li></ul></ul><ul><ul><li>$250 product cost </li></ul></ul><ul><ul><li>holding cost 18% of product </li></ul></ul><ul><li>Weekly holding cost: h = (.18  250) / 52 = 0.87 </li></ul><ul><li>Order quantity Q=  (2  4500  44.6 / 0.87 = 679 </li></ul><ul><li>Order-up-to level: s + Q = 85 + 679 = 765 </li></ul>
    24. 24. What to Make? <ul><li>Question: Will this quantity be less than, equal to, or greater than average demand? </li></ul><ul><li>Average demand is 13,100 </li></ul><ul><li>Look at marginal cost Vs. marginal profit </li></ul><ul><ul><li>if extra jacket sold, profit is 125-80 = 45 </li></ul></ul><ul><ul><li>if not sold, cost is 80-20 = 60 </li></ul></ul><ul><li>So we will make less than average </li></ul>
    25. 25. SnowTime Expected Profit
    26. 26. SnowTime Expected Profit
    27. 27. SnowTime: Important Observations <ul><li>Tradeoff between ordering enough to meet demand and ordering too much </li></ul><ul><li>Several quantities have the same average profit </li></ul><ul><li>Average profit does not tell the whole story </li></ul><ul><li>Question: 9000 and 16000 units lead to about the same average profit, so which do we prefer? </li></ul>
    28. 28. Key Points from this Model <ul><li>The optimal order quantity is not necessarily equal to average forecast demand </li></ul><ul><li>The optimal quantity depends on the relationship between marginal profit and marginal cost </li></ul><ul><li>As order quantity increases, average profit first increases and then decreases </li></ul><ul><li>As production quantity increases, risk increases. In other words, the probability of large gains and of large losses increases </li></ul>
    29. 29. Initial Inventory <ul><li>Suppose that one of the jacket designs is a model produced last year. </li></ul><ul><li>Some inventory is left from last year </li></ul><ul><li>Assume the same demand pattern as before </li></ul><ul><li>If only old inventory is sold, no setup cost </li></ul><ul><li>Question: If there are 7000 units remaining, what should SnowTime do? What should they do if there are 10,000 remaining? </li></ul>
    30. 30. Initial Inventory and Profit
    31. 31. Periodic Review Policy <ul><li>Each review echelon, inventory position is raised to the base-stock level. </li></ul><ul><li>The base-stock level includes two components: </li></ul><ul><ul><li>Average demand during r+L days (the time until the next order arrives): (r+L)*AVG </li></ul></ul><ul><ul><li>Safety stock during that time: z*STD*  r+L </li></ul></ul>
    32. 32. Inventory Management: Best Practice <ul><li>Periodic inventory review policy (59%) </li></ul><ul><li>Tight management of usage rates, lead times and safety stock (46%) </li></ul><ul><li>ABC approach (37%) </li></ul><ul><li>Reduced safety stock levels (34%) </li></ul><ul><li>Shift more inventory, or inventory ownership, to suppliers (31%) </li></ul><ul><li>Quantitative approaches (33%) </li></ul>
    33. 33. Inventory Management: Best Practice <ul><li>Periodic inventory reviews </li></ul><ul><li>Tight management of usage rates, lead times and safety stock </li></ul><ul><li>ABC approach </li></ul><ul><li>Reduced safety stock levels </li></ul><ul><li>Shift more inventory, or inventory ownership, to suppliers </li></ul><ul><li>Quantitative approaches </li></ul>
    34. 34. Changes In Inventory Turnover <ul><li>Inventory turnover ratio = annual sales/avg. inventory level </li></ul><ul><li>Inventory turns increased by 30% from 1995 to 1998 </li></ul><ul><li>Inventory turns increased by 27% from 1998 to 2000 </li></ul><ul><li>Overall the increase is from 8.0 turns per year to over 13 per year over a five year period ending in year 2000. </li></ul>
    35. 35. Inventory Turnover Ratio
    36. 36. Factors that Drive Reduction in Inventory <ul><li>Top management emphasis on inventory reduction (19%) </li></ul><ul><li>Reduce the Number of SKUs in the warehouse (10%) </li></ul><ul><li>Improved forecasting (7%) </li></ul><ul><li>Use of sophisticated inventory management software (6%) </li></ul><ul><li>Coordination among supply chain members (6%) </li></ul><ul><li>Other </li></ul>
    37. 37. Factors that Drive Inventory Turns Increase <ul><li>Better software for inventory management (16.2%) </li></ul><ul><li>Reduced lead time (15%) </li></ul><ul><li>Improved forecasting (10.7%) </li></ul><ul><li>Application of SCM principals (9.6%) </li></ul><ul><li>More attention to inventory management (6.6%) </li></ul><ul><li>Reduction in SKU (5.1%) </li></ul><ul><li>Others </li></ul>

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