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LESSON 16: INVENTORY MODELS (STOCHASTIC) Outline • Single-Period Models – Discrete Demand – Continuous Demand, Uniform Distribution – Continuous Demand, Normal Distribution • Multi-Period Models – Given a Q, R Policy, Find Cost – Optimal Q, R Policy without Service Constraint – Optimal Q, R Policy with Type 1 Service Constraint – Optimal Q, R Policy with Type 2 Service Constraint Stochastic Inventory Control Models Inventory Control with Uncertain Demand • In Lessons 16-20 we shall discuss the stochastic inventory control models assuming that the exact demand is not known. However, some demand characteristics such as mean, standard deviation and the distribution of demand are assumed to be known. • Penalty cost, p: Shortages occur when the demand exceeds the amount of inventory on hand. For each unit of unfulfilled demand, a penalty cost of p is charged. One source of penalty cost is the loss of profit. For example, if an item is purchased at $1.50 and sold at $3.00, the loss of profit is $3.00-1.50 = $1.50 for each unit of demand not fulfilled. Stochastic Inventory Control Models Inventory Control with Uncertain Demand • More on penalty cost, p – Penalty cost is estimated differently in different situation. There are two cases: 1. Backorder - if the excess demand is backlogged and fulfilled in a future period, a backorder cost is charged. Backorder cost is estimated from bookkeeping, delay costs, goodwill etc. 2. Lost sales - if the excess demand is lost because the customer goes elsewhere, the lost sales is charged. The lost sales include goodwill and loss of profit margin. So, penalty cost = selling price - unit variable cost + goodwill, if there exists any goodwill. Single- and Multi- Period Models • Stochastic models are classified into single- and multiperiod models. • In a single-period model, items are received in the beginning of a period and sold during the same period. The unsold items are not carried over to the next period. • The unsold items may be a total waste, or sold at a reduced price, or returned to the producer at some price less than the original purchase price. • The revenue generated by the unsold items is called the salvage value. Single- and Multi- Period Models • Following are some products for which a single-period model may be appropriate: – Computer that will be obsolete before the next order – Perishable products – Seasonal products such as bathing suits, winter coats, etc. – Newspaper and magazine • In the single-period model, there remains only one question to answer: how much to order. Single- and Multi- Period Models • In a multi-period model, all the items unsold at the end of one period are available in the next period. • If in a multi-period model orders are placed at regular intervals e.g., once a week, once a month, etc, then there is only one question to answer: how much to order. • However, we discuss Q, R models in which it is assumed that an order may be placed anytime. So, as usual, there are two questions: how much to order and when to order. Single-Period Models • In the single-period model, there remains only one question to answer: how much to order. • An intuitive idea behind the solution procedure will now be given: Consider two items A and B. – Item A • Selling price $900 • Purchase price $500 • Salvage value $400 – Item B • Selling price $600 • Purchase price $500 • Salvage value $100 Single-Period Models • Item A – Loss resulting from unsold items = 500-400=$100/unit – Profit resulting from items sold = $900-500=$400/unit • Item B – Loss resulting from unsold items = 500-100=$400/unit – Profit resulting from items sold = $600-500=$100/unit • If the demand forecast is the same for both the items, one would like to order more A and less B. • In the next few slides, a solution procedure is discussed that is consistent with this intuitive reasoning. Single-Period Models • First define two terms: • Loss resulting from the items unsold (overage cost) co= Purchase price - Salvage value • Profit resulting from the items sold (underage cost) cu = Selling price - Purchase price • The Question Given costs of overestimating/underestimating demand and the probabilities of various demand sizes how many units will be ordered? Single-Period Models (Discrete Demand) Decision Rule • Demand may be discrete or continuous. The demand of computer, newspaper, etc. is usually an integer. Such a demand is discrete. On the other hand, the demand of gasoline is not restricted to integers. Such a demand is continuous. Often, the demand of perishable food items such as fish or meat may also be continuous. • Consider an order quantity Q • Let p = probability (demand<Q) = probability of not selling the Qth item. • So, (1-p) = probability of selling the Qth item. Single-Period Models (Discrete Demand) Decision Rule • Expected loss from the Qth item = pco • Expected profit from the Qth item = 1 p cu • So, the Qth item should be ordered if pco (1 p )cu cu or , p co cu • Decision Rule (Discrete Demand): – Order maximum quantity Q such that cu p co cu where p = probability (demand<Q) Single-Period Models (Discrete Demand) Example 1: Demand for cookies: Demand Probability of Demand 1,800 dozen 0.05 2,000 0.10 Note: The demand is 2,200 0.20 discrete. So, the demand 2,400 0.30 cannot be other numbers 2,600 0.20 e.g., 1900 dozens, etc. 2,800 0.10 3,000 0,05 Selling price=$0.69, cost=$0.49, salvage value=$0.29 a. Construct a table showing the profits or losses for each possible quantity (Self study) b. What is the optimal number of cookies to make? c. Solve the problem by marginal analysis. Self Study Single-Period Models (Discrete Demand) Demand Prob (dozen) (Demand) 1800 2000 2200 2400 2600 2800 3000 Prob Expected Revenue Revenue Total (Selling Number From From Revenue all the units) Sold Sold Unsold Items Items Cost Profit 0.05 0.1 0.2 0.3 0.2 0.1 0.05 a. Sample computation for order quantity = 2200: Expected number sold = 1800Prob(demand=1800) +2000Prob(demand=2000) + 2200Prob(demand2200) = 1800(0.05)+2000(0.1)+2200(0.2+0.3+0.2+0.1+0.05) = 1800(0.05)+2000(0.1)+2200(0.85) = 2160 Revenue from sold items=2160(0.69)=$1490.4 Revenue from unsold items=(2200-2160)(0.29)=$11.6 Self Study Single-Period Models (Discrete Demand) Demand Prob (dozen) (Demand) 1800 2000 2200 2400 2600 2800 3000 0.05 0.1 0.2 0.3 0.2 0.1 0.05 Prob Expected Revenue Revenue Total (Selling Number From From Revenue all the units) Sold Sold Unsold Items Items 1 1800 1242.0 0.0 1242 0.95 1990 1373.1 2.9 1376 0.85 2160 1490.4 11.6 1502 0.65 2290 1580.1 31.9 1612 0.35 2360 1628.4 69.6 1698 0.15 2390 1649.1 118.9 1768 0.05 2400 1656.0 174.0 1830 Cost 882 980 1078 1176 1274 1372 1470 Profit 360 396 424 436 424 396 360 a. Sample computation for order quantity = 2200: Total revenue=1490.4+11.6=$1502 Cost=2200(0.49)=$1078 Profit=1502-1078=$424 b. From the above table the expected profit is maximized for an order size of 2,400 units. So, order 2,400 units. Single-Period Models (Discrete Demand) c. Solution by marginal analysis: cu co Order maximum quantity, Q such that cu p Probabilitydemand Q cu c0 Demand, Q Probability(demand) Probability(demand<Q), p 1,800 dozen 0.05 2,000 0.10 2,200 0.20 2,400 0.30 2,600 0.20 2,800 0.10 3,000 0,05 Single-Period Models (Discrete Demand) • In the previous slide, one may draw a horizontal line that separates the demand values in two groups. Above the line, the values in the last column are not more than cu cu c0 The optimal solution is the last demand value above the line. So, optimal solution is 2,400 units. Continuous Distribution • Often the demand is continuous. Even when the demand is not continuous, continuous distribution may be used because the discrete distribution may be inconvenient. • For example, suppose that the demand of calendar can vary between 150 to 850 units. If demand varies so widely, a continuous approximation is more convenient because discrete distribution will involve a large number of computation without any significant increase in accuracy. • We shall discuss two distributions: – Uniform distribution – Continuous distribution Continuous Distribution • First, an example on continuous approximation. • Suppose that the historical sales data shows: Quantity No. Days sold Quantity No. Days sold 14 1 21 11 15 2 22 9 16 3 23 6 17 6 24 3 18 9 25 2 19 11 26 1 20 12 Continuous Distribution • A histogram is constructed with the above data and shown in the next slide. The data shows a good fit with the normal distribution with mean = 20 and standard deviation = 2.49. • There are some statistical tests, e.g., Chi-Square test, that can determine whether a given frequency distribution has a good fit with a theoretical distribution such as normal distribution, uniform distribution, etc. There are some software, e.g., Bestfit, that can search through a large number of theoretical distributions and choose a good one, if there exists any. This topic is not included in this course. Continuous Distribution Mean = 20 Standard deviation = 2.49 Continuous Distribution The figure below shows an example of uniform distribution. Single-Period Models (Continuous Demand) Decision Rule • The decision rule for continuous demand is similar to the decision rule for the discrete demand • Decision Rule (Continuous Demand): – Order quantity Q such that cu p co cu where p = probability (demandQ) – Note the following difference • The word “maximum” • The “= “ in the formula for p • The “ “ in the definition of p Single-Period Models (Continuous Demand) Decision Rule • There is a nice pictorial interpretation of the decision rule. Although we shall discuss the interpretation in terms of normal and uniform distributions, the interpretation is similar for all the other continuous distributions. • The area under the curve is 1.00. Identify the vertical line that splits the area into two parts with areas cu p on the left and co cu co 1 p on the right co cu The order quantity corresponding to the vertical line is optimal. Single-Period Models (Continuous Demand) Decision Rule Probability • Pictorial interpretation of the decision rule for uniform distribution (see Examples 2, 3 and 4 for application of the rule): Area p 1 p cu co cu co co cu Area 150 Q* Demand 850 Single-Period Models (Continuous Demand) Decision Rule Probability • Pictorial interpretation of the decision rule for normal distribution (see Examples 3 and 4 for application of the rule): Area Area p 1 p cu co cu Q* Demand co co cu Single-Period Models (Continuous Demand) Example 2: The J&B Card Shop sells calendars. The oncea-year order for each year’s calendar arrives in September. The calendars cost $1.50 and J&B sells them for $3 each. At the end of July, J&B reduces the calendar price to $1 and can sell all the surplus calendars at this price. How many calendars should J&B order if the September-to-July demand can be approximated by a. uniform distribution between 150 and 850 Single-Period Models (Continuous Demand) Solution to Example 2: Overage cost co = Purchase price - Salvage value = Underage cost cu = Selling price - Purchase price = Single-Period Models (Continuous Demand) p cu co cu = Now, find the Q so that p = probability(demand<Q) = Probability Q* = a+p(b-a) = Area = Area = 150 Demand Q* 850 Single-Period Models (Continuous Demand) Example 3: The J&B Card Shop sells calendars. The oncea-year order for each year’s calendar arrives in September. The calendars cost $1.50 and J&B sells them for $3 each. At the end of July, J&B reduces the calendar price to $1 and can sell all the surplus calendars at this price. How many calendars should J&B order if the September-to-July demand can be approximated by b. normal distribution with = 500 and =120. Single-Period Models (Continuous Demand) Solution to Example 3: co =$0.50, cu =$1.50 (see Example 2) p cu 1.50 = = 0.75 co cu 1.50 0.50 Single-Period Models (Continuous Demand) Now, find the Q so that p = 0.75 Self Study Single-Period Models (Continuous Demand) Example 4: A retail outlet sells a seasonal product for $10 per unit. The cost of the product is $8 per unit. All units not sold during the regular season are sold for half the retail price in an end-of-season clearance sale. Assume that the demand for the product is normally distributed with = 500 and = 100. a. What is the recommended order quantity? b. What is the probability of a stockout? c. To keep customers happy and returning to the store later, the owner feels that stockouts should be avoided if at all possible. What is your recommended quantity if the owner is willing to tolerate a 0.15 probability of stockout? d. Using your answer to part (c), what is the goodwill cost you are assigning to a stockout? Self Study Single-Period Models (Continuous Demand) Solution to Example 4: a. Selling price=$10, Purchase price=$8 Salvage value=10/2=$5 cu =10 - 8 = $2, co = 8-10/2 = $3 Now, find the Q so that p = 0.4 or, area (1) = 0.4 Look up Table A-1 for Area (2) = 0.5-0.4=0.10 Area=0.10 Probability cu 2 p = = 0.4 co cu 2 3 100 Area=0.40 (2) (1) (3) z = 0.255 =500 Demand Self Study Single-Period Models (Continuous Demand) z = 0.25 for area = 0.0987 z = 0.26 for area = 0.1025 So, z = 0.255 (take -ve, as p = 0.4 <0.5) for area = 0.10 So, Q*=+ z =500+(-0.255)(100)=474.5 units. b. P(stockout)=P(demandQ)=1-P(demand<Q)=1-p=1-0.4=0.6 Self Study c. P(stockout)=Area(3)=0.15 Look up Table A-1 for Area (2) = 0.5-0.15=0.35 z = 1.03 for area = 0.3485 z = 1.04 for area = 0.3508 Probability Single-Period Models (Continuous Demand) Area=0.35 100 Area=0.15 (2) (1) =500 Demand So, z = 1.035 for area = 0.35 So, Q*=+ z =500+(1.035)(100)=603.5 units. (3) z = 1.035 Self Study Single-Period Models (Continuous Demand) d. p=P(demand<Q)=1-P(demandQ) =1-P(stockout)=1-0.15=0.85 For a goodwill cost of g cu =10 - 8+g = 2+g, co = 8-10/2 = $3 cu 2 g Now, solve g in p = = =0.85 co cu (2 g ) 3 Hence, g=$15. READING AND EXERCISES Lesson 16 Reading: Section 5.1 - 5.3 , pp. 245-254 (4th Ed.), pp. 232-245 (5th Ed.) Exercise: 8a, 12a, and 12b, pp. 256-258 (4th Ed.), pp. 248-250 (5th Ed.)