Inventory Management and Control

Inventory Management and Control
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Inventory Defined Inventory is the stock of any item or resource held to meet future demand and can include: raw materials, finished products, component parts, supplies, and work-in-process 3

Inventory Classifications
Process stage Demand Type Number & Value Other Raw Material WIP Finished Goods Independent Dependent A Items B Items C Items Maintenance Operating

Independent vs. Dependent Demand
Independent Demand (Demand for the final end-product or demand not related to other items; demand created by external customers) Finished product Independent demand is uncertain Dependent demand is certain A Dependent Demand (Derived demand for component parts, subassemblies, raw materials, etc- used to produce final products) B(4) C(2) D(1) E(1) E(2) B(1) E(3) Component parts

Inventory Models Independent demand – finished goods, items that are ready to be sold E.g. a computer Dependent demand – components of finished products E.g. parts that make up the computer

Types of Inventories (1 of 2)
Raw materials & purchased parts Partially completed goods called work in progress Finished-goods inventories (manufacturing firms) or merchandise (retail stores)

Types of Inventories (2 of 2)
Replacement parts, tools, & supplies Goods-in-transit to warehouses or customers

The Material Flow Cycle (1 of 2)

The Material Flow Cycle (2 of 2)
Wait Time Move Time Queue Time Setup Time Run Time Input Output Cycle Time Run time: Job is at machine and being worked on Setup time: Job is at the work station, and the work station is being "setup." Queue time: Job is where it should be, but is not being processed because other work precedes it. Move time: The time a job spends in transit Wait time: When one process is finished, but the job is waiting to be moved to the next work area.

Performance Measures Inventory turnover (the ratio of annual cost of goods sold to average inventory investment) Days of inventory on hand (expected number of days of sales that can be supplied from existing inventory)

Functions of Inventory (1 of 2)
To “decouple” or separate various parts of the production process, ie. to maintain independence of operations To meet unexpected demand & to provide high levels of customer service To smooth production requirements by meeting seasonal or cyclical variations in demand To protect against stock-outs

Functions of Inventory (2 of 2)
5. To provide a safeguard for variation in raw material delivery time 6. To provide a stock of goods that will provide a “selection” for customers 7. To take advantage of economic purchase-order size 8. To take advantage of quantity discounts 9. To hedge against price increases

Disadvantages of Inventory
Higher costs Item cost (if purchased) Holding (or carrying) cost Difficult to control Hides production problems May decrease flexibility

Inventory Costs Holding (or carrying) costs
Costs for storage, handling, insurance, etc Setup (or production change) costs Costs to prepare a machine or process for manufacturing an order, eg. arranging specific equipment setups, etc Ordering costs (costs of replenishing inventory) Costs of placing an order and receiving goods Shortage costs Costs incurred when demand exceeds supply 5

Holding (Carrying) Costs
Obsolescence Insurance Extra staffing Interest Pilferage Damage Warehousing Etc.

Inventory Holding Costs (Approximate Ranges)
Cost as a % of Inventory Value 6% (3 - 10%) 3% ( %) (3 - 5%) 11% (6 - 24%) (2 - 5%) 26% Category Housing costs (building rent, depreciation, operating cost, taxes, insurance) Material handling costs (equipment, lease or depreciation, power, operating cost) Labor cost from extra handling Investment costs (borrowing costs, taxes, and insurance on inventory) Pilferage, scrap, and obsolescence Overall carrying cost

Ordering Costs Supplies Forms Order processing Clerical support etc.

Setup Costs Clean-up costs Re-tooling costs Adjustment costs etc.

Shortage Costs Backordering cost Cost of lost sales

Inventory Control System Defined
An inventory system is the set of policies and controls that monitor levels of inventory and determine what levels should be maintained, when stock should be replenished and how large orders should be Answers questions as: When to order? How much to order?

Objective of Inventory Control
To achieve satisfactory levels of customer service while keeping inventory costs within reasonable bounds Improve the Level of customer service Reduce the Costs of ordering and carrying inventory

Requirements of an Effective Inventory Management
A system to keep track of inventory A reliable forecast of demand Knowledge of lead times Reasonable estimates of Holding costs Ordering costs Shortage costs A classification system

Inventory Counting (Control) Systems
Periodic System Physical count of items made at periodic intervals; order is placed for a variable amount after fixed passage of time. Perpetual (Continuous) Inventory System System that keeps track of removals from inventory continuously, thus monitoring current levels of each item (constant amount is ordered when inventory declines to a predetermined level)

Inventory Models Single-Period Inventory Model
One time purchasing decision (Examples: selling t-shirts at a football game, newspapers, fresh bakery products, fresh flowers) Seeks to balance the costs of inventory over stock and under stock Multi-Period Inventory Models Fixed-Order Quantity Models Event triggered (Example: running out of stock) Fixed-Time Period Models Time triggered (Example: Monthly sales call by sales representative) 7

Single-Period Inventory Model

Single-Period Inventory Model
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.

3 (Newsboy Problem) Single period model: It is used to handle ordering of perishables (fresh fruits, flowers) and other items with limited useful lives (newspapers, spare parts for specialized equipment).

Shortage cost (Cost of Understocking)
Shortage cost: generally, this cost represents unrealized profit per unit (Cu=Revenue per unit – Cost per unit) If a shortage or stockout cost relates to a spare part for a machine, then shortage cost refers to the actual cost of lost production.

Excess cost (Cost of Over Stocking)
Excess cost (Ce): difference between purchase cost and salvage value of items left over at the end of a period. If there is a cost associated with disposing of excess items, the salvage cost will be negative.

Single Period Model Given the costs of overestimating/underestimating demand and the probabilities of various demand sizes the goal is to identify the order quantity or stocking level that will minimize the long-run excess (overstock)or shortage costs (understock).

Single-Period Models (Demand Distribution)
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)
Expected loss from the Qth item = Expected profit from the Qth item = So, the Qth item should be ordered if Decision Rule (Discrete Demand): Order maximum quantity Q such that where p = probability (demand<Q)

Single-Period Model The service level is the probability that demand will not exceed the stocking level. The service level determines the amount of stocking level to keep. 8

Optimal Stocking Level (Choosing optimum Stocking level to minimize these costs is similar to balancing a seesaw) Service level = Cs Cs + Ce Cs = Shortage cost per unit Ce = Excess cost per unit Service Level So Quantity Ce Cs Balance point

Service Level Another way to define ‘Service Level’ is:
proportion of cycles in which no stock-out occurs

Service Level Order Cycle Demand Stock-Outs
Total Since there are two cycles out of ten in which a stockout occurs, service level is 80%. This translates to a 96% fill rate. There are a total of 1,450 units demand and 55 stockouts (which means that 1,395 units of demand are satisfied).

Single Period Model (Demand is represented by a discrete distribution)
Unlike the continuous case where the optimal solution is found by determining So which makes the distribution function equal to the critical ratio cs / (cs + ce), in the discrete case, the critical ratio takes place between two values of F(So) or F(Q) The optimal So or Q corresponds to the higher value of F( So) or F(Q). (Note that, in the discrete case, the distribution function increases by jumps) SEE EXAMPLES 17 & 18 on page 576

Single-Period Models (Discrete Demand) Example : Demand for cookies:
Demand Probability of Demand 1,800 dozen 0.05 2, 2, 2, 2, 2, 3, ,05 Selling price=$0.69, cost=$0.49, salvage value=$0.29 What is the optimal number of cookies to make? c

Single-Period Models (Discrete Demand)
Cs= =$0.2, Ce= =$0.2 Order maximum quantity, Q such that Demand, Q Probability(demand) Probability(demand<Q), p 1,800 dozen 2, 2, 2, 2, 2, 3, ,

Single-Period Models (Continous Demand)
Often the demand is continuous. Even when the demand is not continuous, continuous distribution may be used because the discrete distribution may be inconvenient. We shall discuss two distributions: Uniform distribution Normal distribution

Single-Period Models (Continuous Demand)
Example 2: The J&B Card Shop sells calendars. The once-a-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

Overage cost ce = Purchase price - Salvage value =1.5-1=$0.5 Underage cost cs = Selling price - Purchase price =3-1.5=$1.5

Now, find the Q so that p = probability(demand<Q) =0.75 Q* = a+p(b-a) = ( )=675 Single-Period Models (Continuous Demand) Probability Area Area = = 150 Demand 850 Q *

Example 3: The J&B Card Shop sells calendars. The once-a-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.

Solution to Example 3: ce =$0.50, cu =$1.50 (see Example 2) p = = 0.75

Now, find the Q so that p = 0.75

Single-Period Models (Continuous Demand
Q= So = mean + zσ = (120) = 582

Single Period Example 15 (pg. 574) Demand is uniformly distributed
Ce = $0.20 per unit Cs = $0.60 per unit Service level = Cs/(Cs+Ce) = .6/(.6+.2) Service level = .75 Opt. Stock.Level=S0= ( )= 450 liters Service Level = 75% Quantity Ce Cs Stockout risk = 1.00 – 0.75 = 0.25

Uniform Distribution [Continuous Dist’n]
A random variable X is uniformly distributed on the interval (a,b), U(a,b), if its pdf and cdf are: Properties P(x1 < X < x2) is proportional to the length of the interval [F(x2) – F(x1) = (x2-x1)/(b-a)] E(X) = (a+b)/2 V(X) = (b-a)2/12 U(0,1) provides the means to generate random numbers, from which random variates can be generated.

Poisson Distribution [Discrete Dist’n]
Poisson distribution describes many random processes quite well and is mathematically quite simple. where a > 0, pdf and cdf are: E(X) = a = V(X)

Normal Distribution [Continuous Dist’n]
A normally distributed random variable X has the pdf: Mean: Variance: Denoted as X ~ N(m,s2) Special properties: symmetric about m. The maximum value of the pdf occurs at x = m; the mean and mode are equal.

Evaluating the distribution: Use numerical methods (no closed form) Independent of m and s, using the standard normal distribution: Z ~ N(0,1) Transformation of variables: let Z = (X - m) / s,

Example: The time required to load an oceangoing vessel, X, is distributed as N(12,4) The probability that the vessel is loaded in less than 10 hours: Using the symmetry property, F(1) is the complement of F (-1)

therefore we need 2,400 + .432(350) = 2,551 shirts
Single Period Model (Demand is represented by a continous distribution) Our college basketball team is playing in a tournament game this weekend. Based on our past experience we sell on average 2,400 shirts with a standard deviation of 350 and we can assume that demand for shirts is approximately normally distributed. We make $10 on every shirt we sell at the game, but lose $5 on every shirt not sold. What is the optimal stocking level for shirts? So =mean + zσ Cs = $10 and Ce = $5; P ≤ $10 / ($10 + $5) = .667 Z.667 = .432 therefore we need 2, (350) = 2,551 shirts

Multi-Period Inventory Models
Fixed-Order Quantity Models (Types of) Economic Order Quantity Model (EOQ) Economic Production Order Quantity (Economic Lot Size) Model (EPQ) Economic Order Quantity Model with Quantity Discounts Fixed Time Period (Fixed Order Interval) Models

Fixed Order Quantity Models: Economic Order Quantity Model

Economic Order Quantity Model Assumptions (1 of 2):
Demand for the product is known with certainty, it is constant and uniform throughout the period Lead time (time from ordering to receipt) is known and constant Price per unit of product is constant (no quantity discounts). So it is not included in the total cost. Inventory holding cost is based on average inventory

Economic Order Quantity Model Assumptions (2 of 2):
Ordering or setup costs are constant All demands for the product will be satisfied (no backorders are allowed) No stockouts (shortages) are allowed The order quantity is received all at once. (Instantaneous receipt of material in a single lot) The goal is to calculate the order quantitiy that minimizes total cost 9

Basic Fixed-Order Quantity Model and Reorder Point Behavior
4. The cycle then repeats. 1. You receive an order quantity Q. R = Reorder point Q = Economic order quantity L = Lead time L Q R Time Number of units on hand (Inv. Level) 2. You start using them up over time. 3. When you reach down to a level of inventory of R, you place your next Q sized order.

Average Inventory (Q/2)
EOQ Model Reorder Point (ROP) Time Inventory Level Average Inventory (Q/2) Lead Time Order Quantity (Q) Demand rate Order placed Order received

EOQ Cost Model: How Much to Order?
By adding the holding and ordering costs together, we determine the total cost curve, which in turn is used to find the optimal order quantity that minimizes total costs Slope = 0 Total Cost Order Quantity, Q Annual cost ($) Minimum total cost Optimal order Qopt Carrying Cost = HQ 2 Ordering Cost = SD Q

Why Holding Costs Increase?
More units must be stored if more are ordered Purchase Order Description Qty. Microwave 1000 Order quantity Purchase Order Description Qty. Microwave 1 Order quantity

Why Ordering Costs Decrease ?
Cost is spread over more units Example: You need 1000 microwave ovens Purchase Order Description Qty. Microwave 1 1000 Order (Postage $ 0.33) 1 Order (Postage $330) Order quantity 1000

Basic Fixed-Order Quantity (EOQ) Model Formula
TC=Total annual cost D =Annual demand C =Cost per unit Q =Order quantity S =Cost of placing an order or setup cost R =Reorder point L =Lead time H=Annual holding and storage cost per unit of inventory Total Annual = Cost Annual Purchase Cost Annual Ordering Cost Annual Holding Cost + + 12

EOQ Cost Model Using calculus, we take the first derivative of the total cost function with respect to Q, and set the derivative (slope) equal to zero, solving for the optimized (cost minimized) value of Qopt TC = S D Q H Q 2 = Q2 H TC Q 0 = Qopt = 2SD Deriving Qopt Annual ordering cost = S D Q Annual carrying cost = HQ 2 Total cost = H Q Proving equality of costs at optimal point = S D Q H Q 2 Q2 = 2S D H Qopt = 2 S D

Deriving the EOQ 1) How much to order? 2) When to order?
We also need a reorder point to tell us when to place an order 13

EOQ Model Equations = × Q* D S H N T d ROP L 2 Optimal Order Quantity
Expected Number of Orders Expected Time Between Orders Working Days / Year = × Q* D S H N T d ROP L 2

EOQ Example 1 (1 of 3) Given the information below, what are the EOQ and reorder point? Annual Demand = 1,000 units Days per year considered in average daily demand = 365 Cost to place an order = $10 Holding cost per unit per year = $2.50 Lead time = 7 days Cost per unit = $15 14

EOQ Example 1(2 of 3) In summary, you place an optimal order of 90 units. In the course of using the units to meet demand, place the next order of 90 units when you only have 20 units left. 15

EOQ Example I(3 of 3) Order cycle time= 365/(D/Qopt) = 365/11
Orders per year = D/Qopt = 1000/90 = 11 orders/year Order cycle time= 365/(D/Qopt) = 365/11 = 33.1days TCmin = SD Q HQ 2 (10)(1,000) 90 (2,5)(90) TCmin = $ $111 = 22 $ + +

EOQ Example 2(1 of 2) Annual Demand = 10,000 units
Determine the economic order quantity and the reorder point given the following… Annual Demand = 10,000 units Days per year considered in average daily demand = 365 Cost to place an order = $10 Holding cost per unit per year = 10% of cost per unit Lead time = 10 days Cost per unit = $15 16

EOQ Example 2(2 of 2) Place an order for 366 units. When in the course of using the inventory you are left with only 274 units, place the next order of 366 units. 17

EOQ Example 3 H = $0.75 per yard S = $150 D = 10,000 yards Qopt =
2(150)(10,000) (0.75) Qopt = 2,000 yards TCmin = S D Q H Q 2 TCmin = (150)(10,000) 2,000 (0.75)(2,000) TCmin = $750 + $750 = $1,500 Orders per year = D/Qopt = 10,000/2,000 = 5 orders/year Order cycle time =311 days/(D/Qopt) = 311/5 = 62.2 store days

When to Reorder with EOQ Ordering ?
Reorder Point – is the level of inventory at which a new order is placed ROP = d . L Safety Stock - Stock that is held in excess of expected demand due to variable demand rate and/or lead time. Service Level - Probability that demand will not exceed supply during lead time (probability that inventory available during the lead time will meet the demand) 1 - Probability of stockout

Reorder Point Example Demand = 10,000 yards/year
Store open 311 days/year Daily demand = 10,000 / 311 = yards/day Lead time = L = 10 days R = dL = (32.154)(10) = yards

Determinants of the Reorder Point
The rate of demand The lead time Demand and/or lead time variability Stockout risk (safety stock)

Probabilistic Models Answer how much & when to order
Allow demand and lead time to vary Follows normal distribution Other EOQ assumptions apply Consider service level & safety stock Service level = 1 - Probability of stockout Higher service level means more safety stock More safety stock means higher ROP

Safety Stock Quantity Maximum probable demand Expected demand
LT Time Expected demand during lead time Maximum probable demand ROP Quantity Safety stock Safety stock reduces risk of stockout during lead time

Reorder Point With Variable Demand
point, R Q LT Time Inventory level

Reorder Point with a Safety Stock
point, R Q LT Time Inventory level Safety Stock

Reorder Point With Variable Demand and Constant Lead Time
R = dL + zd L where d = average daily demand L = lead time d = the standard deviation of daily demand z = number of standard deviations corresponding to the service level probability zd L = safety stock

Reorder Point for Service Level
Probability of meeting demand during lead time = service level a stockout R Safety stock dL Expected Demand zd L The reorder point based on a normal distribution of LT demand

Reorder Point for Variable Demand (Example)
The carpet store wants a reorder point with a 95% service level and a 5% stockout probability d = 30 yards per day, (demand is normally distributed) d = 5 yards per day L= 10 days For a 95% service level, z = 1.65 R = dL + z d L = 30(10) + (1.65)(5)( 10) = yards Safety stock = z d L = (1.65)(5)( 10) = 26.1 yards

Shortages and Service Levels
It is also important to specify: 1) Expected number of units short per order cycle E(n) =E(z) σdLT where E(z) is standardized number of units short obtained from Table 12.3, pg ) Expected number of units short per year E(N) =E (n) (D/Q) 3) Annual Service Level SLannual = 1- E(N)/D that is percentage of demand filled directly from inventory, known also as FILL RATE.

Example 10 (pg. 568)– shortages and service levels
Suppose standard deviation of lead time demand is known to be 20 units. Lead time demand is approximately normal. (a) For lead time service level of 90 percent, determine the expected number of units short for any other cycle. (b) What lead time service level would imply an expected shortage of 2 units?

Answer – shortage and service levels
(a) For lead time service level of 90 percent, determine the expected number of units short for any other cycle. σdLT= 20 units lead time service level is 0.90 from z table (lead time), E(z)= (page 569, table 12.3) E(n) =E(z) σdLT = (0.048) (20)= 0.96 or about 1 unit. (b) What lead time service level would an expected shortage of 2 units imply? E(n) = 2 E(n) =E(z) σdLT or E(z) = E(n) / σdLT =(2)/(20)= from the table, lead time service level is percent or 81.7%

Shortages and Service Levels
Expected number of units short per year See example 11, page 568 Annual Service Level See example 12, page 570 Note that annual service level will usually be grater than the cycle service level

Fixed Order Quantity Models: -Noninstantaneous Receipt- Production Order Quantity (Economic Lot Size) Model

Production Order Quantity Model
Production done in batches or lots Capacity to produce a part exceeds that part’s usage or demand rate Allows partial receipt of material Other EOQ assumptions apply Suited for production environment Material produced, used immediately Provides production lot size Lower holding cost than EOQ model Answers how much to order and when to order

POQ Model Inventory Levels (1 of 2)
Time Supply Begins Supply Ends Production portion of cycle Demand portion of cycle with no supply Maximum inventory level

POQ Model Inventory Levels (2 of 2)
Time Inventory Level Production Portion of Cycle Max. Inventory Q/p·(p- u) Q* Supply Begins Supply Ends Inventory level with no demand Demand portion of cycle with no supply Average inventory Q/2(1- u/p)

Maximum inventory level
POQ Model Equations ( ) - u p Maximum inventory level * = 1 Q D = Demand per year S = Setup cost H = Holding cost d = Demand per day p = Production per day D = * S Setup Cost Q ( ) u = Holding Cost 1/2 * H * Q 1 - p

Production Order Quantity Example (1 of 2)
H = $0.75 per yard S = $150 D = 10,000 yards u = 10,000/311 = 32.2 yards per day p = 150 yards per day POQopt = = = 2,256.8 yards 2 S D H 1 - u p 2(150)(10,000) 32.2 150 TC = = $1,329 u p S D Q H Q 2 Production run = = = days per order Q p 2,256.8 150

Production Quantity Example (2 of 2)
H = $0.75 per yard S = $150 D = 10,000 yards u= 10,000/311 = 32.2 yards per day p = 150 yards per day Number of production runs = = = 4.43 runs/year D Q 10,000 2,256.8 Maximum inventory level = Q = 2, = 1,772 yards u p 32.2 150 Qopt = = = 2,256.8 yards 2CoD Cc 1 - d p 2(150)(10,000) 32.2 150 TC = = $1,329 CoD Q CcQ 2 Production run = = = days per order 2,256.8

Fixed-Order Quantity Models: Economic Order Quantity Model with Quantity Discounts

Quantity Discount Model
Answers how much to order & when to order Allows quantity discounts Price per unit decreases as order quantity increases Other EOQ assumptions apply Trade-off is between lower price & increased holding cost Total cost with purchasing cost TC = PD S D Q iP Q 2 Where P: Unit Price

Total Costs with Purchasing Cost
EOQ TC with PD TC without PD PD Quantity Adding Purchasing cost doesn’t change EOQ

Quantity Discount Models
There are two general cases of quantity discount models: Carrying costs are constant (e.g. $2 per unit). Carrying costs are stated as a percentage off purchase price (20% of unit price)

1) Total Cost with Constant Carrying Costs
(Compute the Common Optimal Order Quantity OC EOQ Quantity Total Cost TCa TCc TCb Decreasing Price CC a,b,c

2) Total Cost with Variable Carrying Cost (Compute Optimal Order Quantity for each price range)
Based on the same assumptions as the EOQ model, the price-break model has a similar Qopt formula: i = percentage of unit cost attributed to carrying inventory C = cost per unit Since “C” changes for each price-break, the formula above will have to be used with each price-break cost value

Quantity Discount – How Much to Order?

Price-Break Example 1 (1 of 3)
ORDER SIZE PRICE $10 (d1) (d2) For this problem holding cost is given as a constant value, not as a percentage of price, so the optimal order quantity is the same for each of the price ranges. (see the figure 12.9)

Price Break Example 1 (2 of 3)
Qopt Carrying cost Ordering cost Inventory cost ($) Q(d1 ) = 100 Q(d2 ) = 200 TC (d2 = $6 ) TC (d1 = $8 ) TC = ($10 )

Price Break Example 1 (3 of 3)
Qopt Carrying cost Ordering cost Inventory cost ($) Q(d1 ) = 100 Q(d2 ) = 200 TC (d2 = $6 ) TC (d1 = $8 ) TC = ($10 ) The lowest total cost is at the second price break

Price Break Example 2 QUANTITY PRICE 1 - 49 $1,400 50 - 89 1,100
$1,400 ,100 S = $2,500 H = $190 per computer D = 200 Qopt = = = 72.5 PCs 2SD H 2(2500)(200) 190 TC = PD = $233,784 SD Qopt H Qopt 2 For Q = 72.5 TC = PD = $194,105 SD Q H Q 2 For Q = 90

A company has a chance to reduce their inventory ordering costs by placing larger quantity orders using the price-break order quantity schedule below. What should their optimal order quantity be if this company purchases this single inventory item with an ordering cost of $4, a carrying cost with a rate of 2% of the unit price, and an annual demand of 10,000 units? Order Quantity(units) Price/unit($) 0 to 2,499 $1.20 2,500 to 3, 4,000 or more

Price-Break Example (2 of 4)
First, plug data into formula for each price-break value of “C” Annual Demand (D)= 10,000 units Cost to place an order (S)= $4 Carrying cost % of total cost (i)= 2% Cost per unit (C) = $1.20, $1.00, $0.98 Next, determine if the computed Qopt values are feasible or not Interval from 4000 & more, the Qopt value is not feasible Interval from , the Qopt value is not feasible Interval from 0 to 2499, the Qopt value is feasible

Since the feasible solution occurred in the first price-break, it means that all the other true Qopt values occur at the beginnings of each price-break interval. Why? Because the total annual cost function is a “u” shaped function Total annual costs So the candidates for the price-breaks are 1826, 2500, and 4000 units Order Quantity

Next, we plug the true Qopt values into the total cost annual cost function to determine the total cost under each price-break TC(0-2499)=(10000*1.20)+(10000/1826)*4+(1826/2)(0.02*1.20) = $12,043.82 TC( )= $10,041 TC(4000&more)= $9,949.20 Finally, we select the least costly Qopt, which in this problem occurs in the 4000 & more interval. In summary, our optimal order quantity is 4000 units

Multi-period Inventory Models: Fixed Time Period (Fixed-Order- Interval) Models

Fixed-Order-Interval Model
Orders are placed at fixed time intervals Order quantity for next interval? (inventory is brought up to target amount, amount ordered varies) Suppliers might encourage fixed intervals Requires only periodic checks of inventory levels (no continous monitoring is required) Risk of stockout between intervals

Inventory Level in a Fixed Period System
Various amounts (Qi) are ordered at regular time intervals (p) based on the quantity necessary to bring inventory up to target maximum p Q1 Q2 Q3 Q4 Target maximum Time d Inventory

Fixed-Interval Benefits
Tight control of inventory items Items from same supplier may yield savings in: Ordering Packing Shipping costs May be practical when inventories cannot be closely monitored

Fixed-Interval Disadvantages
Requires a larger safety stock Increases carrying cost Costs of periodic reviews

Fixed-Time Period Model with Safety Stock Formula
q = Average demand + Safety stock – Inventory currently on hand

Fixed-Time Period Model: Determining the Value of sT+L
The standard deviation of a sequence of random events equals the square root of the sum of the variances 20

Order Quantity for a Periodic Inventory System
Q = d(tb + L) + zd T + L - I where d = average demand rate T = the fixed time between orders L = lead time  d = standard deviation of demand zd T + L = safety stock I = inventory level z = the number of standard deviations for a specified service level

Fixed-Period Model with Variable Demand (Example 1)
d = 6 bottles per day sd = 1.2 bottles T = 60 days L = 5 days I = 8 bottles z = 1.65 (for a 95% service level) Q = d(T + L) + zd T + L - I = (6)(60 + 5) + (1.65)(1.2) = bottles

Fixed-Time Period Model with Variable Demand (Example 2)(1 of 3)
Given the information below, how many units should be ordered? Average daily demand for a product is 20 units. The review period is 30 days, and lead time is 10 days. Management has set a policy of satisfying 96 percent of demand from items in stock. At the beginning of the review period there are 200 units in inventory. The standard deviation of daily demand is 4 units. 21

Fixed-Time Period Model with Variable Demand (Example 2)(2 of 3)
So, by looking at the value from the Table, we have a probability of , which is given by a z = 1.75 22

Fixed-Time Period Model with Variable Demand (Example 2) (3 of 3)
So, to satisfy 96 percent of the demand, you should place an order of 645 units at this review period 23

ABC Classification System
Demand volume and value of items vary Items kept in inventory are not of equal importance in terms of: dollars invested profit potential sales or usage volume stock-out penalties 26

ABC Classification System
Classifying inventory according to some measure of importance and allocating control efforts accordingly. A - very important B - mod. important C - least important Annual $ value of items A B C High Low Percentage of Items

ABC Analysis Classify inventory into 3 categories typically on the basis of the dollar value to the firm $ volume = Annual demand x Unit cost A class, B class, C class Policies based on ABC analysis Develop class A suppliers more carefully Give tighter physical control of A items Forecast A items more carefully

Classifying Items as ABC
20 40 60 80 100 50 % Annual $ Usage A B C Class % $ Vol % Items 70-80 5-15 15 30 5-10 50-60 % of Inventory Items

ABC Classification PART UNIT COST ANNUAL USAGE 1 $ 60 90 2 350 40
1 $ 60 90 PART UNIT COST ANNUAL USAGE

ABC Classification PART UNIT COST ANNUAL USAGE 1 $ 60 90 2 350 40
1 $ 60 90 PART UNIT COST ANNUAL USAGE TOTAL % OF TOTAL % OF TOTAL PART VALUE VALUE QUANTITY % CUMMULATIVE 9 $30, 8 16, 2 14, 1 5, 4 4, 3 3, 6 3, 5 3, 10 2, 7 1, $85,400

ABC Classification A B C PART UNIT COST ANNUAL USAGE 1 $ 60 90
1 $ 60 90 PART UNIT COST ANNUAL USAGE TOTAL % OF TOTAL % OF TOTAL PART VALUE VALUE QUANTITY % CUMMULATIVE 9 $30, 8 16, 2 14, 1 5, 4 4, 3 3, 6 3, 5 3, 10 2, 7 1, $85,400 A B C

ABC Classification A B C PART UNIT COST ANNUAL USAGE 1 $ 60 90
1 $ 60 90 PART UNIT COST ANNUAL USAGE TOTAL % OF TOTAL % OF TOTAL PART VALUE VALUE QUANTITY % CUMMULATIVE 9 $30, 8 16, 2 14, 1 5, 4 4, 3 3, 6 3, 5 3, 10 2, 7 1, $85,400 A B C % OF TOTAL % OF TOTAL CLASS ITEMS VALUE QUANTITY A 9, 8, B 1, 4, C 6, 5, 10,

Inventory Accuracy and Cycle Counting
Inventory accuracy refers to how well the inventory records agree with physical count. Cycle counting refers to Physical Count of items in inventory. Used often with ABC classification While A items are counted most often (e.g., daily), C items are counted the least frequently.

Last Words Inventories have certain functions. But too much inventory
Tends to hide problems Costly to maintain So it is desired Reduce lot sizes Reduce safety stocks

Inventory Management and Control

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Inventory Management and Control

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