chapter 2:inventory management and risk pooling

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Chapter 2:InventoryManagementand Risk Pooling

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CASE: Steel Works

Background of case and intentOverview of businessWhat does data tell you about Specialty?How much inventory might you expect?What opportunities are there for Custom?Wrap up

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Background & Intent

Abstraction from summer consulting job Intent is to examine a realistic, but

simplified inventory context and perform a diagnosis of problem – poor service and too much inventory

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Custom Products

Rapid growth, 1/3 of total sales ($133 MM)One customer per productVery high marginsHigh service level3 plants, co-located with R&D centerEach product produced at a single plant

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Specialty Products

Rapid growth, 2/3 of total sales ($267 MM) 6 product families 3 plants, each producing 2 product families 130 customers, 120 products Few big customers Highly volatile demand High service level

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Consultant Recommendation

Drop low volume products Improve forecastsConsolidate warehouses

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What Does Data Tell You?

cv

DB R10 15.5 13.2 0.85

DB R12 1008 256 0.25

DB R15 2464 494 0.20

DF R10 97 92.5 0.95

DF R12 18.5 11.4 0.62

DF R15 55 80 1.46

DF R23 35.5 45.9 1.29

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What Does Data Tell You?

Durabend R12:One customer accounts for 97% of demand

7 products:High volume (2) is not very volatileLow volume (5) is very volatile

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How Much Inventory Should You Expect?

Assume base stock model with periodic review

Review period = r = ?Lead time = L = ?

2

E I r z r L

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Cycle stock

Saf. stock

E[I] Act. Inv.

DB R10 15.5 13.2 8 26 34 72

DB R12 1008 256 504 510 1014 740

DB R15 2464 494 1232 990 2222 1875

DF R10 97 92.5 49 185 234 604

DF R12 18.5 11.4 9 23 32 55

DF R15 55 80 28 160 188 388

DF R23 35.5 45.9 18 92 110 190

1848 1986 3834 3824

Assumes r = 1; L=0.25; and z = 1.8

Cycle stock = r /2 Safety stock = z r+L

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What Are the Opportunities at Custom?

Combine production and inventory for common items, e. g. DF R23

Produce monthly: reduce setups by half and pool safety stocks

Produce twice a month: same number of setups but cut cycle stock and review period in half

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Wrap Up

Realistic diagnostic exercise In real life: not as clean, more data and

more considerationsYet simple models and principles can

provide valuable guidance

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2.1 IntroductionWhy Is Inventory Important?

Distribution and inventory (logistics) costs are quite substantial

Total U.S. Manufacturing Inventories ($m): 1992-01-31: $m 808,773 1996-08-31: $m 1,000,774 2006-05-31: $m 1,324,108

Inventory-Sales Ratio (U.S. Manufacturers): 1992-01-01: 1.56 2006-05-01: 1.25

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GM’s production and distribution network 20,000 supplier plants 133 parts plants 31 assembly plants 11,000 dealers

Freight transportation costs: $4.1 billion (60% for material shipments)

GM inventory valued at $7.4 billion (70%WIP; Rest Finished Vehicles)

Decision tool to reduce: combined corporate cost of inventory and transportation.

26% annual cost reduction by adjusting: Shipment sizes (inventory policy) Routes (transportation strategy)

Why Is Inventory Important?

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Why Is Inventory Required?

Uncertainty in customer demandShorter product lifecyclesMore competing products

Uncertainty in suppliesQuality/Quantity/Costs/Delivery Times

Delivery lead times Incentives for larger shipments

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Holding the right amount at the right time is difficult!

Dell Computer’s was sharply off in its forecast of demand, resulting in inventory write-downs 1993 stock plunge

Liz Claiborne’s higher-than-anticipated excess inventories 1993 unexpected earnings decline,

IBM’s ineffective inventory management 1994 shortages in the ThinkPad line

Cisco’s declining sales 2001 $ 2.25B excess inventory charge

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Inventory Management-Demand Forecasts

Uncertain demand makes demand forecast critical for inventory related decisions:What to order?When to order?How much is the optimal order quantity?

Approach includes a set of techniquesINVENTORY POLICY!!

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Supply Chain Factors in Inventory Policy

Estimation of customer demand Replenishment lead time The number of different products being considered The length of the planning horizon Costs

Order cost: Product cost Transportation cost

Inventory holding cost, or inventory carrying cost: State taxes, property taxes, and insurance on inventories Maintenance costs Obsolescence cost Opportunity costs

Service level requirements

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2.2 Single Stage Inventory Control

Single supply chain stage Variety of techniques

Economic Lot Size Model Demand Uncertainty Single Period Models Initial Inventory Multiple Order Opportunities Continuous Review Policy Variable Lead Times Periodic Review Policy Service Level Optimization

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2.2.1. Economic Lot Size Model

FIGURE 2-3: Inventory level as a function of time

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Assumptions

D items per day: Constant demand rate Q items per order: Order quantities are fixed, i.e., each

time the warehouse places an order, it is for Q items. K, fixed setup cost, incurred every time the warehouse

places an order. h, inventory carrying cost accrued per unit held in

inventory per day that the unit is held (also known as, holding cost)

Lead time = 0 (the time that elapses between the placement of an order and its receipt)

Initial inventory = 0 Planning horizon is long (infinite).

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Deriving EOQ

Total cost at every cycle:

Average inventory holding cost in a cycle: Q/2

Cycle time T =Q/D Average total cost per unit time:

2

hTQK

2

hQ

Q

KD

h

KDQ

2*

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EOQ: Costs

FIGURE 2-4: Economic lot size model: total cost per unit time

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Sensitivity Analysis

b .5 .8 .9 1 1.1 1.2 1.5 2

Increase in cost

25% 2.5% 0.5% 0 .4% 1.6% 8.9% 25%

Total inventory cost relatively insensitive to order quantities

Actual order quantity: Q Q is a multiple b of the optimal order quantity Q*. For a given b, the quantity ordered is Q = bQ*

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2.2.2. Demand Uncertainty

The forecast is always wrong It is difficult to match supply and demand

The longer the forecast horizon, the worse the forecast It is even more difficult if one needs to predict

customer demand for a long period of time Aggregate forecasts are more accurate.

More difficult to predict customer demand for individual SKUs

Much easier to predict demand across all SKUs within one product family

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2.2.3. Single Period Models

Short lifecycle productsOne ordering opportunity onlyOrder quantity to be decided before

demand occurs

Order Quantity > Demand => Dispose excess inventory

Order Quantity < Demand => Lose sales/profits

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Single Period Models Using historical data

identify a variety of demand scenarios determine probability each of these scenarios will occur

Given a specific inventory policy determine the profit associated with a particular scenario given a specific order quantity

weight each scenario’s profit by the likelihood that it will occur determine the average, or expected, profit for a particular ordering

quantity.

Order the quantity that maximizes the average profit.

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Single Period Model Example

FIGURE 2-5: Probabilistic forecast

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Additional Information

Fixed production cost: $100,000 Variable production cost per unit: $80.During the summer season, selling price:

$125 per unit.Salvage value: Any swimsuit not sold

during the summer season is sold to a discount store for $20.

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Two Scenarios

Manufacturer produces 10,000 units while demand ends at 12,000 swimsuits Profit = 125(10,000) - 80(10,000) - 100,000 = $350,000

Manufacturer produces 10,000 units while demand ends at 8,000 swimsuits Profit= 125(8,000) + 20(2,000) - 80(10,000) - 100,000= $140,000

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Probability of Profitability Scenarios with Production = 10,000 Units

Probability of demand being 8000 units = 11%Probability of profit of $140,000 = 11%

Probability of demand being 12000 units = 27%Probability of profit of $140,000 = 27%

Total profit = Weighted average of profit scenarios

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Order Quantity that Maximizes Expected Profit

FIGURE 2-6: Average profit as a function of production quantity

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Relationship Between Optimal Quantity and Average Demand

Compare marginal profit of selling an additional unit and marginal cost of not selling an additional unit

Marginal profit/unit = Selling Price - Variable Ordering (or, Production) Cost

Marginal cost/unit =Variable Ordering (or, Production) Cost - Salvage Value

If Marginal Profit > Marginal Cost => Optimal Quantity > Average Demand

If Marginal Profit < Marginal Cost => Optimal Quantity < Average Demand

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For the Swimsuit Example

Average demand = 13,000 units. Optimal production quantity = 12,000 units.

Marginal profit = $45 Marginal cost = $60.

Thus, Marginal Cost > Marginal Profit

=> optimal production quantity < average demand.

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Risk-Reward Tradeoffs

Optimal production quantity maximizes average profit is about 12,000

Producing 9,000 units or producing 16,000 units will lead to about the same average profit of $294,000.

If we had to choose between producing 9,000 units and 16,000 units, which one should we choose?

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Risk-Reward Tradeoffs

FIGURE 2-7: A frequency histogram of profit

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Risk-Reward Tradeoffs Production Quantity = 9000 units

Profit is: either $200,000 with probability of about 11 % or $305,000 with probability of about 89 %

Production quantity = 16,000 units. Distribution of profit is not symmetrical. Losses of $220,000 about 11% of the time Profits of at least $410,000 about 50% of the time

With the same average profit, increasing the production quantity: Increases the possible risk Increases the possible reward

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ObservationsThe optimal order quantity is not necessarily

equal to forecast, or average, demand. As the order quantity increases, average

profit typically increases until the production quantity reaches a certain value, after which the average profit starts decreasing.

Risk/Reward trade-off: As we increase the production quantity, both risk and reward increases.

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2.2.4. What If the Manufacturer Has an Initial Inventory?

Trade-off between:Using on-hand inventory to meet demand and

avoid paying fixed production cost: need sufficient inventory stock

Paying the fixed cost of production and not have as much inventory

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Initial Inventory Solution

FIGURE 2-8: Profit and the impact of initial inventory

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Manufacturer Initial Inventory = 5,000

If nothing is produced, average profit =

225,000 (from the figure) + 5,000 x 80 = 625,000 If the manufacturer decides to produce

Production should increase inventory from 5,000 units to 12,000 units.

Average profit =

371,000 (from the figure) + 5,000 • 80 = 771,000

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No need to produce anything average profit > profit achieved if we produce to

increase inventory to 12,000 units If we produce, the most we can make on

average is a profit of $375,000. Same average profit with initial inventory of 8,500

units and not producing anything. If initial inventory < 8,500 units => produce to raise

the inventory level to 12,000 units. If initial inventory is at least 8,500 units, we should not

produce anything (s, S) policy or (min, max) policy

Manufacturer Initial Inventory = 10,000

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2.2.5. Multiple Order Opportunities

REASONS To balance annual inventory holding costs and annual fixed order

costs. To satisfy demand occurring during lead time. To protect against uncertainty in demand.

TWO POLICIES Continuous review policy

inventory is reviewed continuously an order is placed when the inventory reaches a particular level or reorder point. inventory can be continuously reviewed (computerized inventory systems are

used)

Periodic review policy inventory is reviewed at regular intervals appropriate quantity is ordered after each review. it is impossible or inconvenient to frequently review inventory and place orders if

necessary.

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2.2.6. Continuous Review Policy Daily demand is random and follows a normal distribution. Every time the distributor places an order from the

manufacturer, the distributor pays a fixed cost, K, plus an amount proportional to the quantity ordered.

Inventory holding cost is charged per item per unit time. Inventory level is continuously reviewed, and if an order is

placed, the order arrives after the appropriate lead time. If a customer order arrives when there is no inventory on

hand to fill the order (i.e., when the distributor is stocked out), the order is lost.

The distributor specifies a required service level.

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AVG = Average daily demand faced by the distributor

STD = Standard deviation of daily demand faced by the distributor

L = Replenishment lead time from the supplier to the

distributor in days h = Cost of holding one unit of the product for

one day at the distributor α = service level. This implies that the probability

of stocking out is 1 - α

Continuous Review Policy

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(Q,R) policy – whenever inventory level falls to a reorder level R, place an order for Q units

What is the value of R?

Continuous Review Policy

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Continuous Review Policy

Average demand during lead time: L x AVG

Safety stock:

Reorder Level, R:

Order Quantity, Q:

LSTDz

LSTDzAVGL

h

AVGKQ

2

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Service Level & Safety Factor, z

Service Level

90% 91% 92% 93% 94% 95% 96% 97% 98% 99% 99.9%

z 1.29 1.34 1.41 1.48 1.56 1.65 1.75 1.88 2.05 2.33 3.08

z is chosen from statistical tables to ensure that the probability of stockouts during lead time is exactly 1 - α

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Inventory Level Over Time

LSTDz Inventory level before receiving an order =

Inventory level after receiving an order =

Average Inventory =

LSTDzQ

LSTDzQ 2

FIGURE 2-9: Inventory level as a function of time in a (Q,R) policy

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Continuous Review Policy Example

A distributor of TV sets that orders from a manufacturer and sells to retailers

Fixed ordering cost = $4,500Cost of a TV set to the distributor = $250Annual inventory holding cost = 18% of

product costReplenishment lead time = 2 weeksExpected service level = 97%

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Month Sept Oct Nov. Dec. Jan. Feb. Mar. Apr. May June July Aug

Sales 200 152 100 221 287 176 151 198 246 309 98 156

Continuous Review Policy Example

Average monthly demand = 191.17 Standard deviation of monthly demand = 66.53

Average weekly demand = Average Monthly Demand/4.3Standard deviation of weekly demand = Monthly standard deviation/√4.3

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Parameter Average weekly demand

Standard deviation of weekly demand

Average demand during lead time

Safety stock

Reorder point

Value 44.58 32.08 89.16 86.20 176

87.052

25018.0

Weekly holding cost =

Optimal order quantity = 67987.

58.44500,42

Q

Average inventory level = 679/2 + 86.20 = 426

Continuous Review Policy Example

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Average lead time, AVGL Standard deviation, STDL. Reorder Level, R:

222 STDLAVGSTDAVGLzAVGLAVGR

2.2.7. Variable Lead Times

222 STDLAVGSTDAVGLz Amount of safety stock=

h

AVGKQ

2Order Quantity =

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Inventory level is reviewed periodically at regular intervals

An appropriate quantity is ordered after each review Two Cases:

Short Intervals (e.g. Daily) Define two inventory levels s and S During each inventory review, if the inventory position falls below s,

order enough to raise the inventory position to S. (s, S) policy

Longer Intervals (e.g. Weekly or Monthly) May make sense to always order after an inventory level review. Determine a target inventory level, the base-stock level During each review period, the inventory position is reviewed Order enough to raise the inventory position to the base-stock level. Base-stock level policy

2.2.8. Periodic Review Policy

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(s,S) policy

Calculate the Q and R values as if this were a continuous review model

Set s equal to RSet S equal to R+Q.

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Base-Stock Level Policy Determine a target inventory level, the base-

stock level Each review period, review the inventory

position is reviewed and order enough to raise the inventory position to the base-stock level

Assume:r = length of the review periodL = lead time AVG = average daily demand STD = standard deviation of this daily demand.

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Average demand during an interval of r + L days=

Safety Stock= LrSTDz

AVGLr )(

Base-Stock Level Policy

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Base-Stock Level Policy

FIGURE 2-10: Inventory level as a function of time in a periodic review policy

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Assume: distributor places an order for TVs every 3 weeks Lead time is 2 weeks Base-stock level needs to cover 5 weeks

Average demand = 44.58 x 5 = 222.9 Safety stock = Base-stock level = 223 + 136 = 359 Average inventory level =

Distributor keeps 5 (= 203.17/44.58) weeks of supply.

Base-Stock Level Policy Example

58.329.1

17.203508.329.1258.443

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Optimal inventory policy assumes a specific service level target.

What is the appropriate level of service? May be determined by the downstream

customerRetailer may require the supplier, to maintain a

specific service levelSupplier will use that target to manage its own

inventoryFacility may have the flexibility to choose the

appropriate level of service

2.2.9. Service Level Optimization

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Service Level Optimization

FIGURE 2-11: Service level inventory versus inventory level as a function of lead time

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Trade-Offs

Everything else being equal:the higher the service level, the higher the

inventory level. for the same inventory level, the longer the

lead time to the facility, the lower the level of service provided by the facility.

the lower the inventory level, the higher the impact of a unit of inventory on service level and hence on expected profit

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Retail Strategy

Given a target service level across all products determine service level for each SKU so as to maximize expected profit.

Everything else being equal, service level will be higher for products with:high profit marginhigh volumelow variabilityshort lead time

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Profit Optimization and Service Level

FIGURE 2-12: Service level optimization by SKU

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Target inventory level = 95% across all products.

Service level > 99% for many products with high profit margin, high volume and low variability.

Service level < 95% for products with low profit margin, low volume and high variability.

Profit Optimization and Service Level

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2.3 Risk Pooling

Demand variability is reduced if one aggregates demand across locations.

More likely that high demand from one customer will be offset by low demand from another.

Reduction in variability allows a decrease in safety stock and therefore reduces average inventory.

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Demand Variation

Standard deviation measures how much demand tends to vary around the averageGives an absolute measure of the variability

Coefficient of variation is the ratio of standard deviation to average demandGives a relative measure of the variability,

relative to the average demand

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Acme Risk Pooling Case Electronic equipment manufacturer and distributor 2 warehouses for distribution in New York and New

Jersey (partitioning the northeast market into two regions)

Customers (that is, retailers) receiving items from warehouses (each retailer is assigned a warehouse)

Warehouses receive material from Chicago Current rule: 97 % service level Each warehouse operate to satisfy 97 % of demand

(3 % probability of stock-out)

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Replace the 2 warehouses with a single warehouse (located some suitable place) and try to implement the same service level 97 %

Delivery lead times may increase But may decrease total inventory investment

considerably.

New Idea

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Historical Data

PRODUCT A

Week 1 2 3 4 5 6 7 8

Massachusetts 33 45 37 38 55 30 18 58

New Jersey 46 35 41 40 26 48 18 55

Total 79 80 78 78 81 78 36 113

PRODUCT B

Week 1 2 3 4 5 6 7 8

Massachusetts 0 3 3 0 0 1 3 0

New Jersey 2 4 3 0 3 1 0 0

Total 2 6 3 0 3 2 3 0

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Summary of Historical DataStatistics Product Average Demand Standard

Deviation of Demand

Coefficient of Variation

Massachusetts A 39.3 13.2 0.34

Massachusetts B 1.125 1.36 1.21

New Jersey A 38.6 12.0 0.31

New Jersey B 1.25 1.58 1.26

Total A 77.9 20.71 0.27

Total B 2.375 1.9 0.81

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Inventory LevelsProduct Average

Demand During Lead Time

Safety Stock Reorder Point

Q

Massachusetts A 39.3 25.08 65 132

Massachusetts B 1.125 2.58 4 25

New Jersey A 38.6 22.8 62 31

New Jersey B 1.25 3 5 24

Total A 77.9 39.35 118 186

Total B 2.375 3.61 6 33

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Savings in Inventory

Average inventory for Product A: At NJ warehouse is about 88 units At MA warehouse is about 91 units In the centralized warehouse is about 132 units Average inventory reduced by about 36 percent

Average inventory for Product B: At NJ warehouse is about 15 units At MA warehouse is about 14 units In the centralized warehouse is about 20 units Average inventory reduced by about 43 percent

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The higher the coefficient of variation, the greater the benefit from risk pooling The higher the variability, the higher the safety stocks

kept by the warehouses. The variability of the demand aggregated by the single warehouse is lower

The benefits from risk pooling depend on the behavior of the demand from one market relative to demand from another risk pooling benefits are higher in situations where

demands observed at warehouses are negatively correlated

Reallocation of items from one market to another easily accomplished in centralized systems. Not possible to do in decentralized systems where they serve different markets

Critical Points

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2.4 Centralized vs. Decentralized Systems

Safety stock: lower with centralization Service level: higher service level for the same

inventory investment with centralization Overhead costs: higher in decentralized system Customer lead time: response times lower in the

decentralized system Transportation costs: not clear. Consider

outbound and inbound costs.

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Inventory decisions are given by a single decision maker whose objective is to minimize the system-wide cost

The decision maker has access to inventory information at each of the retailers and at the warehouse

Echelons and echelon inventoryEchelon inventory at any stage or level of the system

equals the inventory on hand at the echelon, plus all downstream inventory (downstream means closer to the customer)

2.5 Managing Inventory in the Supply Chain

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Echelon Inventory

FIGURE 2-13: A serial supply chain

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Reorder Point with Echelon Inventory

Le = echelon lead time, lead time between the retailer and the

distributor plus the lead time between the distributor and its supplier, the wholesaler.

AVG = average demand at the retailer STD = standard deviation of demand at

the retailerReorder point ee LSTDzAVGLR

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4-Stage Supply Chain Example

Average weekly demand faced by the retailer is 45

Standard deviation of demand is 32 At each stage, management is attempting

to maintain a service level of 97% (z=1.88) Lead time between each of the stages,

and between the manufacturer and its suppliers is 1 week

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Costs and Order Quantities

K D H Q

retailer 250 45 1.2 137

distributor 200 45 .9 141

wholesaler 205 45 .8 152

manufacturer 500 45 .7 255

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Reorder Points at Each Stage

For the retailer, R=1*45+1.88*32*√1 = 105For the distributor, R=2*45+1.88*32*√2 =

175For the wholesaler, R=3*45+1.88*32*√3 =

239For the manufacturer, R=4*45+1.88*32*√4

= 300

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More than One Facility at Each Stage

Follow the same approach Echelon inventory at the warehouse is the

inventory at the warehouse, plus all of the inventory in transit to and in stock at each of the retailers.

Similarly, the echelon inventory position at the warehouse is the echelon inventory at the warehouse, plus those items ordered by the warehouse that have not yet arrived minus all items that are backordered.

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Warehouse Echelon Inventory

FIGURE 2-14: The warehouse echelon inventory

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2.6 Practical Issues Periodic inventory review. Tight management of usage rates, lead times, and

safety stock. Reduce safety stock levels. Introduce or enhance cycle counting practice. ABC approach. Shift more inventory or inventory ownership to

suppliers. Quantitative approaches. FOCUS: not reducing costs but reducing inventory levels. Significant effort in industry to increase inventory turnover

LevelInventoryAverage

SalesAnnualRatioTurnoverInventory

__

___

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Inventory Turnover Ratios for Different Manufacturers

Industry Upper quartile Median Lower quartile

Electronic components and accessories

8.1 4.9 3.3

Electronic computers 22.7 7.0 2.7

Household audio and video equipment

6.3 3.9 2.5

Paper Mills 11.7 8.0 5.5

Industrial chemicals 14.1 6.4 4.2

Bakery products 39.7 23.0 12.6

Books: Publishing and printing

7.2 2.8 1.5

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2.7 Forecasting

RULES OF FORECASTING The forecast is always wrong. The longer the forecast horizon, the

worse the forecast. Aggregate forecasts are more accurate.

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Utility of Forecasting

Part of the available tools for a managerDespite difficulties with forecasts, it can be

used for a variety of decisionsNumber of techniques allow prudent use

of forecasts as needed

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Techniques Judgment Methods

Sales-force composite Experts panel Delphi method

Market research/survey Time Series

Moving Averages Exponential Smoothing

Trends Regression Holt’s method

Seasonal patterns – Seasonal decomposition Trend + Seasonality – Winter’s Method Causal Methods

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The Most Appropriate Technique(s)

Purpose of the forecastHow will the forecast be used?Dynamics of system for which forecast will

be madeHow accurate is the past history in

predicting the future?

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SUMMARY

Matching supply with demand a major challenge Forecast demand is always wrong Longer the forecast horizon, less accurate the

forecast Aggregate demand more accurate than

disaggregated demand Need the most appropriate technique Need the most appropriate inventory policy