Inventory PreRead

7/31/2019 Inventory PreRead

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Inventory Control:

A Pre-Reader

LPA Software, Inc.

290 Woodcliff DriveFairport, New York 14450(716) 248-9600


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The support of Xerox Corporation is gratefully acknowledged.


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Contents

Contents

Contents.........................................................................................................................................i ii

Introduction.....................................................................................................................................1

Logistics Triangle............................................................................................................................1Supply Pipeline.........................................................................................................................2

Value Chains............................................................................................................................4

Stocking Algorithms........................................................................................................................5

Reorder Point (ROP).......................................................................................................................7

Level of Safety Stock.....................................................................................................................12

Lead Time Variability.....................................................................................................................14

Order Quantities.......................................................................................................................... .14

Economic Order Quantity (EOQ)..................................................................................................16

Meeting Targeted Inventory Levels............................................................................................ ...18Changing Lead Time Performance................................................................................................20

Financial Impact of Logistics.........................................................................................................22

Priority Ranking -- The Pareto Principle........................................................................................23

Ranking Example.......................................................................................................... .........23

Inventory Quality...........................................................................................................................26

Improving Inventory Quality....................................................................................................27

Forecasting Techniques................................................................................................................28

Forecasting Objectives...........................................................................................................28

Forecast Versus Data.............................................................................................................29

Total Forecast and Demand............................................................................................. ......30

Forecast Error/Seasonality.....................................................................................................32

Forecast Types.......................................................................................................................33

Example of a Bill of Material........................................................................................ ...........33

Mixing the Demands...............................................................................................................34

Managing the Demands..........................................................................................................34

How to Deal with Buffered Demand...............................................................................................38

Buffered Demand Factors.......................................................................................................39

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Logistics Triangle

Introduction

Effective management of the supply chain is a critical part of a companysability to deliver a consistently high level of customer satisfaction atacceptable levels of return on assets. This document discusses the basic

principles used within the logistics and distribution environment tomanage the supply chain. Increasing your understanding of these principlescan help you more effectively manage inventory levels.

Logistics Triangle

Three logistic factors that contribute to customer satisfaction are depictedat the apexes of the triangle in Figure 1.

Figure 1. Logistics Triangle

Level of service, inventory, and costs affect all aspects of the logistics anddistribution environment. By managing these factors efficiently,effectively, and economically, companies can achieve their goals of totalcustomer satisfaction, improved return on assets, increased market share,

and improved employee satisfaction.

To improve the three critical logistics factors, level of service, inventory,and costs, it is necessary to improve the processes that affect them, makingprocess improvement the ultimate goal.

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Logistics Triangle

Supply Pipeline

The Harvard Business School's Michael Porter developed a theory calledValue Chains. He defines a value chain as a series of process steps thatadd value to a product or service at each step. In other words, as productmoves along the steps in the overall process, value is added to the product

at each step. The theory of value chains spawned the concept of SupplyChains, which is also a value chain. Both concepts describe adding valueto the product in each step of the process.

Figure 2 is an illustration of a simplified Supply Chain.

Figure 2. Supply Chain

The weakness of the Supply Chain concept is that each link in the chain isperceived as separate, yet interconnected. The links are visualized asconnected, but they are still individual entities. The better way toconceptualize a supply chain is to think of it as a Supply Pipeline.

The Supply Pipeline concept is illustrated in Figure 3.

Figure 3. Supply Pipeline

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Logistics Triangle

Notice that although the customer is at the output end of the pipeline andthe vendor is at the input end, the pipeline flows in both directions.Specifically, material flows from the vendor to the customer, informationand customer requirements flow in the opposite direction.

The pipeline itself is made up of segments. Each segment, like a link in thesupply chain, represents a phase or a step in the logistics and distributionprocess. The segment closest to the customer is the distribution location.The product arrives by some form of transport, the previous segment in thepipeline. Following this segment logic, each segment becomesinterdependent.

Within the supply pipeline there are two important factors: informationand material or assets. The information is in the form of orders andavailability, for example:

What does the customer want?

How much of the asset do we have?

The material or assets reflects the investment in the pipeline, for example:

Are we getting our moneys worth?

Are we adding value as the asset moves through the pipeline?

When examining the pipeline in terms of a particular product, or group ofproducts, there may be segments that do not add value. In these cases, it isnecessary to eliminate those segments from the pipeline. For example, aproduct is shipped from the vendor to manufacturing. Manufacturing thenships it to the warehouse without adding value. A possible processimprovement might be that the vendor ships the product directly to thewarehouse, thereby eliminating the non-value added step of shipping it tomanufacturing.

No segment of the pipeline should be considered sacred. Countlessexamples exist where manufacturing, warehousing, transport, and thedistributor have been bypassed, or have had their traditional functionschanged, to provide better levels of customer satisfaction at lower costswith reduced asset levels.

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Logistics Triangle

Value Chains

Functional value, situational value, and time value are terms used byMichael Porter in his theory of value chains. They relate as follows:

Functional Value = Right PartSituational Value = Right PlaceTime Value = Right Time

These terms can be translated into the following declaration: We musthave the right part, at the right place, at the right time. It is also critical toconsider the right cost, as a company must maintain its existence inbusiness through proper cost controls.

The four rights, part, place, time, and cost, relate directly to the logisticstriangle. They impact the level of service, inventory and costs, which

directly affects customer satisfaction. Lets consider how this conceptualframework can be used to actually change the way business is carried out.

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Stocking Algorithms

Stocking Algorithms

Figure 4 illustrates a simple inventory over time scenario. As demandcomes in, the inventory goes down. It goes down in increments; somesmall, some large. Periodically, the inventory is replenished. If the

inventory is allowed to fall below a zero level, then a stock out occurs. Thegoal is to minimize stock outs without keeping a large inventory that is,to manage inventory.

Figure 4. Inventory versus time

To minimize stock outs, it is necessary to set a minimum inventory level atwhich you experience an acceptable number of stock outs. The objective isto keep stock outs at a reasonable and manageable level that is acceptableto the customer, because it is not possible to be entirely rid of them.

Stocking algorithms, or formulas, are a good starting point in learning howto manage inventory. The most important element in all critical stockingalgorithms is forecasting data. You must know what your need is.

The next important element to define is lead time. Lead time is the timerequired to recognize that more material is needed and to have it available

for distribution, not simply how long it takes a truck or airplane to get frompoint A to point B. Lead time includes:

the time for people at point B to say they need it,

the time for people at point A to manufacture it, pull it off theirshelves, load it onto a vehicle, and get it to point B, and

the time for people at point B to unload it and recognize that theyhave it available for use.

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Stocking Algorithms

In addition, it may include the time required for communications betweenthe people and the systems involved in the whole process.

Sometimes the non-physical aspects of logistics take the longest toperform. There may be a process whereby material is pulled, packed and

loaded in a day, transport takes another day; and unloading and receivingtakes a day. However, if you want the shipment to be made only once aweek, this requirement overrides all of the other processing timesregardless how efficient they are.

Therefore, in the logistics and distribution environment, the inventoryversus time plot is enhanced by using a reorder point in the effort tobecome more efficient.

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Reorder Point

Reorder Point (ROP)

In the effort to enhance the inventory versus time plot, various inventorylevels are built in, as illustrated in Figure 5.

Figure 5. Reorder Point

The minimum inventory level to trigger an order is often the reorder point(ROP). The idea of an ROP is that, over time, as your inventory depletesand reaches this point, it is immediately apparent that it is time for areplenishment order. While the replenishment order is in process,

inventory levels continue to fall. The stock is targeted for replenishment atthe safety stock level of inventory. Therefore, ROP = safety stock plus leadtime demand.

There has been a lot of effort spent in determining the best way to calculatesafety stock. Basically the calculation takes into account two basicvariables: the forecast and the lead time. The following are four types ofsafety stock calculations:

Months or days of stock

Probability of stock out

Piece part fill rate Stock out occurrences

One common calculation is called months of stock or days of stock. Letsassume that you want to have three weeks of supply at the distributioncenter, so whenever the inventory level drops to three weeks of supply plusthe lead time demand you order more material. The problem with thismethod is that it does not take demand variances into account. It gives a

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Reorder Point

targeted inventory level with no consideration of the desired level ofservice. Demand variances will drive the level of service from an end useror customer perspective.

Figure 6 indicates the distribution of demand or sales. There is an averagedemand, with some numbers higher than average and some numbers lowerthan average. The probability of stock out, piece part fill rate, and stockoutoccurrences methods of calculating safety stock use this demand profileand calculate stocking based on the variability of the demand pattern.

Figure 6. Demand Distribution

When demand is low, there is typically too much inventory, and whendemand is high, the risk of stock outs is greater. By taking these high andlow demand periods into account, this stocking algorithm tries to minimizethe number of stock outs, and to avoid excess inventory.

Lead time demand is used because the basic mission of distribution is tooffer shorter lead times to your customer than you experience from yourvendors. This is the purpose of inventory. If your supplier lead times were

the same as those you quote to your customers, inventory would not benecessary. You would simply take an order from your customer, hand it toyour vendor, and ask the vendor to ship the product directly to yourcustomer.

Because the customer wants the product now, lead time and safety stockcome into play. The simplified ROP formula equals lead time demand plus

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Reorder Point

safety stock. Safety stock is needed to accommodate variability of demandand the difficulty this variability presents in forecasting.

If you knew exactly what your customers wanted and when they wanted it,your inventory would decrease in an ideal, straight-line relationship overtime, as it does in Figure 7. And, under ideal circumstances, you wouldknow the exact lead time needed to replenish your inventory. Then, theideal inventory depletion would begin anew.

Figure 7. Ideal depletion

Unfortunately, the world of supply and demand, vendors, manufacturers,

and customers do not operate under ideal circumstances. There are manyinterruptions and changes, from variability in demand quantities, vendorshortages, transportation problems, manufacturing downtimes, demandchanges, to natural disasters. These fluctuations make inventory strategyvery important.

Lets consider the inventory versus time plot without an inventory cushion,or safety stock. Using the graph in Figure 8, note that if you use averagedemand as your inventory reorder determinant, you can expect toexperience stock outs at least half of the time, as shown by the lower halfof the vertically plotted standard distribution, or bell, curve. The other half

of the time you can expect to have plenty of material on hand. Thechallenge then is to determine some percentage of demand that you expectto be unfilled, but one that is acceptable to both your customers and yourcompany.

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Reorder Point

Figure 8. Without Safety Stock

Once you have determined the acceptable level of unfilled demand, it iseasy to establish the level of safety stock. As shown on Figure 9, yousimply extend the point at which your acceptable level of unfilled demandintersects the bell curve, in a straight and horizontal line, back to the Y-axis, which is the inventory level scale. The area delineated by the averagedemand line and the acceptable level line represents the level of safetystock.

This graphically illustrates your goal of maintaining a certain level of

inventory at the distribution center and elsewhere so that stock outs arekept at an acceptable level.

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Reorder Point

Figure 9. With Safety Stock

Lets consider the standard distribution, or bell curve, in more detail(Figure 10). In statistics as well as everyday business, this type of statisticalplotting is common.

Figure 10. Standard Distribution Curve

Each bell curve has an average point, which is the highest point on thecurve. In statistics, this point is called the mean. The Greek letter sigma, ,is the standard deviation from the mean.

As with any standard distribution, as you go out farther from the meanthere is always the possibility of the occurrence happening. In thedistribution environment, the more you want to limit the number of stockouts, the farther out on the curve you must go. This, in turn, means that toreduce the number of stock outs to a very small number, you must increase

your safety stock dramatically.

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Level of Safety Stock


Determining the level of safety stock to maintain at a distribution center isan important and subjective function of logistics and distribution. Twofactors that influence this determination are percent of filled demand and

probability of stock out. As an example, look at the simplified plot ofrequired safety stock as a function of percentage of filled demand, Figure11. This plot shows that as the percent of filled demand, or serviceexpectation, increases, the level of required safety stock increases.

Figure 11. Safety Stock versus Demand

On the other hand, if you were to plot required safety stock as a function ofprobability of stock out, the reverse would be true. That is, as theprobability of stock out increases, the level of required safety stockdecreases.

The selection of the optimum point on either plot is the criticaldetermination. Maintaining a high level of safety stock is very expensive.Maintaining a low level impairs the level of service and thus customersatisfaction. Therefore, it is necessary to set these levels, estimating what isaffordable and what level of safety stock is acceptable.

With the level of safety stock determined, you can add lead time demandto it to calculate your reorder point. The goal is to achieve an acceptablelevel of filled demand, while maintaining an acceptable probability ofstock outs. You will need to know, one lead time away, when shipmentsmust be made.



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Lets now combine several of the plots already discussed into onesummary-type plot of inventory versus time, Figure 12. Here you see thatwhen the inventory level, which begins at a level higher than the averageinventory level, drops to the reorder point, an order is initiated, as shownby the dotted line, at a time that compensates for the expected lead timedemand. By doing so, the inventory level is restored no later than at theend of the lead time demand period. If all goes as planned, the level ofsafety stock is maintained.

Figure 12. Inventory/Time with ROP

This summary-type plot is the end result of understanding reorder points.



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Lead Time Variability

Forecasting is not an exact science. The forecast for lead time demand canvary significantly, as illustrated in Figure 13 by the dashed bell curve.

Figure 13. Lead Time Variability

However, this forecast is used to determine how much safety stock tomaintain. If the lead time, or the components of lead time, areunpredictable, it is important to understand that distribution and have moresafety stock to cover the lead time variations. A replenishment order maytake one expected lead time, or it may arrive in longer or shorter amountsof time. This provides another statistical distribution to consider. It resultsin a distribution curve like the solid line bell curve shown in Figure 13above.

Because the variability of lead times and transport replenishment cyclesmakes things more complicated, it is necessary to make them aspredictable as possible. Then the unpredictability of what the customer

wants is the only factor requiring attention.

Order Quantities

Once you know the reorder point, determine how much inventory shouldbe ordered when you reach the reorder point. Two concerns drive orderquantities: costs and inventories.



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Lead Time Variability

Included in costs are the cost to place the order, the cost to transport thematerial, and the cost to receive the material. In each of these items, thereare costs that are easy to determine and those that are difficult to uncover.The cost to place an order, for example, has obvious components, such asthe cost of completing the order document, the cost of sending the ordervia the mail or transmitting it via a computer, and the cost of recording theorder in an order log. There are also the possible costs of managementapproval cycles and the cost of any volume discount that is not taken.

The cost to transport the material from the supplier to the warehouseshould clearly include the freight cost. Costs are also incurred when payingthe freight bill and managing the transport network. The cost to receive thematerial must include not only the cost of the receiving dock and placingthe material in storage, but the cost of clearing the order from the booksand paying the vendor.

Inventories involve the concept of asset management; namely the cost of

storage and the cost of money. Building or leasing warehouse space tostore products costs the company money. Infrastructure costs, such assecurity and insurance, must also be added. The cost of money refers to thecost of lost opportunity that the company faces when it invests in inventoryrather than some other asset or project. Sometimes, the cost of money iscomputed as the cost of capital, that is stock plus debt.

Order quantities, then, are determined by the interdependencies of thesecost factors. For example, concern regarding one factor, such asinventories, will drive small order quantities. This causes the ordering,transporting, and receiving costs to go up. Conversely, concern overtransport, ordering, minimum vendor production runs, and receiving costswill drive large order quantities, and the amount of inventory goes up.Balancing all of the factors is required.



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Economic Order Quantity

Economic Order Quantity (EOQ)

The task of determining order quantities requires accommodation betweenthe conflicting concerns. A standard, classical formula in inventorymanagement is called the economic order quantity, or EOQ. The formula

has four basic inputs: the forecast; the cost of ordering, which includes thecosts to place the order, transport the material, and store it; the cost ofholding the inventory, which is normally called the carrying cost; and thecost of the item.

Figure 14. Economic Order Quantity

This formula demonstrates how to derive a method that minimizes total

cost based on order quantities. However, the result of the equation, theEOQ, is based on three estimates. The forecast is an estimate. The cost ofordering depends on assumptions based on time taken to complete theorder documents, etc. And, the cost of holding the inventory is based on awhole series of estimates. This makes it an elegant mathematical formulathat bases its answer on three guesses. Its value is only as good as theseestimates.

It has other flaws as well. As an example, if you use a high cost ofordering, you calculate large order quantities and thus order infrequently.If you look at your cost base and divide it by the number of orders

processed, the cost of ordering appears high. But, if you take the sameformula and use a low cost of ordering, youd generate a lot of orders.Then, if the costs are divided by the number of orders, the cost of orderingis low. The equation can become a type of self-fulfilling prophecy.


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Economic Order Quantity

The formula is best used as an advisory tool. It is relatively insensitive tosmall differences in the values used, and thus these differences have anegligible effect on the resulting EOQ. Order quantities that are close tothe EOQ have a total cost that is not too different than that of the EOQ.The EOQ provides a good guideline.


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Meeting Targeted Inventory Levels


In the plot of inventory versus time (Figure 15), note the dynamics ofreorder point and order quantity. If an order is placed when the reorderpoint is reached, some more product will be used before the new order

arrives. This lead time demand reduces the stock at the location to thesafety stock level. On average, you can expect to have safety stock plusone-half an order quantity on the shelf. This, however, is not the wholestory. When an order is placed, the company commits to placing thoseassets on the shelf. The average committed inventory is then the ROP plusone-half the order quantity. This is especially important when ownership ofthe inventory passes to the company when the material is shipped or whenreplenishing a distribution stock point.

Figure 15. Meeting Targeted Level


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These two major variables, average committed inventory and averageinventory on the shelf, are important when creating an inventory target.Reducing inventory requirements must then incorporate ways of reducingthese variables. The amount of inventory can be reduced in two ways:

by reducing the order quantities

by reducing the reorder points.

Reducing order quantities affects our logistics costs. Reducing ROPsarbitrarily, in effect, lowers our safety stock, which, in turn, puts theservice levels in jeopardy unless process changes are made to shorten thelead time. Reducing the lead time demand component of the ROP alsoreduces the required safety stock, as it only has to cover variations indemand over shorter lead times.


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Changing Lead Time Performance


Changing lead time performance is the best way of affecting overalllogistics inventories and level of service. The obvious lead time of concernis the supplier lead time; that is, how long it takes from when a request is

made for the product to be built until the item is received. Also of concernare internal lead times; that is, how long it takes to fill an order, how longit takes to replenish a distribution center, etc.

If the lead time is unpredictable, or highly variable, the lead time is asdisplayed in the top of Figure 16. Some degree of certainty is desired as thedeterminant of lead time; for example, 95% of the time it takes this long toget the material to a certain distribution center. This tends to encouragestocking those items that are replenished a lot earlier than the 95% time.And, if it is not possible to predict which items these are, there may be alot of wrong assets in inventory.

The trick is to get this distribution more under control, which is expensive.But, when the 95% level is not too far from the average lead time, asshown in the bottom of Figure 16, there is predictability in the process. It ispossible to plan and target inventory and costs better.


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Figure 16. Changing Lead Time


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Financial Impact of Logistics

Return on assets or ROA is determined by the revenues, expenses, andassets of a corporation. It can be expressed by the simple formula of ROAequals revenues minus expenses divided by assets. Because logistics

purchases the inventory to fill the pipeline, it has a major impact on assets.As inventories increase, the return on assets decreases. When inventoriesare reduced, the return on assets increases.

Additionally, logistics costs are added to the companys overall expenses.These expenses can vary depending on how much or how little inventorythere is to store and maintain. Logistics also affects the companysrevenues. If the right assets are not purchased, or planned for theappropriate level of sales, the company does not benefit from the revenue.Inventory management significantly affects the companys ROA.


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Priority Ranking

Priority Ranking -- The Pareto Principle

The concepts and theories about logistics and distribution have beencovered. How these concepts and theories are applied is critical to overalllogistics management. The first, and most important application, is priority

ranking, which is also called the Pareto Principle after the statistician whodiscovered these relationships. The Pareto Principle indicates that 20% of acompanys product or customers typically account for 80% of demand.Likewise, it is often true that 20% of the items in the inventory account for80% of inventory value. However, the top 20% of the demand items maynot be the top 20% of the inventory items. Also the highest demandeditems in terms of pieces may not necessarily be the items that have thehighest sales in terms of dollars.

To manage inventory value, it is necessary to consider how parts rankbased on dollars of sales or demand. To manage the service levels requires

considering the ranking based on pieces of demand.

Ranking Example

The following is an example of how to perform a ranking analysis. Thischart illustrates the parts, how much they cost, what the demand for themis, and what the demand equals in value. For example, part 1 costs onepenny, the demand is 1000 units, and the value of the demand is $10.00.

Part Cost Demand Value of Demand

1 $.01 1000 $10.002 $1.00 20 $20.003 $2.00 30 $60.004 $.50 50 $25.00

Figure 17

The ranking is calculated on the basis of demand by pieces. The parts aresorted by descending piece usage, as shown in Figure 18.

The cumulative column in Figure 18 shows the total demand for all of the

parts up to this point. The percent column is based on the ratio of thecumulative to the total demand for the parts.


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Priority Ranking

Part Cost Demand Cum Pcs %1 $.01 1000 1000 91%4 $.50 50 1050 95%

3 $2.00 30 1080 98%2 $1.00 20 1100 100%

Figure 18

The ranking can also be calculated on the basis of demand by dollar value,as shown in Figure 19. The extended value of the demand is accumulatedfor each part and the percents based on the cumulative dollars is calculatedto the total.

Part Cost Value of Demand Cum $ %3 $2.00 $60.00 $60.00 52%4 $.50 $25.00 $85.00 74%2 $1.00 $20.00 $105.00 91%1 $.01 $10.00 $115.00 100%

Figure 19

Notice that in the ranking by pieces the top item is part 1, but part 1 is thelast item in the ranking by dollars. While part 3 accounts for only 3% ofthe pieces demand, it accounts for 52% of the dollars usage, and is on the

top of the dollars ranking.

To manage the service level, assuming it is calculated in pieces of demandsatisfied, focus should be on part 1. It has the highest demand, and thecustomer will not understand if it is out of stock. In managing theinventory investment, however, the most important part is 3, with part 1being least important. Ordering more of part 3 can add significantly to thecompanys investment. Part 1, on the other hand, does not affect theinvestment much, but it helps the level of service. An inventory strategywould be to manage the inventory of part 3 tightly, and keep a largeinventory of part 1.

The choices are more difficult in the middle of the ranking. For example,part 4 is second on both lists. It is important from both a service level andthe investment perspective because it accounts for 4% of the total demandin pieces and 22% of the total dollars demand. The inventory for this partshould be watched closely, but should not run too low or else the level ofservice may suffer.


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Priority Ranking

Thus, safety stocks should be set high on part 1, low on part 3, and inbetween on part 4. This example makes it relatively easy to see what to do;there are only a few items, and they can be compared easily. It is muchharder to extrapolate to an actual business because the number of lineitems is much greater. This makes it necessary to set inventory policies bygroups of parts. Inventory policies are then set for each group or sub-group.These policies are based on how important a part is to the level of service,and how important the part is to the investment. Parts can then be assignedcodes to rank them in importance.

Rankings can be used for a number of factors. For example, if the space ina service technicians trunk is an issue, a ranking can be used to categorizethe parts by cubic volume. To help bridge the gap that may exist betweenMarketing (or Sales) objectives and what Logistics and Distribution isattempting to accomplish, a ranking on the revenue or gross margin by lineitem can be helpful.

The example illustrated that the ranking using pieces demand was differentfrom that using dollars demand. Similarly, the highest revenue items, orthe highest gross margin items, may not be the same as those for which youhave the most pieces or dollars demand.


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Inventory Quality

Inventory Quality

Inventory quality is the appropriate statistical distribution of a companysinventory. To determine how well the inventory is performing, it isnecessary to look at periods of stock on hand.

Similar to statistical process control, where the effort is focused on havinga manageable and predictable distribution of results, inventory quality ishaving a manageable and predictable distribution of inventory.

In Figure 20, the darker bars represent the current inventory, and thelighter bars represent the future inventory (projected out approximately sixmonths). The horizontal axis is periods of stock, or POS, ranges. Thevertical axis represents the number of parts with stock in that range. Theobjective is to have the projected inventory in line with the idealdistribution, which occurs in this graph. The number of parts with high

levels of POS decreases, and the number of parts with a projectedinventory in the middle ranges increase. The intention is for inventorybalances to be more under control and more predictable.

Figure 20. Inventory Quality Illustration

"Predictable""In Control"


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Inventory Quality

Improving Inventory Quality

To improve inventory quality, the following must occur:

1. Inventory polices must be followed. Ordering in advance, or just incase, drives inventories too high. Making up for stocking too much

inventory by keeping some items below minimum stock levels, drivesinventories too low.

2. Forecast must be correct or good. While theyll never be 100%accurate, forecasts should be close to what actually will happen. Thisprovides the basis for planning todays and tomorrows inventory.

3. The inventory records must be accurate. They must reflect what isactually happening, in terms of the quantity of inventory and thequantity and timing of demand.

4. The physical logistics processes must be predictable. There cant be awide variation in the performance of the physical distribution,warehousing, replenishment of outlying stock points, or replenishmentof service technicians.

Until you have a process that is under control and predictable, changingparameters does not result in anything that can be predicted.


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Forecasting Techniques


The following forecasting techniques are listed in ascending order ofcomplexity:

Moving average: For example, a three-month moving average isthe last three months demand divided by three. It is simple tocalculate and use.

Exponential smoothing: Two common types are single smoothing,which is like moving average, and double smoothing, which is atrended forecast.

Leading indicator: This technique ties the expected part needs toanother anticipated activity, such as machine installs or machineremovals.

Time series: These include regression analysis, seasonal forecast,

etc. Delphi method: A non-mathematical method that involves asking

several people in the organization what they think will be sold. Thebest of these estimates, or perhaps the average, is then used as theforecast.

The incorporation of market intelligence into any of themathematical forecasting techniques can provide a much morereliable forecast. It is helpful to understand why demand is at thelevel it is.

Forecasting Objectives

All of the forecasting methods have advantages and disadvantages. Themethod that should be selected is the one that provides a forecast that isroughly right. Two things can go wrong with a forecast:

It can be consistently too high or too low. This is a commonproblem with moving average type forecasts when demand isincreasing or decreasing.

Problems can occur when one periods forecast is drasticallywrong. The forecast for one period may not even be close to theactual demand.

The objective is to develop forecasts or use forecasting techniques thatproduce a forecast that is:

Neither too high nor too low

Has small differences between the forecast and actual demand.


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Forecast Versus Data

Lets consider a typical forecast and how it responds to changes in data.The chart at the top of Figure 21, has an increasing demand trend; thelower chart has a decreasing demand trend. The forecast used in each case

is a three-month moving average, which, because it is fairly short term, ishighly responsive to changes in the data. In both charts, the forecast ischanging in response to the data.

0

10

20

30

40

50

1 2 3 4 5 6 7 8 9 10 11

Months

Qu

antity Demand

Forecast

0

10

20

30

40

50

1 2 3 4 5 6 7 8 9 10 11

Months

Quantity Demand

Forecast

Figure 21. Forecast versus Data


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In the top chart, a horizontal line has been drawn from each forecast point.These lines reflect what the forecast would look like if it were used formultiple periods in the future. The moving average looks fine on a month-by-month basis, although it lags behind the actuals. It becomes lesseffective as it is used to forecast three to four months into the future. The

errors get larger the farther out the forecast is used.

The impact of this difference accumulates when the forecast is used toreplenish inventory. In the top chart, Figure 21, the inventory on hand isgradually used, since each month the actual demand is slightly higher thanthe forecast. The stock keeps decreasing and eventually stock outs areexperienced. Conversely, on a decreasing demand trend, inventory is builtto excess over time.

It is therefore necessary to pay attention to forecast errors. Any bias in theforecasts can cause both stock outs and excessive inventories. The straight-line forecast produced by the moving average method is not appropriate inthis case because the demand demonstrates a definite trend.

Total Forecast and Demand

To look for bias in the forecast, it is necessary to examine each partsforecast and demand. Aggregate information does not reveal bias until it ismuch too late. The previous two graphs were combined into the followingtotal forecast and demand chart, Figure 22, which shows how the inventoryis performing. Aggregate demand (either pieces or dollars) for severalitems is compared with aggregate forecasts for the same items typically toprovide a reality check regarding forecasting.


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50

55

60

65

70

75

1 2 3 4 5 6 7 8 9 10 11

Months

Quantity

Demand

Forecast

Figure 22. Total Forecast

In the above graph, Figure 22, the total forecasts look good. In fact, inmonth 5, the forecast and the actual were the same. So, if just the aggregateis looked at, a false sense of security can be achieved. Meanwhile, one itemis building excess inventory and the other item is approaching stock out.

An important fact to keep in mind is that waiting for monthly data onforecasts and demand often times is not sufficient. For key items, look atweekly, or even daily, demand. The earlier the demand trend is spotted, thesooner the response can be initiated.


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Forecast Error/Seasonality

The total of your individual month errors is normally called the forecasterror. Forecast errors can be fairly large without having a significant biasin situations that have a large degree of seasonality. This scenario, depicted

in Figure 23, causes the following to occur:

Figure 23. Forecast Error

The demand pattern tends to fluctuate around the forecast or normalexpected level. This is seasonality. If the forecast is like the dashed line,there is no bias because cumulative errors will be relatively small,

depending on how long the cycles last.

Seasonality can be a misleading term. It can exist in data without beinglinked to the seasons of spring, summer, fall, and winter. One commoncausal of seasonality is the differing number of business days in eachfinancial period.

In a situation where there is significant seasonality, it is possible to havelarge errors, but with little or no bias. If the demand swings are largeenough, the item can have periods of inventory build up, followed byperiods of stock outs. Both bias and the size of the forecast errors are

important to monitor, and must be tracked at the item level.


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Forecast Types

There are two kinds of forecasts: independent and dependent. They arebased on the kind of demand and the use to which an item is placed.Examples of independent demand are service technician repair parts,

customer sales, etc. They are called independent because they are notdetermined by, or dependent on, any scheduled activity.

Dependent demands are requirements for items needed to manufacture, re-manufacture, or refurbish a product. They are called dependent because thelevel of demand depends on other processes. Dependent demands can becalculated.

You will often begin with an independent demand forecast. For example,you can forecast how many machines will be refurbished orremanufactured. To refurbish or remanufacture machines, a certainnumber of parts are needed. Manufacturing and refurbishing machinescreates dependent demand because theyre driven by a basic calculation,the bill of material calculation. The bill of material then drives a series ofdependent demands.

Example of a Bill of Material

Lets consider the making of a felt-tip marker. To make it, you need a cap,a marker body, some felt, and some ink. After forecasting how manymarkers are needed, it is possible to calculate how many caps, bodies, feltpieces and ink are required. The demand for the component items isdependent on how they are to be built. They may be built in advance and

built to accommodate the economies of scale. A manufacturing line maybuild thousands at a time, and lot sizes will determine demand for thecomponent parts, not the forecast of pen sales, which may be onlyhundreds per week.

Lot sizes, or economic order quantities (EOQs), tend to be driven by whatsomebody views as an efficient production quantity. Some manufacturers,such as Toyota and Honda, are trying to make the economic lot size equalto the economic build quantity equal to one. It is their goal to make it asefficient to build one item at a time as it is to build 1000 at a time. Thisway the manufacturing process can be brought in line with the customer

demand process.


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Mixing the Demands

Mixing dependent and independent demands can negatively affect theprocesses for forecasting and reorder points. The dependent demand canoverwhelm the independent demand.

If there is a process to forecast and set ROPs for an independent demandstream based on demand for repair parts and a dependent demand, such aspipeline fill, is added, the stock balance is quickly reduced and the demandprofile is compromised.

When you have dependent demand mixing with independent demand, itcreates a large degree of uncertainty. Trying to cover it with safety stock,means a lot more safety stock will be needed. The problem is that thesafety stock is not needed all of the time, just some of the time. Safetystock should not be needed to cover the dependent demand, because whatis needed should be known in advance.

Managing the Demands

How are the two types of demand managed in a logistics environment?Logistics and distribution are typically organized hierarchically because ofservice levels and economies of scale. There is a mixture of dependentdemands for refurbishing and initial and/or replenishment stocking alongwith the independent demands driven by customer orders and machinerepair.

The replenishment demand, which is an internal order, is different from a

customer order. A replenishment order is created because product isexpected to be needed in the future. The customer expects to use thematerial now.


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Demand Summary Example

Period

1 2 3 4

Independent Demand 100 95 98 104

Dependent Demand 50 0 0 50

Total Requirements 150 95 98 154

Minimum Inventory(200 Pieces)

Target BeginningOn-Hand Balance(OHB)

350 295 298 354

Started with 400 Pieces

Beginning OHB

Demand

Ending OHB

400

-150

250

250

- 95

155

155

- 98

57

57

-154

-103

For this example, lets say we have a distribution center, which hasindependent demand for the next four periods of 100, 95, 98, and 104, afairly stable demand pattern. A dependent demand of 50 (a replenishmentorder) is added in periods one and four. The total requirements is the sumof the two demand streams.

The Safety Stock is 200 units; this is the minimum you want to have onhand. If this is added to the individual period requirements, the total is thedesired beginning on-hand balance; the amount of stock you want at thebeginning of each period. These totals are 350, 295, 298, and 354.

If you started with 400 pieces and each period you deduct the totaldemand, without any replenishment stock, the inventory shrinks and goesnegative. In this example, the stock out at the end of the fourth period is103 pieces.


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Replenishment Order Example

Period

1 2 3 4

Independent Demand 100 95 98 104

Dependent Demand 50 0 0 50

Total Requirements 150 95 98 154

Minimum Inventory(200 Pieces)

Target BeginningOn-Hand Balance

350 295 298 354

Started with 400 Pieces

Beginning OHB

Demand

Ending OHB

Orders

400

-150

250

45

295

- 95

200

98

298

- 98

200

154

354

-154

200

Using the same example, we will calculate when a replenishment orderneeds to be initiated. Note the on hand balance at the end of period one is250, which is less than the required on hand balance of 295 at the

beginning of period two. There is a shortfall of 45 pieces, so areplenishment for this quantity is needed during period one to bring thebeginning on hand balance for period two to the required 295.

When the replenishment order is added, the beginning balance of periodtwo is sufficient, but period three is short. Another order for 98 pieces isnecessary during period two.

In period three, you end with 200 pieces on hand, but would like to startperiod four with 354 pieces. An order for 154 pieces is necessary duringperiod three.

Now the inventory is balanced for the next four periods. The minimuminventory levels are maintained and enough material is coming in to coverthe forecasted demands during this time.


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The supplier would have received orders for 45, 98, and 154 pieces insuccessive periods. They would expect next periods order to beapproximately 200. However, the normal demand is only about 100 perperiod, and the dependent demand is 50. The demand picture provided tothe supplier looks like it is going out of control.

This is a classic problem within logistics and distribution. End customerdemand is buffered by stocking rules and decisions, and the resulting orderpicture provided to the next higher tier of the hierarchy is very muchdifferent than what is actually happening. The total amount ordered duringperiods one through three is correct, but the stock rules have placed timingrestrictions on when the material is wanted. Ultimately the supplyinglocation will probably buy too much inventory based on an increasingtrend in the demand.


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Buffered Demand

How to Deal with Buffered Demand

How can this situation be rectified? Two solutions are:

1. Do all planning at the high level, including replenishment planning.

This is called distribution requirements planning. All stockbalances and forecasts are accumulated to the highest level of thesupply chain. Calculations are made that determine whenreplenishment shipments are necessary, and orders are calculatedbased on the total netted requirements of the supply chain. Whilethis eliminates the problem within the supply chain, it does notnecessarily provide a clear picture to the suppliers.

2. The second method is called managing the velocity. This involveslimiting the stocking decisions made at lower levels of thehierarchy. The objective is to make the individual periodrequirements align closely with the actual demand. This is the

concept behind strategies such as vendor-managed inventory; thevendor determines how much material to ship to its customer basedon the customers demands and inventory requirements.

Another step towards improving inventory velocity is to bypass individualsegments of the supply chain. For example, a vendor may be able to shipthe material more economically to customers than to regional warehouses.Recognizing this economic fact and modifying the process accordingly,one segment in the supply chain is bypassed, and the inventory velocity isimproved.


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Buffered Demand

Buffered Demand Factors

For either of these solutions to work well, three critical factors must be inplace.

The process must be in control. It must reliably and predictablyproduce the same result time after time.

The lead times must be short. Longer lead times create greateruncertainty and less predictability.

The forecast must be accurate. It should avoid problems of bias andlarge individual errors.

These three factors are important for the information flows as they are forthe material flows. The processes for gathering the information andcommunicating it must be in control. The customers demand must beunderstood. There cannot be a long lead time to get the information

necessary to drive the material flows. Forecasts of activity are dependenton information flows. Forecasts must be timely and use a period relevantto the overall expectations of the supply chain.

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