Inventory/Forecasting Handout

Inventory Control:

A Pre-Reader

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Contents

Contents

Contents.................................................................................................................................... iii

Introduction..................................................................................................................................

Logistics Triangle..........................................................................................................................

Supply Pipeline.......................................................................................................................

Value Chains..........................................................................................................................

Stocking Algorithms......................................................................................................................

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

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

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

Order Quantities...........................................................................................................................

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

Meeting Targeted Inventory Levels...............................................................................................

Changing Lead Time Performance................................................................................................

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

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

Ranking Example...................................................................................................................

Inventory Quality..........................................................................................................................

improving inventory quality.....................................................................................................

Forecasting Techniques................................................................................................................

Forecasting Objectives...........................................................................................................

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

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

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

Forecast Types......................................................................................................................

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

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

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

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

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

Inventory Control iii

Logistics Triangle

Introduction

Effective management of the supply chain is a critical part of a company’s ability to deliver a consistently high level of customer satisfaction at acceptable levels of return on assets. This document discusses the basic principles used within the logistics and distribution environment to manage the supply chain. Increasing your understanding of these principles can help you more effectively manage inventory levels.

Logistics Triangle

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

Figure 1. Logistics Triangle

Level of service, inventory, and costs affect all aspects of the logistics and distribution environment. By managing these factors efficiently, effectively, and economically, companies can achieve their goals of total customer 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, making process improvement the ultimate goal.

Inventory Control 1

Logistics Triangle

Supply Pipeline

The Harvard Business School's Michael Porter developed a theory called “Value Chains.” He defines a value chain as a series of process steps that add value to a product or service at each step. In other words, as product moves 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 “Supply Chains”, which is also a value chain. Both concepts describe adding value to 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 is perceived as separate, yet interconnected. The links are visualized as connected, but they are still individual entities. The better way to conceptualize 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

2 Inventory Control

Logistics Triangle

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

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

Within the supply pipeline there are two important factors: information and material or assets. The information is in the form of orders and availability, 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 money’s worth?

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

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

No segment of the pipeline should be considered sacred. Countless examples exist where manufacturing, warehousing, transport, and the distributor have been bypassed, or have had their traditional functions changed, to provide better levels of customer satisfaction at lower costs with reduced asset levels.

Inventory Control 3

Logistics Triangle

Value Chains

Functional value, situational value, and time value are terms used by Michael 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 must have the right part, at the right place, at the right time. It is also critical to consider the right cost, as a company must maintain its existence in business through proper cost controls.

The four “rights”, part, place, time, and cost, relate directly to the logistics triangle. They impact the level of service, inventory and costs, which directly affects customer satisfaction. Let’s consider how this conceptual framework can be used to actually change the way business is carried out.

4 Inventory Control

Stocking Algorithms

Stocking Algorithms

Figure 4 illustrates a simple inventory over time scenario. As demand comes in, the inventory goes down. It goes down in increments; some small, some large. Periodically, the inventory is replenished. If the inventory is allowed to fall below a zero level, then a stock out occurs. The goal 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 at which you experience an acceptable number of stock outs. The objective is to keep stock outs at a reasonable and manageable level that is acceptable to the customer, because it is not possible to be entirely rid of them.

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

The next important element to define is lead time. Lead time is the time required 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 from point 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 their shelves, load it onto a vehicle, and get it to point B, and

Inventory Control 5

Stocking Algorithms

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

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

Sometimes the non-physical aspects of logistics take the longest to perform. There may be a process whereby material is pulled, packed and loaded in a day, transport takes another day; and unloading and receiving takes a day. However, if you want the shipment to be made only once a week, this requirement overrides all of the other processing times regardless how efficient they are.

Therefore, in the logistics and distribution environment, the inventory versus time plot is enhanced by using a reorder point in the effort to become more efficient.

6 Inventory Control

Reorder Point

Reorder Point (ROP)

In the effort to enhance the inventory versus time plot, various inventory levels 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 depletes and reaches this point, it is immediately apparent that it is time for a replenishment order. While the replenishment order is in process, inventory levels continue to fall. The stock is targeted for replenishment at the safety stock level of inventory. Therefore, ROP = safety stock plus lead time demand.

There has been a lot of effort spent in determining the best way to calculate safety stock. Basically the calculation takes into account two basic variables: the forecast and the lead time. The following are four types of safety 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. Let’s assume that you want to have three weeks of supply at the distribution center, so whenever the inventory level drops to three weeks of supply plus the lead time demand you order more material. The problem with this

Inventory Control 7

Reorder Point

method is that it does not take demand variances into account. It gives a targeted inventory level with no consideration of the desired level of service. Demand variances will drive the level of service from an end user or customer perspective.

Figure 6 indicates the distribution of demand or sales. There is an average demand, with some numbers higher than average and some numbers lower than average. The probability of stock out, piece part fill rate, and stockout occurrences methods of calculating safety stock use this demand profile and 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 when demand is high, the risk of stock outs is greater. By taking these high and low demand periods into account, this stocking algorithm tries to minimize the number of stock outs, and to avoid excess inventory.

Lead time demand is used because the basic mission of distribution is to offer shorter lead times to your customer than you experience from your vendors. This is the purpose of inventory. If your supplier lead times were the same as those you quote to your customers, inventory would not be necessary. You would simply take an order from your customer, hand it to your vendor, and ask the vendor to ship the product directly to your customer.

8 Inventory Control

Reorder Point

Because the customer wants the product now, lead time and safety stock come into play. The simplified ROP formula equals lead time demand plus safety stock. Safety stock is needed to accommodate variability of demand and 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 over time, as it does in Figure 7. And, under ideal circumstances, you would know the exact lead time needed to replenish your inventory. Then, the ideal 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 many interruptions and changes, from variability in demand quantities, vendor shortages, transportation problems, manufacturing downtimes, demand changes, to natural disasters. These fluctuations make inventory strategy very important.

Let’s consider the inventory versus time plot without an inventory cushion, or safety stock. Using the graph in Figure 8, note that if you use average demand as your inventory reorder determinant, you can expect to experience stock outs at least half of the time, as shown by the lower half of the vertically plotted standard distribution, or bell, curve. The other half of the time you can expect to have plenty of material on hand. The challenge then is to determine some percentage of demand that you expect to be unfilled, but one that is acceptable to both your customers and your company.

Inventory Control 9

Reorder Point

Figure 8. Without Safety Stock

Once you have determined the acceptable level of unfilled demand, it is easy to establish the level of safety stock. As shown on Figure 9, you simply extend the point at which your acceptable level of unfilled demand intersects 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 average demand line and the acceptable level line represents the level of safety stock.

This graphically illustrates your goal of maintaining a certain level of inventory at the distribution center and elsewhere so that stock outs are kept at an acceptable level.

10 Inventory Control

Reorder Point

Figure 9. With Safety Stock

Let’s consider the standard distribution, or bell curve, in more detail (Figure 10). In statistics as well as everyday business, this type of statistical plotting is common.

Figure 10. Standard Distribution Curve

Each bell curve has an average point, which is the highest point on the curve. 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 mean there is always the possibility of the occurrence happening. In the distribution environment, the more you want to limit the number of stock outs, the farther out on the curve you must go. This, in turn, means that to reduce the number of stock outs to a very small number, you must increase your safety stock dramatically.

Inventory Control 11

Level of Safety Stock


Determining the level of safety stock to maintain at a distribution center is an important and subjective function of logistics and distribution. Two factors that influence this determination are percent of filled demand and probability of stock out. As an example, look at the simplified plot of required safety stock as a function of percentage of filled demand, Figure 11. This plot shows that as the percent of filled demand, or service expectation, 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 of probability of stock out, the reverse would be true. That is, as the probability of stock out increases, the level of required safety stock decreases.

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

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



Let’s now combine several of the plots already discussed into one summary-type plot of inventory versus time, Figure 12. Here you see that when the inventory level, which begins at a level higher than the average inventory level, drops to the reorder point, an order is initiated, as shown by the dotted line, at a time that compensates for the expected lead time demand. By doing so, the inventory level is restored no later than at the end of the lead time demand period. If all goes as planned, the level of safety stock is maintained.

Figure 12. Inventory/Time with ROP

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



Lead Time Variability

Forecasting is not an exact science. The forecast for lead time demand can vary 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 to maintain. If the lead time, or the components of lead time, are unpredictable, it is important to understand that distribution and have more safety stock to cover the lead time variations. A replenishment order may take one expected lead time, or it may arrive in longer or shorter amounts of time. This provides another statistical distribution to consider. It results in a distribution curve like the solid line bell curve shown in Figure 13 above.

Because the variability of lead times and transport replenishment cycles makes things more complicated, it is necessary to make them as predictable 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 should be ordered when you reach the reorder point. Two concerns drive order quantities: costs and inventories.


Lead Time Variability

Included in costs are the cost to place the order, the cost to transport the material, and the cost to receive the material. In each of these items, there are 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 as the cost of completing the order document, the cost of sending the order via the mail or transmitting it via a computer, and the cost of recording the order in an order log. There are also the possible costs of management approval cycles and the cost of any volume discount that is not taken.

The cost to transport the material from the supplier to the warehouse should clearly include the freight cost. Costs are also incurred when paying the freight bill and managing the transport network. The cost to receive the material must include not only the cost of the receiving dock and placing the material in storage, but the cost of clearing the order from the books and 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 to store products costs the company money. Infrastructure costs, such as security and insurance, must also be added. The cost of money refers to the cost of lost opportunity that the company faces when it invests in inventory rather than some other asset or project. Sometimes, the cost of money is computed as the “cost of capital,” that is stock plus debt.

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


Economic Order Quantity

Economic Order Quantity (EOQ)

The task of determining order quantities requires accommodation between the conflicting concerns. A standard, classical formula in inventory management is called the economic order quantity, or EOQ. The formula has four basic inputs: the forecast; the cost of ordering, which includes the costs to place the order, transport the material, and store it; the cost of holding the inventory, which is normally called the carrying cost; and the cost 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, the EOQ, is based on three estimates. The forecast is an estimate. The cost of ordering depends on assumptions based on time taken to complete the order documents, etc. And, the cost of holding the inventory is based on a whole series of estimates. This makes it an elegant mathematical formula that bases its answer on three guesses. Its value is only as good as these estimates.

It has other flaws as well. As an example, if you use a high cost of ordering, 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 same formula and use a low cost of ordering, you’d generate a lot of orders. Then, if the costs are divided by the number of orders, the cost of ordering is low. The equation can become a type of self-fulfilling prophecy.

Economic Order Quantity

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

Meeting Targeted Inventory Levels


In the plot of inventory versus time (Figure 15), note the dynamics of reorder point and order quantity. If an order is placed when the reorder point is reached, some more product will be used before the new order arrives. This lead time demand reduces the stock at the location to the safety stock level. On average, you can expect to have safety stock plus one-half an order quantity on the shelf. This, however, is not the whole story. When an order is placed, the company commits to placing those assets on the shelf. The average committed inventory is then the ROP plus one-half the order quantity. This is especially important when ownership of the inventory passes to the company when the material is shipped or when replenishing a distribution stock point.

Figure 15. Meeting Targeted Level


These two major variables, average committed inventory and average inventory on the shelf, are important when creating an inventory target. Reducing inventory requirements must then incorporate ways of reducing these 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 ROPs arbitrarily, in effect, lowers our safety stock, which, in turn, puts the service levels in jeopardy unless process changes are made to shorten the lead time. Reducing the lead time demand component of the ROP also reduces the required safety stock, as it only has to cover variations in demand over shorter lead times.

Changing Lead Time Performance


Changing lead time performance is the best way of affecting overall logistics inventories and level of service. The obvious lead time of concern is 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 concern are internal lead times; that is, how long it takes to fill an order, how long it takes to replenish a distribution center, etc.

If the lead time is unpredictable, or highly variable, the lead time is as displayed in the top of Figure 16. Some degree of certainty is desired as the determinant of lead time; for example, 95% of the time it takes this long to get the material to a certain distribution center. This tends to encourage stocking 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 a lot 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, as shown in the bottom of Figure 16, there is predictability in the process. It is possible to plan and target inventory and costs better.


Figure 16. Changing Lead Time


Financial Impact of Logistics

Return on assets or ROA is determined by the revenues, expenses, and assets of a corporation. It can be expressed by the simple formula of ROA equals 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 inventories are reduced, the return on assets increases.

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

Priority Ranking

Priority Ranking -- The Pareto Principle

The concepts and theories about logistics and distribution have been covered. How these concepts and theories are applied is critical to overall logistics management. The first, and most important application, is priority ranking, which is also called the Pareto Principle after the statistician who discovered these relationships. The Pareto Principle indicates that 20% of a company’s product or customers typically account for 80% of demand. Likewise, it is often true that 20% of the items in the inventory account for 80% of inventory value. However, the top 20% of the demand items may not be the top 20% of the inventory items. Also the highest demanded items in terms of pieces may not necessarily be the items that have the highest sales in terms of dollars.

To manage inventory value, it is necessary to consider how parts rank based 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. This chart illustrates the parts, how much they cost, what the demand for them is, and what the demand equals in value. For example, part 1 costs one penny, the demand is 1000 units, and the value of the demand is $10.00.

Part Cost Demand Value of Demand1 $.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 are sorted 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 the cumulative to the total demand for the parts.

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 accumulated for each part and the percents based on the cumulative dollars is calculated to 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 the last item in the ranking by dollars. While part 3 accounts for only 3% of the 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 demand satisfied, focus should be on part 1. It has the highest demand, and the customer will not understand if it is out of stock. In managing the inventory investment, however, the most important part is 3, with part 1 being least important. Ordering more of part 3 can add significantly to the company’s investment. Part 1, on the other hand, does not affect the investment much, but it helps the level of service. An inventory strategy would be to manage the inventory of part 3 tightly, and keep a large inventory 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 and the investment perspective because it accounts for 4% of the total demand in pieces and 22% of the total dollars demand. The inventory for this part should be watched closely, but should not run too low or else the level of service may suffer.

Priority Ranking

Thus, safety stocks should be set high on part 1, low on part 3, and in between 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 much harder to extrapolate to an actual business because the number of line items is much greater. This makes it necessary to set inventory policies by groups 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 assigned codes to rank them in importance.

Rankings can be used for a number of factors. For example, if the space in a service technician’s trunk is an issue, a ranking can be used to categorize the parts by cubic volume. To help bridge the gap that may exist between Marketing (or Sales) objectives and what Logistics and Distribution is attempting to accomplish, a ranking on the revenue or gross margin by line item can be helpful.

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

Inventory Quality

Inventory Quality

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

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

In Figure 20, the darker bars represent the current inventory, and the lighter bars represent the future inventory (projected out approximately six months). The horizontal axis is periods of stock, or POS, ranges. The vertical axis represents the number of parts with stock in that range. The objective is to have the projected inventory in line with the ideal distribution, which occurs in this graph. The number of parts with high levels of POS decreases, and the number of parts with a projected inventory in the middle ranges increase. The intention is for inventory balances to be more under control and more predictable.

Figure 20. Inventory Quality Illustration

"Predictable""In Control"

Inventory Quality

Improving Inventory Quality

To improve inventory quality, the following must occur:

1. Inventory polices must be followed. Ordering in advance, or just in case, drives inventories too high. Making up for stocking too much inventory by keeping some items below minimum stock levels, drives inventories too low.

2. Forecast must be correct or good. While they’ll never be 100% accurate, forecasts should be close to what actually will happen. This provides the basis for planning today’s and tomorrow’s inventory.

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

4. The physical logistics processes must be predictable. There can’t be a wide variation in the performance of the physical distribution, warehousing, replenishment of outlying stock points, or replenishment of service technicians.

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

Forecasting Techniques


The following forecasting techniques are listed in ascending order of complexity:

Moving average: For example, a three-month moving average is the last three months’ demand divided by three. It is simple to calculate and use.

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

Leading indicator: This technique ties the expected part needs to another anticipated activity, such as machine installs or machine removals.

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. The best of these estimates, or perhaps the average, is then used as the forecast.

The incorporation of market intelligence into any of the mathematical forecasting techniques can provide a much more reliable forecast. It is helpful to understand why demand is at the level it is.

Forecasting Objectives

All of the forecasting methods have advantages and disadvantages. The method that should be selected is the one that provides a forecast that is “roughly right.” Two things can go wrong with a forecast:

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

Problems can occur when one period’s forecast is drastically wrong. The forecast for one period may not even be close to the actual demand.

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

Neither too high nor too low

Has small differences between the forecast and actual demand.


Forecast Versus Data

Let’s 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; the lower 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, is highly responsive to changes in the data. In both charts, the forecast is changing in response to the data.

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Figure 21. Forecast versus Data


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 for multiple periods in the future. The moving average looks fine on a month-by-month basis, although it lags behind the actuals. It becomes less effective 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 to replenish inventory. In the top chart, Figure 21, the inventory on hand is gradually used, since each month the actual demand is slightly higher than the forecast. The stock keeps decreasing and eventually stock outs are experienced. Conversely, on a decreasing demand trend, inventory is built to excess over time.

It is therefore necessary to pay attention to forecast errors. Any bias in the forecasts can cause both stock outs and excessive inventories. The straight-line forecast produced by the moving average method is not appropriate in this 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 part’s forecast and demand. Aggregate information does not reveal bias until it is much too late. The previous two graphs were combined into the following total forecast and demand chart, Figure 22, which shows how the inventory is performing. Aggregate demand (either pieces or dollars) for several items is compared with aggregate forecasts for the same items typically to provide a reality check regarding forecasting.


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Figure 22. Total Forecast

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

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


Forecast Error/Seasonality

The total of your individual month errors is normally called the forecast error. Forecast errors can be fairly large without having a significant bias in 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 normal expected 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 being linked to the seasons of spring, summer, fall, and winter. One common causal of seasonality is the differing number of business days in each financial period.

In a situation where there is significant seasonality, it is possible to have large errors, but with little or no bias. If the demand swings are large enough, the item can have periods of inventory build up, followed by periods of stock outs. Both bias and the size of the forecast errors are important to monitor, and must be tracked at the item level.


Forecast Types

There are two kinds of forecasts: independent and dependent. They are based 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 not determined 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 the level of demand depends on other processes. Dependent demands can be calculated.

You will often begin with an independent demand forecast. For example, you can forecast how many machines will be refurbished or remanufactured. To refurbish or remanufacture machines, a certain number of parts are needed. Manufacturing and refurbishing machines creates dependent demand because they’re driven by a basic calculation, the bill of material calculation. The bill of material then drives a series of dependent demands.

Example of a Bill of Material

Let’s 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 many markers are needed, it is possible to calculate how many caps, bodies, felt pieces and ink are required. The demand for the component items is dependent on how they are to be built. They may be built in advance and built to accommodate the economies of scale. A manufacturing line may build thousands at a time, and lot sizes will determine demand for the component parts, not the forecast of pen sales, which may be only hundreds per week.

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


Mixing the Demands

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

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

When you have dependent demand mixing with independent demand, it creates 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 the safety stock is not needed all of the time, just some of the time. Safety stock should not be needed to cover the dependent demand, because what is 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 of service levels and economies of scale. There is a mixture of dependent demands for refurbishing and initial and/or replenishment stocking along with the independent demands driven by customer orders and machine repair.

The replenishment demand, which is an internal order, is different from a customer order. A replenishment order is created because product is expected to be needed in the future. The customer expects to use the material now.


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, let’s say we have a distribution center, which has independent demand for the next four periods of 100, 95, 98, and 104, a fairly stable demand pattern. A dependent demand of 50 (a replenishment order) is added in periods one and four. The total requirements is the sum of the two demand streams.

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

If you started with 400 pieces and each period you deduct the total demand, without any replenishment stock, the inventory shrinks and goes negative. In this example, the stock out at the end of the fourth period is 103 pieces.


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 Beginning On-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 order needs to be initiated. Note the on hand balance at the end of period one is 250, 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 a replenishment for this quantity is needed during period one to bring the beginning on hand balance for period two to the required 295.

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

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

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


The supplier would have received orders for 45, 98, and 154 pieces in successive periods. They would expect next period’s order to be approximately 200. However, the normal demand is only about 100 per period, and the dependent demand is 50. The demand picture provided to the supplier looks like it is going out of control.

This is a classic problem within logistics and distribution. End customer demand is buffered by stocking rules and decisions, and the resulting order picture provided to the next higher tier of the hierarchy is very much different than what is actually happening. The total amount ordered during periods one through three is correct, but the stock rules have placed timing restrictions on when the material is wanted. Ultimately the supplying location will probably buy too much inventory based on an increasing trend in the demand.

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 stock balances and forecasts are accumulated to the highest level of the supply chain. Calculations are made that determine when replenishment shipments are necessary, and orders are calculated based on the total netted requirements of the supply chain. While this eliminates the problem within the supply chain, it does not necessarily provide a clear picture to the suppliers.

2. The second method is called managing the velocity. This involves limiting the stocking decisions made at lower levels of the hierarchy. The objective is to make the individual period requirements align closely with the actual demand. This is the concept behind strategies such as vendor-managed inventory; the vendor determines how much material to ship to its customer based on the customer’s demands and inventory requirements.

Another step towards improving inventory velocity is to bypass individual segments of the supply chain. For example, a vendor may be able to ship the 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 is improved.

Buffered Demand

Buffered Demand Factors

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

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

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

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

These three factors are important for the information flows as they are for the material flows. The processes for gathering the information and communicating it must be in control. The customer’s demand must be understood. There cannot be a long lead time to get the information necessary to drive the material flows. Forecasts of activity are dependent on information flows. Forecasts must be timely and use a period relevant to the overall expectations of the supply chain.

Documents

Inventory/Forecasting Handout