Inventory Management Foundations

A BRIEF HISTORY AND OVERVIEW SCIENTIFIC INVENTORY
MANAGEMENT

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SCIENTIFIC INVENTORY MANAGEMENT

A BRIEF HISTORY AND OVERVIEW

Anders H. Herlitz

EVOLUTION

I find it very interesting that even today a number of corporations still do not
deploy scientific techniques to assist their purchasing departments in their
planning, forecasting, replenishment, and investment chores. The modern “tricks
of the trade” evolved quite naturally with the introduction of computers in the
business world, and progressive organizations started to benefit from the advanced
technology in this field more than a quarter of a century ago! The resulting benefits,
in particular improved cash flows and higher profits, have been enormous. Now,
with the ability to collaborate over the Internet, the benefits go even further.

THE ART OF BUYING

Before the computer and long before the days of the World Wide Web, we used to
be able to keep track of our inventories and replenish them with the help of the
famous, or infamous, stock card. In the late 1950s the computer emerged, and our
stock cards were transformed into small holes in punch cards, which later were
transcribed into magnetic tapes or magnetic disks. Consequently, once a week,
computers printed out the buyer’s stock information in report form. This event was
supposed to be progress! Perhaps it was to some but many buyers, not convinced
of the benefits of these computerized changes, retained their loyal stock cards.
Therefore, most data processing people looked upon these buyers as slow, stubborn,
old-fashioned, and even as the enemy.

In fact, one very important feature of the stock card file was ignored by data
processing: its “scribbleability.” The old cards contained much more than the item
numbers, descriptions, stock status, etc. which were duly converted into machine-
readable files. They also contained little notes, remarks, hints, hunches and other “non-
processable” pieces of information. As a result, it was going to take 25 years before some
buyers would get this scribbleability back, and most buyers today still have to use
notebooks and “remember this” stacks to compensate for this loss.

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Of course, mechanization had its advantages. The stock status reports,
printed by computers, look much better than card files. They are also a lot easier to
work with, provided you have the right information in them. Another very important
advantage of the early stock status report was that the statistics were generated by
the computer, thus eliminating mathematical mistakes.

It was, in fact, the arithmetical ability of the computer that started the
evolution of Scientific Inventory Management. As much as some buyers hated the
computerization of their lives, others realized that they could take advantage of the
capabilities of these new systems and transform their role in the organization.

INTRODUCING SCIENCE TO THE ART OF BUYING

The initial discovery on the road toward computerizing the purchasing process
happened as follows. Buyers recognized to a large extent that they made identical
(or at least very similar) decisions over and over again, week after week. So, some
buyers asked their data processing experts whether it would be possible to enter an
Order Point for each item into the computer files, such that whenever the item
balance fell below the Order Point, an asterisk could be printed to highlight that item.
Needless to say this was easy!

The Regular Order Quantity soon followed the Order Point, as buyers
realized that they basically bought the same quantity each time they replenished an
item. So why not let the computer know, and have the computer remind the buyer?
The stock status report was thus converted into a Suggested Order Report.

It took a few more years before Scientific Inventory Management was
born. Although I don’t know who was first, how it happened is quite clear. All of the
slide rules and mathematical tables, which were available to assist buyers in their
efforts to balance the cost of carrying inventory with the cost of handling the
replenishment, were all derived from one cost balancing formula. The Wilson
Formula, also called the Economic Order Quantity Formula (EOQ), could also be
programmed into the Suggested Order Report!

Now, the question of “How much to buy” could, at least partially, be
answered with the help of science. How about “When to buy?” The Order Point? Well,
if the vendor lead time is two weeks, and you sell 10 units a week, then it would be
foolish not to buy when you have less than 20 units on hand.

A BRIEF HISTORY AND OVERVIEW 3

Then, of course, selling 10 a week doesn’t mean selling exactly 10 every
week. Also, the lead time is not always exactly two weeks, and the suppliers don’t
always ship exactly what we order, so we need a bit of a buffer, often called a
safety stock.

It was evident that to answer the question of “When to buy” three numbers
had to be derived from statistics:

Ẅ Customer demand forecast
ẅ Vendor lead-time forecast
Ẇ Safety stock measurement
Weighted averages came in handy for the first two, and gut feel or “hip shooting”
normally took care of the safety stock. Obviously, not all the answers were perfect,
but Scientific Inventory Management was a reality!

THE IMPACT OF “IMPACT”

Of all the efforts made to develop a reliable Scientific Inventory Management system,
one stands out: IBM’s joint study with six chain or wholesale companies in the
early 1960s. The project was designed to explore how to use computers to enhance
inventory management. The result was a set of computer programs for the IBM 1401
computer called “Wholesale IMPACT,” which stood for Wholesale Inventory
Management Programs and Control Techniques.

IBM could not have found a more appropriate name or acronym, since few
computer program developments in the ’60s made a bigger impact on the business
world it addressed than did IMPACT. Even today, most computer manufacturers
and software houses catering to the distribution trade have (or claim to have)
derivatives of Wholesale IMPACT.

IMPACT THE PIONEER

IMPACT employed the Wilson formula to calculate EOQs, exponential smoothing
to fine tune demand forecasts, probability theory to size safety stocks, and service
point theory to trigger joint replenishing orders. It was great pioneering back then,
and for most companies, it would still be considered leading edge if they were to
implement and use the IMPACT system today.

After IMPACT was developed, only minor improvements were seen for
some time. The next major development required the availability of computerized,
interactive data processing. This really did not arrive until the late 1970s. For the first
time, the buyer was presented with electronic access to inventory information

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making it possible to enter and maintain all the data needed for daily operation. This
information came from suppliers, marketing, customers, or elsewhere. Powered with
automation, the buyer could fine tune orders and complete them without waiting
for final edit reports and the like. The first such system that I am aware of is IBM’s
INVEN/34 which was announced in June of 1979. I actually know it quite well
because I was responsible for the design and the development of INVEN/34 while
I worked for IBM. INVEN/34 was later only partially modified for the IBM System/36
and renamed “DMAS Inventory Management.”

FOCUSING ON THE MAN-MACHINE RELATIONSHIP

At E3, our first development contribution to Scientific Inventory Management is
called E3TRIM®. It is an advanced and complete distribution center solution,
specifically designed for IBM’s iSeries Application System (AS/400), with
installations worldwide. E3TRIM conforms to your business model while factoring
the important functions of demand, seasonality and lead time into the inventory
order process. When it was created, E3TRIM was heavily influenced by the original
IMPACT standard.

I started E3 Corporation in 1980, and one of my first efforts was aimed at
making seasonal forecasting work. After the initial IMPACT development had been
fully established, it became evident that several areas within the total concept
required further study. For instance, slow-moving, irregular items (often called
“lumpy items”), did not lend themselves to very accurate forecasting models.
Seasonal forecasting seemed simple enough and quite straightforward, but
practically nobody used it.

Like many other researchers and developers, I had erroneously tried to
solve these and other open issues in my “Ivory Tower” rather than getting into the
real world. To correct this, I worked for a while on human factors studies rather than
statistics. I realized that features like how to present the information to the buyer were
as important as the quality of the information.

The result was an approach to seasonal forecasting that makes it natural
for buyers to contemplate seasonality, and it is easy to use.

GAINING RESPECT FOR THE “INVESTORS” IN PURCHASING

Scientific Inventory Management has been around for a long time, yet many
companies still are not benefiting from this development. It is not because the
theories are flawed. Rather, the theories are just fine, and even a rather simplistic

A BRIEF HISTORY AND OVERVIEW 5

implementation of the theories could vastly enhance the inventory service and
profitability of any chain or wholesale business. The problem seems to be that
the people who would benefit the most from the use of Scientific Inventory
Management–the owners and/or top management of the distribution
business–typically don’t get involved with inventory replenishment.

In fact, the buying area is one that has been given little attention by both
management and data processing. Buyers have long been known as the “Rodney
Dangerfields” of the business because they get so little respect. Buying is looked upon
as simply a necessary evil, done mainly to fill holes in the warehouse.

The real irony is that buyers are the investors for the company. No group
has more impact over the profitability of the products than buyers. Despite the fact
that inventory is normally the largest asset of any chain or wholesale business, the
buyers are given very few tools to help them make smart buying decisions. This is
largely due to the fact that top management of most companies is “entrepreneurial,”
having achieved success through sales. Therefore, when in trouble, they focus
attention on sales to cure their ills. I have long been one of the group who strongly
believes that selling generates revenue, while buying right and buying smart
generates profit!

BUYING AS A COMPETITIVE ADVANTAGE

Recent innovations give the buyer more potential for cash flow and profit impact than
ever before. The ability to collaborate on the Internet in a private marketplace
enables businesses to discretely communicate with their trading partners to
discover the best and most profitable time to replenish inventory from the store
level up to raw materials procurement. Trading partners are able to leverage their
unique insights and competencies to ensure inventory is positioned profitably to
the end consumer.

Using a sophisticated CPFR® (Collaborative Planning, Forecasting and
Replenishment) process, purchase orders are automatically driven from the analyzed
inventory, preventing cumbersome methods that previously slowed down and
created greater costs in the buying process. The end benefit is real-time
synchronization of information leading to efficient automation.

At E3, we believe that we were the first company in the world to facilitate
live trading pairs–retailers and suppliers–who actively use a collaborative
replenishment system to maintain and manage their inventory.

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MANAGEMENT’S CHALLENGE TO BUYERS:
“100% SERVICE WITH NO INVENTORY”

Chain and wholesale distributors are performing the service of providing goods
“locally” to a distribution level that cannot buy directly from manufacturers,
domestically or internationally. To be competitive in this business, you must be able
to fill a high percentage of your customers’ demands, often called service level.
Your service level must be as good as, or better than your competition, unless you
want to try to compete with price alone, a concept that justifies literature of its own.

You need to serve your customers well enough to get your fair share of the
market. More importantly, you wish to do so with a profit that generates a return on
investment that makes it all worthwhile.

How does the buyer attain the dual objectives of high service and low
inventory or high turns? In most cases, the practice can be defined as a term called
“Noise Level Minimization” which goes as follows:

MINIMIZING THE NOISE

First, the buyer makes purchase decisions based on gut feel. If and when a buyer
runs out of an item, he or she buys more next time to avoid the unfriendly voices
of customers and sales people. When the extra inventory reaches a noticeable level,
a voice from above, whose identity coincides with the signature on the paychecks,
mentions that four-letter word to the buyer: “TURN.” The message that top
management would like to turn the inventory more quickly is responded to, and
the whole cycle starts over again! After a few revolutions of this game, the buyer
eventually finds a general level of inventory that will minimize the noise from
both sides.

Anyone who runs a wholesale business knows that you cannot afford to
meet all customer demand. The 100-percent service level is not normally profitable.
But whenever the buyer runs out of stock, it is obvious who should take the blame.
It is amazing that anyone remains a buyer for more than a few weeks.

This caricature of the buyer’s life is a gross over-simplification. The buyer’s
day is filled with other curve balls such as short shipments, vendor minimums and
maximums, promotions, new items, dead items, angry sales managers, hysterical
customers, etc. Somehow, the actual concept of replenishment purchasing tends to
get mixed up with all of the other duties of the buyer. In the midst of putting out the
daily fires, buyers are also asked to find time to buy!

A BRIEF HISTORY AND OVERVIEW 7

THE DEFINITION OF A BUYER

So, let’s define the buyer’s world, in particular the part that may be assisted by
Scientific Inventory Management. When you are out of stock, it is the buyer’s fault.
When you are overstocked, it is the buyer’s fault. And, should anything happen to
go right somewhere, then it is thanks to the sales department! Buyers usually
recognize this picture.

We are capable of changing the image and making the buyer’s day a lot
more satisfying and efficient. Buyers have a large amount of information to analyze
in order to make intelligent purchasing decisions. The purchasing process is easier
to understand when we break it apart and define the steps that lead to a complete
order. For all buyers, the purchasing process consists of seven major decision-
making processes.

In one way or another, every buyer goes through all seven processes when
building purchase orders. Unfortunately, many buyers try to do all seven in one step.
The result is that they often make decisions by using irrelevant information. The
following is an outline of the Purchasing Process:

Ẅ Demand Forecasting
ẅ Lead-Time Forecasting
Ẇ Order Policy Cycle Analysis
ẇ Service Level Analysis
Ẉ Replenishment Analysis
ẉ Special Order Analysis
Ẋ Order Validity Analysis

Ẅ DEMAND FORECASTING

The cornerstone of any replenishment system, this area should be given the most
attention of all the seven processes. While most buyers today are buying with the
help of only two or three months’ worth of history, two to three years of demand
information may be the most valuable information any chain or wholesale business
can have!

ẅ LEAD-TIME FORECASTING

While every company puts an effort into forecasting demand, very few lend any
resources to the area of Lead-Time Forecasting. In fact, Lead-Time Forecasting is
almost as important as Demand Forecasting, as it is difficult for buyers to attain their
goals without helpful statistics concerning their suppliers.

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Ẇ ORDER POLICY CYCLE ANALYSIS

This process helps buyers figure out how often they should place an order with a
particular vendor. Because they have limited time, buyers must normally adhere to
weekly purchase cycles. They then hope that stock does not run out before they get
a chance to look at the vendor again. With the help of computers, steps can be
taken to not only minimize costs, but also to have orders triggered by service
problems rather than a scheduled day of the week.

ẇ SERVICE LEVEL ANALYSIS

Management and buyers typically set service level goals to be competitive. The
buyers then try to figure out how many weeks’ supply they will need to meet their
goals. But some items are more important than others. And customer demands
fluctuate to different degrees, and so do supplier lead times.

Ẉ REPLENISHMENT ANALYSIS

Today, the process of replenishment most commonly means that the buyer analyzes
the previous four processes and then arrives at a magical number. Instead, if those
four true “buying tasks” were done in their proper time and sequence, replenishment
buying would become a process of piecing the components together and comparing
against the stock status to arrive at an order quantity.

ẉ SPECIAL ORDER ANALYSIS

Over and above the net order required to maintain desired service levels, the buyer
may elect to take advantage of special deals, dating and price increases, or to stock
up for promotional activities. The profit potential in this area is enormous, and the
buyer must have been given the proper guidelines by management in order to
invest additional money wisely.

Ẋ ORDER VALIDITY ANALYSIS

Finally, now that an order is built and special deals considered, does the order meet
the vendor’s minimums and restrictions? If the order does not meet the restrictions,
it must be adjusted before it can be placed.

These seven steps are so important that each merits a closer look.

A BRIEF HISTORY AND OVERVIEW 9

DEMAND FORECASTING

It is no secret that Demand Forecasting is the most important aspect of any
replenishment process, whether it is manual or computer assisted. The benefit is that
we can program a method of forecasting demand, and the system will rapidly
perform the functions that would normally have to be done by hand. The computer
does not have to take the “shortcuts” buyers most often use for lack of time.

An additional advantage of a forecasting system is that a computer program
cannot “overreact” the way buyers tend to do when responding to changes. Unlike
a buyer, the computer does not face the day-to-day battleground of screaming sales
people, angry customers, and turn-minded managers. On the other hand, the
computer does not know enough about the products to anticipate demand trends,
promotions, and the like. The ideal situation therefore calls for a combination of a
strong forecasting replenishment system driven by an experienced buyer!

THE DEMAND SPIKE

Perhaps the toughest dilemma for the buyer is a sudden spike in demand of a
normally stable item. How the buyer responds can mean the difference between
being overstocked or facing a second consecutive stock-out. Unfortunately, the
buyer normally analyzes the situation shortly after taking a lot of heat for running
out of the product. Therefore, when reanalyzing the forecasting situation for that
item, it is not unusual for the buyer to disregard what could be years’ worth of
steady demand history in overreaction to a sudden demand “spike.”

Let us look at an item that for the last two years has been averaging about
100 units a month and doing so very smoothly (rarely selling less than 75 or more
than 125). Suddenly, for no known reason, demand for the item is 180! Will this
continue? On the other hand, will demand next month balance that out with a very
low demand? Buyers face these types of situations regularly.

If we don’t know why it happened, and if we could forget that we caught
hell for being out of stock, we should realize that those two years of stable demand
should weigh very strongly in determining the new forecast. In fact, in a study done
by IBM in the 1960s, such spikes were due to errors in more than half the cases.
Reasons for the errors included data entry, the customer ordering the wrong product
or quantity, and, of course, pack size mix-ups.

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In this common occurrence, it can be very costly for buyers to confuse a
random variation with a true change in demand level. Moreover, such spikes are not
limited to the high side. Spikes on the low side can be even more difficult because
the reasons for them are harder to research. Regardless, the same rules apply. If we
don’t know why a spike occurred, and if we have a good amount of solid history on
the item, then the forecast is about 100 units a month!

This, however, does not mean buyers should ignore changes in the
demand. It means that occasional variations from “normal” demand patterns
should not be confused with true changes in demand level for an item.

PERIOD-TO-DATE INFORMATION

If one occasional month of big or small sales should not influence our perception
of the market for an individual item, what about month-to-date statistics? Most
buyers ask to see sales or demand for the last few months and current month-to-date
information. Although there are times when month-to-date information can be
helpful, more often it is misleading.

For instance, let’s look at that same item that tends to sell smoothly at about
100 units per month, then at the additional ability to check month-to-date sales.

During the first week of a particular month, we sell 60. It isn’t too difficult
for us to translate that into 240 for the month. We envision outages, angry sales
people and trouble overall. So we had better buy on the heavy side!

Of course, if we could know that the next week we would sell only 20, then
the four-week forecast would drop from 240 to 160. That is still more than we
expected, but not quite as dramatic as the 240. Had we known that during the third
week, 45 of the initial 60 units will come back (wrong item number) and that the
whole month will finish at 90 units sold, then we should not have bought any extra
just because the item seemed to move well at the beginning of the month.

THE DANGERS OF OVER-FORECASTING

It is dangerous to try to relate a week’s worth to an entire month. To do so makes the
history very erratic. In fact, most buyers are able to closely project sales for the next
year for many items, but when projecting a week’s or even a day’s worth, their error
rate becomes very high.

A BRIEF HISTORY AND OVERVIEW 11

A sporadic high demand means that we must buy more than usual to get
the inventory level back to normal, but we should not anticipate that the sales will
continue at a higher level due to an exceptional week.

More importantly, buyers should not buy in self-defense. They should not
buy larger quantities simply to keep from running out on the assumption that high
first-week sales portend an increase for the entire month. Nine out of 10 spikes in
demand are flukes. The buyer must play the odds and at times take the “blame” for
missing some true improved demands.

“HOW MUCH HISTORY” TO USE WHEN FORECASTING?

How much history should you take into account when you forecast demand for the
near future? If an item has been selling between 80 and 120 consistently for two full
years, then it makes no difference whether you use three months, 12 months or 24
months to forecast. The average, or forecast, will still be close to 100 units.

But consider another item with similar average sales. Its monthly sales have
ranged more broadly between 40 and 160 for the past two years. Regardless of
whether it sold 50 or 150 units last month, the best guess is still that it will sell around
100, on average, for some time to come. On items with fluctuating demand, it is
important to use more history to get an accurate forecast. To judge from demand
of only the past few weeks will lead the buyer to believe the item is always changing
course. The buyer has no choice but to follow these up-and-down patterns. But by
viewing the past two years, we see that the item does have an average of 100. And,
because of the large monthly fluctuation in demand, we need quite a bit of buffer
stock to cover the random variations.

If, on the other hand, an item has been selling around 100 units a month
for years and goes 90, 70, 80, 40, 50, you should probably disqualify the old history.
It no longer represents the demand level of the item. A forecast of 40 to 60 is probably
the best bet for the near future.

Although we like to think we can let the computer handle all our items with
one simple forecasting calculation, it is not that simple. Within any inventory, there
are many items with many varying demand patterns. There are items that are
keeping a steady course, items trending upward and downward, and even those
trending slowly and very quickly. To forecast all items correctly, it is necessary to
analyze the item and then forecast its demand in an appropriate manner.

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DEMAND HISTORY VS. SALES HISTORY

Although I have used the words “sales” and “demand” rather interchangeably up to
this point, I want to make the distinction that, whenever possible, buyers should use
demand to forecast, not sales or shipping quantities. The purpose of forecasting is
to meet future “demand,” not simply to ship the same amount your warehouse was
able to supply in previous months, regardless of previous service levels. Shipping
quantities and sales numbers may not reflect true demand, and using them can
cause problems with inadequate service levels.

Obviously, outages tend to decrease the average and in turn the inventory
level, regardless of demand. Although demand figures may be inflated at times,
they do give the buyer a better picture of the future. Because you are in business to
service your customers, it is much healthier to err in favor of service than stock-outs!

MAKING THE MOST OF THE BUYER/COMPUTER TEAM

As soon as you have sufficient demand history by item, you can use computers to
calculate averages as well as a maximum and minimum “warning range” for each
item. The computer assists by calculating new forecasts for all items every night. In
addition, the computer can warn the buyer of items that surpass certain warning
ranges. Since the buyer does not have time to analyze every item every day, this
pinpoints items the buyer will most likely want to review. The buyer may try to find
out why an item sold outside its normal range, and, if needed, can make manual
adjustments to the forecast.

Preferably, the buyer should have up to three years’ worth of history, the
forecast for the item and the actual demand for the last month. Very often, the
buyer will know of an upcoming change to the demand of an item before the
demand happens and the computer ever sees it. Therefore, the buyer must have the
ability to change the forecast for an item, which disqualifies all previous history from
the computer’s point of view. The true responsibility for the item must reside with
the buyer. This may be a subtle point for many, but a key point for success!

SEASONAL FORECASTING: MAKING IT WORK

Not all items sell evenly throughout the year. Many items follow identifiable demand
patterns that vary with the seasons. If an item sells normally throughout most of the
year, but sells two and a half times the normal rate each June and July, then we can
take steps to forecast the seasonal pattern. With the proper history, we can pinpoint
the forecast, then factor in the seasonal pattern. We can tell the computer to expect

A BRIEF HISTORY AND OVERVIEW 13

demand two and a half times greater in June and July. Therefore, whether we are
averaging 100 per month, or grow to 500 per month, we still know what to expect in
the two peak months.

Regardless of the complexity of peaks and valleys in an item’s demand
pattern, seasonal changes can be recognized and forecast for the future. A little work
on the front end eliminates the need for buyers to “baby” these items as the season
begins and ends. It is also important that the system brings inventory in a bit early,
and that it keeps the stock on the lean side toward the end of the season.

LEAD-TIME FORECASTING:
THE NEGLECTED FACTOR

The ability to forecast lead times is almost as important as having good demand
forecasts. Surprisingly, though, few companies have taken steps to help buyers
forecast lead times from lead-time history.

Those that have tried to do Lead-Time Forecasting have tended to forecast
simply at the vendor level, thus disregarding items that tend to be back ordered or
scratched more often. It is very difficult for buyers to maintain a desired service level
without knowing the true lead time for each item.

Like Demand Forecasting, Lead-Time Forecasting causes the buyer his
share of headaches. For example, the last few deliveries from vendor X have taken
14, 25, 15, 13, 16 and 15 days. Which lead time is the buyer most likely to remember?
Answer: the 25-day lead time, when the buyer ran out of stock, triggering a chain of
unfriendly phone calls from the sales force through top management. Any normal
buyer will remember this occurrence for some time and will allow it to affect
future ordering from that supplier. The buyer will do anything to avoid repeating
that nightmare!

THE LEAD TIME SPIKE–OR THE VENDOR GOOF?

Spikes in lead times are just as important, or unimportant, as demand spikes. We
must be able to recognize the difference between a random “mishap” by the supplier
and a true change in the lead time of a product.

We should use the same resources that assist us so well in Demand
Forecasting. As with analyzing demand, it is important to react when lead times

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are truly changing, yet not overreact when an item’s lead time is far from normal.
They signal when the buyer should contact the supplier for their assessment of
the near future.

LOTS OF STOCK

VENDOR IS LATE....

OUT OF
STOCK!

LEAD TIME LEAD
TIME
VARIANCE

Many will say it is impossible to predict which items will be back ordered,
that back ordering is totally random. So what can be done? Industry studies have
proven that some items are more prone to being “short shipped” than others. The
only accurate way to track these is to have an Inventory Management System that
does so for you.

The top items, or A items, are typically delivered as ordered. They are the
backbone of the business, so the manufacturer or vendor will do his utmost to keep
these items in stock. Because very slow moving items (or C items) are manufactured
less frequently, the vendor tends to sit on a heavy supply most of the time. The
items vendors will have most problems with are the ones in the middle, the B items.
Unfortunately, those are the same items the wholesaler tends to stock on the lean
side as well. The tendency is strong enough to make it necessary to measure lead
times by item, not just by vendor.

A BRIEF HISTORY AND OVERVIEW 15

ORDER POLICY ANALYSIS
BALANCING THE COSTS

Order Policy Analysis is one of the most profit-sensitive functions in the purchasing
process. It answers the buyer’s question “How often should I buy from a vendor?”
To determine the best answer, the buyer should balance the cost of carrying
inventory and the cost of placing an order. This will help determine the most
economical “policy” or cycle for replenishing each supplier line.

To analyze one example, we have chosen a vendor line consisting of 60
items with total annual sales of $60,000. The cost of carrying inventory is 40 percent
a year, and the cost of placing each order is $20, with an additional 50 cents for each
line on the order.

Orders Per Year 1 3 6 13
Amount Per Order $ 60,000 $ 20,000 $ 10,000 $ 4,615
Header Cost
Line Cost 20 60 120 260
Cycle Stock Cost 30 88 180 376
Safety Stock Cost 12,000 4,000 2,000 923
Gross Bill 812 980 1,230 1,990
Annual Net Cost 60,000 60,000 60,000 60,000
72,862 65,128 63,530 63,549

5 The header cost and the line cost will go up as you increase

the number of orders per year.
5 The cycle stock goes down, since the average order goes

down in size.
5 The gross bill, which represents the total amount we expect

to spend with the vendor, remains constant since the vendor

offers no discounts.

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In this example, we will analyze four different ordering cycles or ordering
policies, in an effort to find one that minimizes annual net cost.Annual net cost, or
the sum of all costs, reaches a minimum for six orders per year. In addition, the
cost difference between six and 13 orders per year is so small that either could
be chosen, and the decision may depend on which works most effectively within
your environment.

Some vendors offer special advantages if you order above certain
requirements. These discount opportunities should be analyzed within this function.
It is critical that you not simply accept any discounts, but rather determine if the
discount advantage offsets the possible extra carrying costs incurred by stepping up
to a higher minimum.

Order Policy Analysis is a very important profit boosting function, but it is
nearly impossible to do by hand. Fortunately, computer programs have been devised
to assist you.

SERVICE LEVEL ANALYSIS
MANAGEMENT’S STEERING WHEEL

We have determined that forecasts for demand and lead times are simply averages.
Thus we expect that half the time demand will be greater or less than normal. We
also expect suppliers to occasionally ship late. Safety stock provides “insurance”
against both these occurrences. In choosing a service level or fill rate, we determine
how much of the safety stock range we wish to cover. As the service objective
increases, distributors must keep more safety stock, and the total inventory amount
will increase as well.

Although most companies choose an overall service rate to maintain, they
tend to break it down into a “mix” of service levels. Buyers normally strive for a
higher fill rate on key items. In fact, the most common way to identify these items
and maintain a mix of service objectives is through A-B-C analysis. The A items,
or fast movers, need to be given preferential treatment, while the B and C items
could probably afford to be lower. Although this method is an excellent starting
point, we can go a step further and also make service point decisions based on
item profitability.

A BRIEF HISTORY AND OVERVIEW 17

SAFETY STOCK NEEDED
TO MEET DESIRED
SERVICE LEVEL

80% 90% 95% 97.5% 99%

SERVICE LEVEL

TAKING SERVICE LEVELS A STEP FURTHER

Some fast movers may be low-margin, erratic items that require too much inventory
to meet high service levels. The extra inventory may reduce the profit opportunity
for these items. On the other hand, some relatively slow items may be very stable with
excellent margins. Because of the profit made when we sell these items, it may pay
to maintain a higher service level objective for them.

With the correct information concerning the items–including demand
history, margins, and the cost of losing a sale–we can ensure that service levels are
not only competitive, but are also working in the best interests of the company’s
bottom line! The result will be a viable inventory, with each item at its most cost-
effective inventory level.

18 SCIENTIFIC INVENTORY MANAGEMENT

It should also be noted that for the sake of maintaining a good reputation
for the company, some items may need to be placed at a service level so high as to
be unprofitable. That is, if we look foolish when we are out of stock of a “staple” item,
then we may need to maintain close to a 100 percent fill rate, regardless of what the
optimum level would be if we have to consider profit only.

REPLENISHMENT ANALYSIS

To most buyers, the process of replenishments involves juggling all kinds of
information. On the specific day or week, they sit with a stock status report and sort
through details concerning demand history, lead-time estimates, and a rough
measure of buffer stock in order to piece together an intelligent order. With thousands
of items and limited hours in the day, it is normally all that can be done to keep the
goods flowing.

The importance of breaking the buying process into seven purchasing
decisions is that the buyer gives proper attention to the first four steps and also
completes them before buying. It is important to note that these four factors not only
determine how much to buy, but they also affect WHEN to buy! Consequently, the
first four steps constitute the basic tasks of true buying. The fifth step, replenishment,
is a culmination of the first four tasks. In the traditional method of buying, if you have
been purchasing based on a lead time of 14 days, and during replenishment you
realize that the lead time has changed to 21 days, then you probably should have
bought seven days ago.

With the “real” work done ahead of time, the computer can show its
strength by sorting through a heavy load of numbers. The system can analyze
demand, lead-time percentages, order cycles and safety stocks, then suggest that the
buyer purchase at a calculated level. This level will then be compared to the end-
of-day stock status information to create the Suggested Order Quantities (SOQ).

It is a little more complicated to decide whether to order a vendor line on
a particular day or to wait one or more days. One item will always reach its order point
before the rest. However, if that item is allowed to “trigger” a stock order for the entire
vendor line, then the vendor line will often be overstocked because orders will be
placed too often. Order Policy Analysis tells us the “best” ordering cycle, but that does
not mean we must stick to a strict schedule.

A BRIEF HISTORY AND OVERVIEW 19

It is time to buy when delaying the order by one more day would prevent
us from maintaining our average service level objective. When we do buy, it is
important to order the same amount of days supply for every item.

ORDER-UP-TO-LEVEL ITEM ORDER POINT
(LOW STOCK)

ORDER CYCLE LEAD SAFETY
TIME STOCK

By doing so, the vendor line is balanced in terms of days stock, and the
items have the best chance of reaching their order points together the next time. This
minimizes the need for placing fill orders or buying the vendor line prematurely.

In summary, each item requires an Order Point that covers the forecast for
a minimum of time to cover the sum of the lead time, review time and safety stock
time. Safety stock, of course, expresses how many days extra stock you need to
meet your service level goal.

The Order-Up-To-Level is derived from the forecast for a maximum of days
supply you may have on hand and on order. The logic is now quite simple: as soon
as an item has passed its Order Point, you should buy up to the maximum or Order-
Up-To-Level.

One additional point should be made concerning joint ordering. I
mentioned that very often one or a few items would reach their item reorder points
far ahead of the rest of the line. Although we do not want these items to automatically

20 SCIENTIFIC INVENTORY MANAGEMENT

trigger the entire order early, it is important to show these items to the buyer during
the time of purchasing. If any of these items are our so-called “foolishness” items
(i.e. the items that make you look foolish to be out of), the buyer may choose to take
action and place the order early. Once again, the buyer must have the final say!

SPECIAL ORDER ANALYSIS

Now we have taken the proper steps to handle the replenishment of the day-to-day
service inventory, it is time to consider the buying of any possible “investment”
inventory. In other words, to what extent will we be taking advantage of discounts,
price increases, additional dating and any other special buying that may enhance
the normal profit of an item? This area is so complex, and the additional profit
potential so enormous, that I address investment buying in a separate paper entitled
“Forward Buying.”

It should be noted here that special opportunities such as deals, price
increases and dating may influence the size and timing of an order. Such adjustments
should be made before the buyer takes the last step in the purchasing process:
Order Validity Analysis.

ORDER VALIDITY ANALYSIS

By using the proper resources to handle the decisions the buyer faces, orders can
be generated in such a way that they not only satisfy customer needs, but also do so
in a way most economical to the distributor. However, if the order does not meet the
size, weight or dollar restrictions of the supplier, then we really have no order at all.
In fact, one of the buyer’s most time-consuming tasks consists of adjusting order
quantities until they meet the vendor’s minimum and maximum requirements.

Like many of the steps in the purchasing cycle, logic or common sense can
often get us into more trouble than we ever realize. For example, suppose one
vendor requires a minimum order of 200 cases. Replenishment analysis shows we
need to order 180 cases to fulfill service needs. But because we are 20 cases shy of a
valid order, we must bump the quantities of some items to reach the minimum.
Which items shall we pick? Common sense and 20 other reasons tell us that the top

A BRIEF HISTORY AND OVERVIEW 21

items in the line (the A items) will not only get us there quickly, but they are a safe
choice as well.

As the next buying cycle rolls around, we analyze again and conclude that
we need only 160 cases, largely because we bought 20 extra last time. But, we must
buy because many of the mid-range items are running low. This time we dig a little
deeper within the top items to reach our minimum.

As this cycle recurs once or twice, we become so heavily stocked in the A
items that we must skip an order. This sends the service level spinning downward
for our lower-range items.

There is a better way! When the order must be padded, it is important to
add the same amount of time supply for every item within the vendor line. This
will not only keep the line balanced, but we will be doing everything we can to
ensure that all items have the best chance of reaching their next order points at
the same time.

SUMMARY

Inventory is normally the largest asset of any retail or wholesale business. Despite
this, the purchasing process and buyers have been given little attention from
everyone from the owners through the IT department. Yet, there are proven, sound
methods of buying that, when implemented, go further than any other possible
process to maximize profits while maintaining inventory at the most sensible level.

Effectiveness varies dramatically among systems available today. One
philosophy is hard to refute. It is the idea of breaking the replenishing task down
into separate, sequential “decision-making” steps. This is the foundation of
smarter buying.

New innovations such as Collaborative Planning, Forecasting and
Replenishment will allow companies to work hand-in-hand with their suppliers via
the Internet to optimize inventory, ordering at the best possible time to minimize
costs throughout the supply chain and eliminate lead time problems.

Of course, a certain amount of art or “human touch” will always be needed
in buying. After all, no machine will ever totally replace the creativity and drive of a
dedicated employee. But a large part of the buying task can be enormously assisted
by science.

22 SCIENTIFIC INVENTORY MANAGEMENT

Buyers work their best in an environment where they have the best possible
tools at their discretion to make sound purchasing and replenishment decisions.
Loosing the bonds of antiquated methods, procedures and solutions will only open
the doors for maximum purchasing power–the key to enhancing your inventory, and
in turn, your bottom line.

A BRIEF HISTORY AND OVERVIEW 23

NOTES

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