A good Inventory Management System can dramatically improve the profitability of the business by streamlining production.  This applies to not only a manufacturing business but any business that has a workflow process.

Table of Contents

Min-Max Inventory Control
TOC Inventory Model
Managing Uncertainty
Simulation Case Study
Case Study Results
Real World Examples
Wrap Up

Inventory is the process of managing the inputs to a workflow and holding the outputs of a workflow prior to sale or delivery.

It is an important aspect of any business that has a workflow process built into it.  That is not just manufacturing; it can also include retail and professional services.

Most businesses that have inventory will also have some form of Inventory Control System whereby they trigger a replenishment of inventory items.

If this process is not well tuned, you will frequently run out of important Tools for your workflow (called stock-outs) or alternatively, and perhaps simultaneously, have over-supply of others.  This can be a very expensive problem for a business to have.  It ties up money in surplus supply and surplus stock and loses money, and possibly clients, when you are out of stock of essential Tools. 
Go to the article: Cost of Over and Under Stocking

Over time, most people have probably settled on a so-called Min-Max system of inventory control which we will describe next.  We then offer suggestions on better inventory controls to replace this one.

The types of items in an inventory are very often referred to as SKUs which is short for “Stock Keeping Units”.  We will use that term here.

Min-Max Inventory Control

Over many years, a Minimum-Maximum System of Inventory Control has been installed in many companies.  This works on 3 basic rules in the normal implementation of the system.


Over time, and often by a process of trial and error, a decision will be made for each SKU about at which trigger point it should be restocked.  This is the Minimum part of the Min-Max system.

The Minimum is often determined by the time it takes to re-supply the item.  This is known as the “lead time”.

For example, we consume an SKU at the rate of 10 a day
Therefore, we always want at least 10 in stock for each day it takes to resupply the SKU so we don’t run out. 
If it is normally supplied in 10 days, we would hold a minimum of (10 used per day * 10 days to replenish) = 100 items.

But, resupply will no doubt vary from the average of 10 days so, rather than run out, we will pad the order a bit to leave more stock in case of replenishment delay.  That means we will either order before the MIN is reached or raise the level of the MIN point to compensate for the risk that lead time will be longer than expected.

This might also be further ‘padded” by CYAA (Cover your ass always) so you don’t get into trouble for running out of stock.

Alternatively, depending on how good your measurement systems are, you may well have fallen below this Minimum point by the time your measuring system reports that you have reached the MIN.  When you do get around to re-ordering there is a significant likelihood that you will be out of stock for some time before the ordered parts arrive.

Under-stocking in this way can have considerable cost. 
Go to the article: Cost of Over and Under Stocking

The reality is that the MIN is likely never really the MIN but something with a fair amount of safety built in.


The Maximum, on the other hand, is an arbitrary figure which sets the Maximum you hold at any one time.  Again, this is probably based on a gut feeling.  It is usually quite conservative as people normally do not like to run out of things so they may well overstock.  This has the effect of increasing the amount of money tied up in inventory.
The difference between the current holding and the Maximum holding is the amount that is re-ordered.

Dis-aggregated Stock

Further, many implementations of Min-Max also require the stock to be held close to the point of consumption.  If you are in a workflow where a single part is consumed by several different production systems, there will be a mini stock pile of this part at each of those production locations.

Depending on the speed of consumption at each of those locations, parts in inventory may be exhausted at one location before another.  Some locations are well off for stock while others starve.

This gives conflicting information to the Min-Max system which possibly shows there is enough stock on hand to not trigger the Minimum re-order point but it does not necessarily indicate where that stock is.

Also, stock from a production line with a surplus part of that inventory does not get moved to a production line with a shortage of a product because the lines are worried that if they give their stock away they will shortly be out of stock themselves.

Setting the Wrong Metrics

Quite often, in larger production systems, the procurement department’s performance is measured (metrics).  This may cause them to act in ways that are not in the best interest of smooth production through the system.

  1. They may, for example, be incentivised to keep the inventory at as low a level as possible to save the capital investment.  To achieve this, they delay re-stocking. Consequently the production lines will, through normal production variability, often be out of stock of a part required.  Therefore, the entire production system comes to a halt.  This is a false economySaving money on procurement stops production and therefore loses money.  But, from the procurement department’s point of view, it is someone else’s problem.
  2. The procurement staff may also be incentivised to buy in bulk and thereby get quantity discounts.  While this is a good thing, it becomes less of a good thing if it starves the production system for supply while waiting for a big enough order to build up.  Buyers might also buy bigger amounts so the quality discounts make them look effective.  However, this might fail to take into account the cost of the cash tied up in inventory.  The interest on this cash might exceed the quality discount so the decision costs the company money.
  3. The procurement staff may be incentivised to buy at minimum price.  To achieve this, they may frequently shop around and change suppliers to get the better price.  An adverse consequence of this is that the suppliers are far less familiar with the demand cycle for the parts they supply and they themselves may not be able to supply in a timely manner.  It is also true to say that a supplier is going to favour their larger, steadier clients rather than a client that comes and goes.  If they are going to have difficulty servicing demand for a part from their clients, they are going to give first preference to their “best clients”.
  4. Alternatively, a common issue with inventory under MIN MAX is that fairly arbitrary Maximum quantities are allocated to products.  Often someone orders cutbacks on parts for the manufacturing process to slow down cashflow out to suppliers or to slow down the often-ballooning cost of inventory by keeping the MAX component fairly low. This is an example of a common policy constraint in a tight manufacturing environment.  This has the effect of starving the manufacturing process and thereby reducing throughput but it does look as though management is tackling the inventory/cashflow problem.  This policy is masking the resultant slowdown in production and sales and therefore sales revenue so is probably quite silly. 

Go to the Campaign introduction: Theory of Constraints (TOC) for more on policy constraints.

TOC Inventory Model

The Theory of Constraints (TOC) Inventory model is rather different to the traditional Min-Max. 

Firstly, instead of working on a Supply-Push system, which the traditional Min-Max systems use, it works on a Demand-Pull system.

As a part is used, it is replenished on quite a frequent basis.  It is quite conceivable that you will be placing smaller size orders on a much more frequent basis – even as often as daily.

What this means is that the inventory on hand, at any point in time, is tracking the demand.  If you have a product that is in heavy demand, the parts necessary to supply that product will automatically be ordered more frequently to keep the “pipe lines” open.

Conversely, when you have a product that is slowing down in demand the supply of parts to service that product will also slow down.

TOC Stockpiles

In Min-Max, the inventory is often distributed out to the various production lines.

These production lines could be lines within a single company or could be production lines in several different branches in different geographical locations.

An example of geographic distribution is a network of shops selling the same product range under the same company banner.

Because the consumption of parts/products will almost certainly differ between production lines/locations, any local stock pile of the product will either be rapidly consumed and experience periods of outages, when income and possibly customers will be lost, while other parts of the system will be experiencing over supply.  This is the problem with ‘dis-aggregation’ discussed above.

The internal inventory system under TOC works the same way as the external procurement system does.

In TOC as much stock as possible is held as far upstream, close to the point of the supplier, as possible.  Then, as the demand for the product draws down from that stock pile, it is replenished from the supplier.

This has the effect of leaving a suitable buffer stock of a product at the highest level in the company, and closest to the supplier, which any of the product lines can draw down on.  Therefore, there is minimum surplus stock with minimum shortages at the actual production lines or selling points themselves.  Also, the stock on hand at any point in time is primarily at this higher order point and therefore can be much better measured and supervised.

Because there is minimum stock held at the production and selling points, the total quantity of inventory is likely to drop.

Stock is ordered from the suppliers on a frequent basis and according to the amount that has been taken out of the principal warehouse in that period.

There needs to be sufficient “buffer stock” of the inventory to allow for the normal, and perhaps uncertain, lead time for the supplier to supply the replacement products.

Nevertheless, this would also have been allowed for in Min-Max but in TOC we are ordering only the minimum amount necessary to replace the demand on that item. We almost certainly will have less product and stock than under Min-Max.

Issues to Address

Under this TOC Demand-Pull system, you might be restocking as frequently as daily to get the best tracking possible.

However, opponents might argue this is not practical/possible.  Worst comes to worse, you might have to bulk up your orders a little.  You can probably measure the impact of this in the Simulator Tool mentioned below.

The common resistances are:

The supplier won’t supply small quantities

Your suppliers have an overhead service cost to provide you with any order.  This might include time to pick the order, invoicing costs and shipping.

They may resist providing you with small quantities because of this.

If your supplier is adamant, you could look around for a more flexible supplier.

Costs like invoicing might be reduced if your orders were invoiced periodically (say monthly) rather than each time they supply.

Chances are, your supplier also has an inventory resupply problem; either from their upstream supplier or their own manufacturing process.  Surprisingly, if you make the case to them about how the TOC Inventory Ordering system works, it is likely to also work for them and they might see the benefits to having more of their customer’s order in smaller, more frequent amounts.

Transport Costs

The Transport costs are significant without doubt.

You could look at alternatives and change shipping methods.  You might find a supplier with a truck that makes rounds to all its customers and use that rather than third party shipping.

Quantity Discounts

You might find that your best price is when you order in a batch size sufficient to get a quantity discount.

It might even be that your present system has a Policy Constraint forcing you to buy a minimum quantity for this very reason. 
Go to the Campaign introduction: Theory of Constraints (TOC) for more on Policy Constraints and the damage they can do.

It might be that you can agree with the supplier that you will take a quantity over a period that is large enough to give you the quantity discount but agree to take it in small parts.

Managing Uncertainty

It may be the case that you will be unable to accurately forecast the demand for parts of the production line or demand from customers for your products.

There is a whole host of variables that come into play to generate this uncertainty including:

Because of this uncertainty, an inventory control system like Min-Max, that relies on pushing parts/products into production or the point of sale will almost certainly be, at best, responding to historical events.

At worse, it is trying to predict what the demand will be.  While this might be necessary for products that take some considerable time to assemble, it is almost certainly going to lead to large stock piles of products that are no longer attractive to the market place.  This inventory will either remain on the books consuming cash or need to be disposed of at cost or as scrap.

Demand-Pull Systems

On the other hand, a Demand-Pull System will have minimum quantities of a product on hand for input to the manufacturing cycle or for sale to customers and then respond as quickly as possible to the demand for more of those parts/products.

Such a system only has the minimum amount of inventory at any point in time and just sufficient to satisfy the immediate demand of the production line/sales point.

This means that when uncertainty creeps in by way of changing demand for products – either greater or lesser – the system can respond as quickly as possible.

TOC is a Demand-Pull system whereas Min-Max is a Supply-Push system and is probably inferior to the TOC approach.

Simulation Case Study

You can test for yourself the difference between a Min-Max and a TOC inventory system.

We will set up a hypothetical demand for a part and then use a dice to model demand in a simulation of the two reorder systems.

The results of the simulation method discussed below are available for download as an Excel spreadsheet for Registered users.  
Email us for a copy of the download and PDF version: reception@12faces.business

Rather than build you own spreadsheet, use ours as a template.

Min-Max Simulation

Example of Min-Max Inventory Control: 

Assume that we have a part that has a maximum level of 30 items and a minimum level, which is the re-order point, of 15 items.

The lead time to replenish this part averages 4 weeks and that average is based on a range from 3 to 5 weeks to re-supply that part to us.  This will be dependent on other demands from our supplier at the time.

Usage of these parts varies by week but on average is equal to about 3 items per week.

In a spreadsheet, create a 26-week (26 lines) table.

Assume that these items cost $1,000 each to keep in stock.

Using a dice, the consumption of items, on a weekly basis, is calculated from each throw which will range between 1 and 6.  This is possibly a more extreme variation than we might have for the product but it will serve the purpose. Enter this number to Column B.

In Column C start with 30 items, which is about 10 weeks’ supply, at week 1.  With each dice throw subtract the number of items consumed from the stock balance in Column C.

As you consume product on each dice throw, at some point, you will reach or fall below the re-order point of 15 units.  At this point you should place an order to bring the inventory back to the maximum level of 30 items.  Add this figure to Column D.

The order will become available somewhere between 3 and 5 weeks from the time you order.  Toss the dice until you get a figure between 3 and 5, enter this figure to Column EAdd the purchase inventory into stock on hand at that number of weeks from order.

In the meantime, you are continuing to consume the stock.  At some stage you may come to a point where you have zero items in stock, you can no longer sell/produce that item.  Stop tossing the consumption dice until your re-stock order comes into play.

Continue this for the 26-week period and see how many outages you have.

Column F, in your spreadsheet, multiplies the stock on hand by the price per unit of stock ($1,000).  At the bottom of this column, after your 26 weeks, generate an average value of stock you have on hand for the period

TOC Approach

Now let us play the same process again with additional columns for TOC

Under TOC Inventory control items will be re-ordered each week, as per consumption for that week, for delivery within 3-5 weeks.

Because of the weekly re-ordering we can now assume that our part has a maximum level of 20 items, 10 less than under the Min-Max system.

Column A – Items consumed (allocated via dice throw)
Column B – Stock on Hand
Column C – Re-order Number
Column D – Number of weeks to delivery of order
Column E – Stock on Hand Value

Assume that these items cost $1,000 each to keep in stock.

Copy the results of the previous dice throws to estimate the consumption so that both Min-Max and TOC system simulations are using the same consumption patterns.  Enter these figures to Column A.

Commence with a stock on hand of 20 items in Column B.  With each dice throw subtract the number of items consumed from the stock balance in Column B.

Items will be re-ordered weekly, as per the consumption of stock in Column A, for delivery in the 3-5-week delivery period.  Enter this re-order figure into Column CToss the dice to get the number of weeks for delivery, as was done for the Min-Max system, because there will be many more re-ordering points here.  Enter the delivery weeks into Column DAdd the purchase inventory into stock on hand at that number of weeks from order.

Once again, you can check the TOC system for outages.  Almost certainly, you will have fewer, if any, outages of supply.

Again, Column E multiplies the stock on hand by the price per unit of stock ($1,000).  At the bottom of this column, after your 26 weeks, generate an average value of stock you have on hand for the period under the TOC system.

We cannot predict the results that you will get but we have included a table of typical results that you can look at below.

Case Study Results

The results of the simulation method discussed below are available for download as an Excel spreadsheet for Registered users.  
Email us for a copy of the download and PDF version: reception@12faces.business

In the example simulation, the dice throw Column B (MM), Column A (TOC) is the random demand.

Columns C (MM) and B (TOC) show how much is on hand each weekNote that we start the TOC column at a lower amount (20) because the strategy means less inventory will need to be held.

Columns F (MM) and E (TOC) show the dollar value of stock on hand.

Under Min-Max the stock is triggered to re-order once the level on hand is at 15 or belowTOC stock is re-ordered on a weekly basis as per consumption.

The case study shows that the average amount of stock on hand over the ½ year period, at $1000 per unit, shows that the TOC method has 24% less stock on hand.

Finally, we can see the number of times the two systems ran entirely out of stock.

The MM method had stock outages 3 times:

The TOC method was able to support all consumption, nil outages.

  Inventory Case Study      
 WeekConsumed         (dice throw)MM Stock on handMM reorderWeeks to deliveryMM Stock Value Consumed         (dice throw)TOC Stock on handTOC reorderWeeks to deliveryTOC Stock Value
 1130  $30,000 12014$20,000
 2629  $29,000 6196 $19,000
 3323  $23,000 3133 $13,000
 4220  $20,000 2102 $10,000
 5418  $18,000 494 1st arrives$9,000
 6314165$14,000 3113 $11,000
 7511  $11,000 5115 $11,000
 826  $6,000 282 $8,000
 944  $4,000 4104 $10,000
 1030  $0 393 $9,000
 11216  $16,000 2112 $11,000
 12314165$14,000 3113 $11,000
 13111  $11,000 1121 $12,000
 14410  $10,000 4144 $14,000
 1556  $6,000 5125 $12,000
 1611  $1,000 6101 $10,000
 17416  $16,000 454 $5,000
 18312184$12,000 353 $5,000
 1929  $9,000 272 $7,000
 2037  $7,000 363 $6,000
 2144  $4,000 474 $7,000
 22318  $18,000 363 $6,000
 23615154$15,000 656 $5,000
 2449  $9,000 424 $2,000
 2535  $5,000 323 $2,000
 2632  $2,000 323 $2,000
 Averages31216 $310,000 393 $237,000
 Av. $ value    $11,923     $9,115
 Saving       24%   
 Outages 3     0   


Real World Examples

You are now familiar with the Min-Max versus TOC inventory models.

Imagine a vending machine sitting beside a small convenience store.  The vending machine has multiple products (like drinks and snacks) that people purchase.

The convenience store sells much the same product line from a small kiosk type arrangement.

The kiosk works on a Min-Max system.  They keep an eye on the product and as it starts to run down, they order more – usually a carton or more at a time.  Consequently, they need a warehouse facility to store the product that has been ordered but not yet placed out on display for sale.

They run the risk that the life of some of the product will expire before it is sold and it must be trashed.  Also, they run the risk that they will run out of fast moving product lines because of the need to buy one or more cartons at a time.

They probably only place an order for product (say) once a week.  Unfortunately, the kiosk operator cannot necessarily arrange their product by expiry date which leads to some of their products passing the expiry date and others having a very short term life.  This leads to further waste.

On the other hand, the vending machine, which sells the same products, is serviced daily and only sufficient stock is put into the vending machine to replace stock that has been consumed.

Automatically, if the consumption of a product line slows down, less of the item will be replenished on the shelves of the vending machine.  Conversely, if demand picks up for some reason (cold drinks on a hot day), the daily servicing of the machine will match that demand.  When a product is particularly fast moving, more than one channel in the vending machine could be dedicated to that product.

There will be very little life expired product, except for very perishable items like sandwiches, under this model because the oldest is consumed automatically by being presented first to the customer.

Which do you think is the more efficient inventory system?

Wrap Up

By now the different characteristics of the 2 systems are probably quite clear.  Hopefully, the case study with the dice has demonstrated, at least in a particular set of circumstances, that the TOC system is better.

The first thing that you will notice with TOC is the dramatic reduction of the total inventory required to maintain a functioning supply chain system.  There is a cost associated with inventory, and as demonstrated in the example, TOC can greatly reduce that cost.

The other benefit is the large-scale elimination of stock out situations except in the most extreme events.  Stock out items, by definition, are your fast-moving items that are in most demand.  Not to have them on hand, when necessary, means the risk of substantial sales forgone

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