Our thoughts on classical logistic concepts

The Pareto Principle - 80% of the result can be achieved with 20% of the effort

Completing a project that achieves 60% of a goal is more useful than failing at a project that would have gone all the way. Large projects demand more time before achieving results and always entail greater risks. Striving for the perfect solution is not profitable, since according to the Pareto principle 80% of the maximum can be achieved with only 20% of the effort. If you know your company's processes and choose the right investments, the ROI is very fast.

The Pareto principle, a.k.a. the 80/20 rule, can be applied to a number of phenomena, both at home and at work. For example, it is very common that 20% of articles handled in a company stand for 80% of the value.

At MA-system we work according to the Pareto principle

We deploy resources where they do the most good. For this reason, we work together with our customer to set a level of ambition that becomes our goal. By prioritizing and focusing on the parameters that will carry us faster towards our goal, we can achieve great results at a relatively small work effort.

Flow Management - The flow is what generates money, not the stock

We're not saying that stocks are a waste, just that they are a cost, and should be used mainly as a buffer against variation and uncertainty of delivery times and sales. There is no point in building systems and models that focus on finding the optimum in warehousing costs. The flow is what generates money, and the flow is what needs to be managed, not the warehouse.

Efficient flows give you a competitive edge and help raise revenue. Flow costs are reduced by minimizing handling and shortening lead times.

Lower inventory levels by reducing uncertainty

In order to lower inventory levels and manage the flow efficiently, you must do something about the uncertainty. This might mean better machine maintenance in order to make production more reliable, shorter lead times in order to reduce dependence of uncertain forecasts, or a closer collaboration with suppliers and customers to avoid being surprised by increases or decreases in demand.

Flexibility - Traditional methods no longer work

Many of the most common methods of working used today were developed in a time when the technology was unable to update information directly at the time of the transaction. The models are often based on forecasts and on theories which are exact in themselves, but whose results are wrong. The reason is that they require great quantities of data that are hard to gather and have to be estimated, and that calculations often have to be carried out for entire batches. One example is the Wilson formula.

Even though both the world and the technology have changed, the old ways of thinking have been cemented. To achieve greater flexibility and efficiency in handling fast material flows, you need dynamic real time systems.

Fast change requires flexible systems

In modern corporate culture "constant improvement" has become a leading concept, and the technology has to keep up. Therefore the systems need to be flexible and adaptive, i.e. they need to be able to adapt ti changes in the flow.

The limits of this flexibility are set at an early stage, by your choice of system and provider/supplier.

Real time makes daily tasks easier

A system that works in real time means that you can trust the figures to be correct and to show the situation as it is at this moment. Thanks to this, the system can be used for hands-on, everyday tasks such as guidance in moving resources between work areas to even out the work load and make sure the work gets done in time.

The Big Picture - The old models ...

Companies used to be viewed as separate units, and models were designed to solve only problems within the company.

... and the new supply chains ...

Today, companies connect into networks or chains, increasing the flow rate throughout the supply chain to satisfy an ever more demanding customer.

A company that exchanges its old way of working for the new chain model can cut its throughput times considerably and create a "win-win situation" where everyone gets a share of drastical cost reductions while the customer gets better service and lower prices. In the long run, this leads to increased sales.

... don't work well together.

But the traditional methods still aim to create advantages for one particular company at the expense of all the others in the chain. Those who "obey" the old models will therefore automatically defend their own interests. By doing this they hurt the other parts of the chain, and the chain weakens. In the end, this backfires on themselves.

A new way of thinking

The ability to collect and process information in real time has created possibilities for completely new ways of working, adapted to the idea of a supply chain and with routines that make the chain considerably more efficient.

Information in Real Time - Learn from history, but don't manage by it

To control a process, you need information about its state. When the process changes faster, the information has to be gathered more often, or decisions will be based on obsolete facts.

If you increase the speed of information, you reduce stocks

Modern information technology allows you to see the state of the supply chain change in real time. Instead of uncertain estimates and obsolete figures, we can base ourselves on the reality of this moment. This reduces uncertainty and drastically affects our need for stocks.

If you increase the speed of information, you increase the flow rate

When information can be had in real time, only the physical handling limits the speed of the flow of material. Therefore, logistics is as much about flows of information as flows of goods.

Correct information is quick information

The speed of information affects its reliability. Logistical information is correct if it always shows the situation right now; in other words, the information is available in real time.

MRP - You don't trust what you can't understand

In the ´70s and ´80s, decision support technologies were developed with the aim of planning the material requirement. The basic principle of these Materials Requirements Planning (MRP) systems, and later Manufacturing Resource Planning (MRP II) systems, is that you enter conditions such as orders and forecasts, and receive a "wish list" with capacity requirements and suggested start days for production orders.

MRP is a complex mathematical model which is hard to survey. A small change of data, such as the size of an order, can have consequences that seem illogical and are therefore hard to understand. As a result, the planner often makes his own decisions based on practical experience, because:

  • You know that the calculations are based on average values and guesses at lead times, and are therefore probably wrong anyway.
  • The software system is not much help in pointing out what and how much needs to be changed.
  • Time is of the essence, and a planning cycle can take as much as several days to compute. This means the schedule is obsolete when it is presented, and therefore useless.

Seeks the optimum at each level, and forgets the whole

MRP is frankly ill-suited for use in a changing environment, where new calculations have to be made often. When we make a new calculation, several optimizations will show a different value than last time. These are moved up to the next level and lead to suboptimization. The interesting thing is that the whole, the big picture, is absent from MRP.

Requires long delivery times to the customer in order to be stable

In order for MRP to work, the delivery time from the supplier plus the production time need to be shorter than the delivery time to the customer. When this is not true, you have to resort to emergency solutions such as forecasts and frozen schedules.

Forecasts are guesses, and should be used for strategic decisions ? not operative ones. And in a time when adapting to the customer is a success factor, freezing your schedules is not a very clever thing to do.

Slow systems cannot control a dynamic environment

The complexity of the calculations means that it is realistic to compute MRP once a week. Using a week-based method when each hour is a competitive advantage means that you will have to "put out fires" every day to make deliveries on time.

The Wilson Formula

The Wilson formula is a traditional method for determining the order or production quantity if you know the total consumption during a period of time. The formula assumes that the only costs entailed are a warehousing cost per stock keeping unit and a one-time cost every time an order is placed, known as administrative re-ordering costs. The formula tries to find an optimal balance between the two costs to minimize the total cost, which is known as the economic order quantity (EOQ).

The conditions are not met

In order for the Wilson formula to work, a number of conditions have to be met:

  • Demand is constant and continuous
  • The lead time for receiving ordered goods is constant
  • Administrative re-ordering costs and warehousing costs are constant
  • The order quantity does not need to be expressed as an integer
  • The entire order quantity is delivered to the warehouse on the same occasion
  • No shortages allowed
  • The price/cost is independent of time requirements and ordered quantity

Also, the formula doesn't take into account the one thing that really costs - handling.

The values are hard to determine, and even harder to keep up-to-date

Lagerhållningskostnaden är i Wilsons formel kopplade till värdet på artikeln. Det ger en falsk bild, eftersom det kostar mer att hantera t ex en bunt rör än ett beslag.

The warehousing cost in the Wilson formula is connected to the value of the article. This is misleading, because it costs more to handle e.g. a bunch of pipes than one fitting. The administrative re-ordering cost is hard to determine, and is also dependent on the type of article. For example: Is the transport cost the same if a construction company orders 100 units of insulation wool as when it orders the same number of screwdrivers?

Because the values are usually dependent on the type of article, a large amount of data needs to be defined. The big problem is maintaining these huge amounts of data, which usually means they are not updated. In other words, you navigate using an obsolete map. The question is, was it ever correct?


Many attempts have been made to minimize the effect of the unreasonable conditions of the original formula. For instance, there are amendments that handle shortages, differences in lead time and differences in demand. The problem is that these new formulas require even more data that is hard to collect, and the results are often the same as with the original formula, while the amount of work spent on administration and gathering information is significantly increased.

Using the formula is an example of suboptimization. The desire to reach an optimal solution to a local problem steals resources from the whole.

Why is the formula used in spite of the unreasonable conditions?

We suspect the reason is this: When the total cost is optimized, the graph is very level, which means among other things that a 50% deviation from the optimum only affects 8% of the total cost. Since the formula produces nearly the same result regardless of how the figures vary, there is a feeling of safety, which is reinforced by the scientific aura of the formula.

This way, you can keep following the well-trodden path - and where it will take you is something few consider.

What should be done instead?

Our suggestion is a new concept that turns the model around - a solution based on vendor managed inventory and full transparency towards the market and everyone in the supply network. Rather than manage orders, the system works in real time and the vendor is responsible for always keeping the customer?s stock at the desired level. The application built around this way of thinking is called PipeChain .