Supply Chain Improvement Tools


As most of the supply chain folks have faced varying degree of supply chain issues, I thought it would be useful to share some of the supply chain Improvement tools I have used over the years to resolve recurring supply chain issues.

Intensive competition, fast technological change, and shortened product life cycles are just a few of the recurring challenges faced by many firms in today’s competitive markets. In last decade many western manufacturing firms have increased outsourcing of component parts and services to independent, external suppliers, while focusing on core capabilities to face these challenges. Such firms may then become reliant on a supply chain performance consisting of suppliers of varying levels of performance to provide competitive services or products. Therefore, their suppliers’ performance is increasingly critical to the long-term success for firms supply chain. .

In Japan it was realized at an early stage that everyone in a company had to participate in the improvement work. This meant that the statistical or improvement tools, which were to be used, had to be fairly simple and yet efficient. In this blog I will give a short description of these ten tools for supply chain improvement tools, namely

  1. Data Collection
  2. Pareto Charts
  3. Cause-and-Effect diagrams
  4. Stratification
  5. Cross-Functional Process mapping
  6. Control Charts
  7. Histograms
  8. Flow Charts
  9. Scatter Plots
  10. Check Sheets


Let’s explore each supply chain improvement briefly:

1- Data Collection

The collection of data is one of the most important steps in a programme for quality improvement and almost the rudimentary supply chain improvement tools . Having substantial basis for decision- making is vital. It is, of course, also essential that the basis elucidates the topic in questions. If incorrect or misleading data are collected, not even the most sophisticated approaches will be of help in the analysis. From the very start we have to be aware of the purpose of the data collection.

  • What is the supply chain problem we are facing?
  • What facts are required to elucidate the problem?

Not until these questions have been answered is it possible to move on to collecting data.

2- Pareto Charts

There are several problems present in connection with a programme for quality improvements. In general only one problem can be solved at a time. The pareto chart (named by Juran after the Italian economist and statistician Vilfredo Pareto, 1848-1923) is then of great help with deciding in which order the problems should be addressed.

Note that in the Pareto chart:

  • Each type of defect is illustrated by the rectangle whose height equals the number of defectives on the left-hand scale.
  • The order between the different types of defects is such that the one with the largest frequency is placed furthest to the left. After that the number of defects decreases to the right. The smallest columns furthest to the right can possibly be put together in one group “others”, if each of them contributes too little.
  • A line illustrating the cumulative number of defectives or the accumulated percentage of defectives is often drawn. However, this line does not appear in all Pareto charts and its value can be discussed.
  • It is important always to state where and when data have been collected.

With the help of the Pareto chart the most serious problem is very clearly made visible. When the problem is solved we can move on to the next. In this way each problem is focused on, one at a time.

Often the Pareto chart shows that a small number of problems account for a large number of the errors or the poor-quality costs. Juran, therefore, spoke of “the virtual few and the trivial many” , The so-called 80-20-rule, which is often found in the field of business economics, states the same thing.

3- Cause-and-Effect diagrams

Once we have selected a supply chain problem its root cause have to be found. Here a systematic analysis can be made using a cause-and-effect diagram, which is also called a fishbone diagram or anIshikawa diagram. This type of diagram was introduced for the first time by Dr Kaoru Ishikawa in 1943 in connection with a supply chain improvement programme at the Kawasaki Steel Works in Japan. Its construction resembles a simplified fault tree.

In the diagram we first roughly describe those types of causes that can possibly produce the observed Supply Chain problem. Then we concentrate on one of these roughly described causes and try to investigate it in more detail.

The causes of the problem can often be referred to any of the following seven M’s:

  • Does the management provide sufficient information, support and means for the improvement activities?
  • Does the operator have adequate training, motivation and experience?
  • Are the proper tools available? Are the process parameters properly specified and are they possible to control?
  • Are the testing devices properly calibrated? Are the any disturbing environmental factors?
  • Is preventative maintenance adequately executed? Has the machine the capability to produce units with a variation which is sufficiently small?
  • What is the quality of the material used in the process? Are  the supplier’s quality activities adequate?
  • Does the environment affect production outcome?

Usually, the steps are:

  1. Develop a flow chart of the area to be improved
  2. Define the problem to be solved
  • Brainstorm to find all possible causes of problems (from seven M’s)
  1. Organize the brainstorming results in rational categories
  2. Construct a cause and effect diagram that accurately displays the relationships of all the data in each category.

4- Stratification

One way of deducing causes of variation is through stratification. If, you have data collected from different sources, then we should classify these data into subgroups and illustrate each group separately, for instance by using a histogram.

If these histograms differ substantially, we may have found a cause of the problem. Then it is a matter of going further to rectify the problem. Maybe an additional refinement of the cause-and-effect diagram is required?

A basic rule closely related to the stratification principle is that we must avoid mixing data of different origins. Through stratification we can obtain important information for the supply chain improvement work.

5- Cross-Functional Process Mapping

Cross- Functional Process Mapping involves creating teams who members are selected from very department involved in the value stream- from marketing to manufacturing to research and development. The next phase involves mapping each step within the value stream from upstream to downstream.

Using the cross-functional format, each step of the process is mapped out; along with the time each step takes. The result of the exercise will be twofold: a map that shows the current process, and an appreciation among team members of the contributions of their fellow team members.  The As-is map can be used to improve the current process (KAIZEN). If possible, any steps that do not add value in the customer’s eyes, or that are redundant, should be deleted.

6- Control Charts

The control chart was the major tool introduced by Shewhart to find if assignable causes of variation exist, in order to make a manufacturing process predictable. It is also an excellent tool to graphically show the output of the process in time order.

The basic ideal behind the control chart is to take out information from the process at regular intervals, create one or more suitable process quality indicators and based on these indicators check whether the process characteristics perform in a suitable and predictable way. Not only Is the process variation illustrated in the control chart as a function of time, but process changes are also indicated.

In a manufacturing process the information often is collected based on the produced units. The information is then weighed together in a suitable manner, for instance to an arithmetic mean or a standard deviation, and plotted in the chart. However, other measurements of the process might be even more informative.

There are two main purposes in using control charts. The first one is to identify assignable causes of variation in order to get the process stable. The second one is to quickly detect whenever change has occurred in a stable process, resulting in an alteration in the mean value or in the dispersion.

7- Histograms

There is a proverb saying “a picture tells more than a thousand words”, but it also holds that “a good picture tells more than a thousand numbers”. An important part of data analysis is to illustrate the data in a good way.

Using the histogram we can in an effective way illustrate how a product or process characteristic varies. Note that a histogram can very easily be obtained using a frequency table as a basis. The big difference is that the histogram generally describes relative frequencies and not numbers of observations.

8- Flow Charts

flow chart is simple a tool that graphically shows the inputs, actions, and the outputs of a given system. Flow charting is such a useful activity that the symbols have become standard and used in software like Microsoft Visio. The simple purpose of the flow chart is to help people understand the process and this is not accomplished with flow charts that are either too simple or too complex.

I personally use this supply chain tools extensively and will write detailed article on how this tools can drive whole heap of supply chain improvement ideas!

9- Scatter Plots

In cases where original conditions vary continuously it may be unsuitable, or in some cases impossible, to stratify. Instead a scatter plot can be used to illustrate how a process or product characteristic varies due to an explanatory variable. Maybe, the variation of the explanatory variables explains a great deal of the observed variation of the product characteristic. In that case, we have a good basis for quality improvement.

There are often many parameters influencing the product characteristic of interest. In such cases we should draw a series of scatter plots, one plot for each combination of the parameters, and of the product characteristic in combination with the parameters.

The kind of co-variation which can be interpreted from a scatter plot can also be used for controlling and supervising the process. Instead of measuring a product characteristic it is better to measure an explanatory parameter directly in the process. By studying the process parameter instead of measuring a finished product, we can more rapidly prevent the problem of process variation. Note, however, that we can be on guard against nonsensical correlations. Perhaps both the product characteristic and the “explanatory” parameter may depend on a third parameter.

10- Check Sheets

Check sheets are devices which consist of lists of items and some indicator of how often each item on the list occurs. In their simplest form, checklists are tools that make the data collection process easier by providing pre-written description of events likely to occur. A well-designed check sheet will answer the questions posed by the investigator. Although they are simple, check sheets are extremely useful process-improvement and problem-solving tool. Their power is greatly enhanced when they are used along with Histograms, Pareto Charts etc.


To be a successful manufacturing and any other operations employees must eliminate supply chain related losses. However, some supply chain folks don’t have the necessary skills to perform the problem-solving tasks that accomplish this goal. They must be trained in the proper methods for logically and systematically discovering the root causes of a situation and this is where these supply chain improvement tools will be vital.

This article has briefly introduced various problem-solving supply chain improvement tools, and takes readers systematically through each tool so they can research them in depth when required.

10 Supply chain tools you should know about

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