Variation and its impact on Productivity

Variation or variability is one the biggest disruptors of productivity. Variation exists all around us and is most often misunderstood and overlooked in the Supply Chain and Operations environments. It is categorized in 2 groups, special or assignable cause variance and common cause variance. It exists in machines, materials, methods, measurements and business processes.

Special or assignable variation is generally a result of a decision made in the course of business, without clearly understanding the impact of that decision on the greater whole. An example could be deciding on the batch quantity for a new product to be launched based on the immediate availability of a process vessel, or of a piece of equipment that was previously mothballed. While the decision itself may be sound, based on price to purchase or refurbish, the consequences of the decision are causes of variation both upstream and downstream of the process for the foreseeable future.

Common cause variation can be a power failure or the process capability of a machine caused by the combination of tolerances in its component parts. While all the components are within their technical specification limits, the machine will respond slightly differently from others of the same make and model. Another example is the impact of the moisture content of a powder mixture or granule on its processing equipment. Even though we strive to keep our factory temperatures and humidity at constant levels, they fluctuate and this has an impact on the moisture content of a batch of WIP. The result is that the batch will either gain or lose moisture, altering its characteristics and needing a different setup for the processing equipment. So, it is important to control the time a batch spends in the various stages of WIP, and also to keep these times consistent between batches.


Quality is more than your products, it is the skills levels of your teams, the quality of all batch documentation, the state of processing equipment and the quality of the process operation when in production. To focus only on the first, leads to common error. When assessing a process, it is important to view each of these factors with an equal level of importance. This isn’t EITHER/OR, it is AND. As the QA personnel audit documentation, the operations personnel, Engineering, production operators, supervisors and managers need to review processes. These reviews should include maintenance history, Statistical Process Control charts for production completed and performance metrics for the process during the past period.

Efficiency, Productivity, Effectiveness and Measures of Effectiveness

Words that we use interchangeably in operations, but which actually mean different things. All need to be top of mind and must be highly visible to the entire team.

Efficiency is about optimizing resources, getting more out of existing or leaner systems and structures and thereby reducing your cost per unit.

Productivity is the measure of efficiency, the ratio between output volume and input volume

Effectiveness is about the quality and consistency of results, delivered by the team.

Measures of Effectiveness (MOE) are designed to record how we perform against objectives and the consistent achievement of desired results. They quantify the results to be obtained by a system and may be expressed as probabilities that the system will perform as required.

  • Measure of Performance (MOP)
    Measure of a system’s performance expressed as speed, payload, range, time-on-station, frequency, or other distinctly quantifiable performance features. Several MOPs and/or Measures of Suitability (MOSs) may be related to the achievement of a particular Measure of Effectiveness (MOE).
  • Measure of Suitability (MOS)
    Measure of an item’s ability to be supported in its intended operational environment. MOSs typically relate to readiness or operational availability and, hence, reliability, maintainability, and the item’s support structure. Several MOSs and/or Measures of Performance (MOPs) may be related to the achievement of a particular Measure of Effectiveness (MOE).

The direct impact

Common Error

The lack of understanding of variation and statistical process control techniques leads to organizations depending solely on formal In Process Control testing or post production analysis. It’s like trying to drive your motor car by looking in the rear-view mirror. Operators run blind, relying on memory and experience to set the machines on start-up, because all the information recorded travels with the batch document. This is exacerbated by the fact that batches stand around for varying lengths of time, resulting in changes in the characteristics of the product. Moisture content change, particle separation, aging of the granule etc. Each of these changes requires different settings on machines, even if this change is small. Together, they impact on variance which results in losses on start up because the ever-changing parameters.

In closing, to perform at optimal levels sustainably over time, it is important for manufacturers to make sure they have stable, efficient and effective processes and operations teams. Without the correct foundations in place, performance levels will fluctuate constantly leading to variation in both throughput and quality and teams will run in circles trying to figure out the causes. This results in further loss in throughput and sales, and increasing levels of inventory. All costs remain the same (or increase due to overtime), so your Return on Investment will fall proportionately.

Variation forms part of a compliant productivity program and you can request a link to a short introductory video to the modules that make up the program. Please send an email or LinkedIn request to You can also call me on +27 82 777 0922.

About the author

Dave Hudson is a supply chain and operations specialist coach with over 35 years’ experience, and currently 1 of 5 endorsed Demand Driven instructors on the African continent.

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