Automating Process Mapping with the Industrial IoT

The Broad Applications of Process Maps

Process maps, also known as flowcharts, are typically one of the first steps in any process improvement project. They are also widely used in financial (statutory) audits to document the as-designed process and validate that adequate internal controls are in place – the primary mandate of the Sarbanes-Oxley Act and most other regulatory regulations. Process maps are also critical in ISO 9000 and related audits that seek to validate that an organization has properly documented their processes – doing what they documented, and documenting what they do.

Basic Process Mapping

Basic process map shows who and what is involved in a process and can be used in any business or organization revealing areas where a process should be improved. Virtually every Lean Six Sigma or financial project I led over the last ten years started with a process mapping exercise. This begs the question – were there no existing process maps in place prior to the project?  The answer in every case is that if there was an existing process map, it was woefully inadequate and too high-level to be of much use. In a majority of cases, existing process maps did not capture the reality of the actual operations.

 

Value-Stream Mapping

A value-stream map takes process mapping to a more sophisticated level in which not only the who, what, where, and when of a process is captured, but the actual work effort is separated out from the duration of the effort.  In its simplest terms, a process that takes place over 10 hours but only requires one hour of work would be rated as 90% wasted time and only 10% value-added time.

 

Gage Repeatability and Reproducibility (ANOVA Gage R&R) in Process Mapping

A Gage Repeatability and Reproducibility (R&R) is a measurement analysis technique that uses an analysis of variance (ANOVA) random effects model to assess a measurement system. Repeatability is the variation that is observed when the same operator measures the same part many times, using the same gage, under the same conditions. Reproducibility is the variation that is observed when different operators measure the same part many times, using the same gage, under the same conditions.

In my experience, Gage R&R should be a vital component to any process or value-stream mapping exercise but is rarely utilized.  The reason is simple, it is very difficult and unrealistic to capture the repeatability and reproducibility of any process unless you manually observe and measure operations from one operator to another, and then observe the same operators over multiple days.  Consider a simple process with 10 steps by 10 individual operators performing the 10 process steps in a day.  To validate the Gage R&R, all ten steps and all ten operators would have to observe and measured over a minimum of 5 days. That is 500 operations.  Since multiple operations are occurring simultaneously, a team member would have to be assigned to each operator.  You get the point – no one has this depth of resources or patience to adequately perform a Gage R&R.

As an alternative, project teams typically host facilitated working sessions in which operators and/or their supervisors offer their versions of reality.  Ironically, it is not uncommon to revisit the results of such an exercise in follow-on sessions, in which the participants change their minds, and may openly disagree with each other. In other cases, they can’t agree on a standard process as each operator has their unique way of doing things.

 

How to Improve Process Mapping?  Automating Process Maps Using the Industrial IoT

There is a solution to the subjectivity, inconsistency, inaccuracy, and labor-intensive nature of today’s process maps, value-stream maps, and Gage R&Rs.  Using IoT sensors to passively and continuously monitor operations and operators, one will capture the actual way things are done. This can and usually does capture a wide variety of inconsistencies from person to person and from day to day. The key is that the monitoring is unobtrusive to prevent the Hawthorne effect – operators acting differently because they are being watched and measured.

To be truly effective, the acquired information is married to advanced analytics to statistically capture the ANOVA – an analysis of the variations in a process, and allowing the capture of operations with all the variations from the as-designed and as-documented process.

By automating this critical process, industrial engineers, Lean Six Sigma professionals, and continuous improvement teams can greatly reduce the time-consuming and ineffective nature of today’s process mapping exercises.  They can focus on much higher value-add activities – standardizing, simplifying and automating ineffective processes and eliminating bottlenecks.

Author:

Anthony Tarantino, PhD

Adjunct Professor, Santa Clara University – Operations and Finance

Six Sigma Master Black Belt, Certified Scrum Master, CPIM (APICS), CPM (ISM)

Senior Advisor to Atollogy for Alliances and University Relations

Tony@atollogy.com