Category Archives: Quality

Seven Wastes and One More: Lean Perspectives on Enterprise Support

Folding Rule

It’s very exciting to see the increasing coverage Lean Manufacturing (or just Lean) is getting. From my experience, Lean provides very useful and intuitive methodologies for process improvement and streamlining as well as a more robust set of tools for advanced implementations. A good number of books have been written about Lean Manufacturing over the years, from its origins in the automotive industry with Toyota (hence the alias TPS for Toyota Production System) through other implementation to the influential ‘The Lean Startup

In its most basic form, Lean provides guidelines and ideas for reducing or eliminating waste in process driven operations. Waste, for this purpose, is defined as anything that does not add value that the customer will appreciate and pay or. There are seven different types of waste (sometimes known by their Japanese name Muda) that are recognized: Transportation, Inventory, Motion, Waiting, Over Processing, Over Production and Defects. Additionally, for some organizations, including Enterprise Support, I believe it is useful to add Unused Employee Talent as the eighth waste

When discussing these wastes we should keep in mind that some apply to the work in progress (from cases and knowledge items through equipment being repaired to employment candidates) while others apply to the resources (people or machines) that perform the work. We should also know that Lean distinguishes between necessary waste and unnecessary waste. Necessary waste, known as ‘Type I’, could, for example, be any activity that’s required for compliance – from inventory audit to health and safety drills. Un-required waste is known as ‘Type II’, and should be eliminated. While the first distinction is built into each waste’s definition, determining which wasteful activity is necessary and which isn’t is subject to individual analysis

With this understanding, let’s look at each waste and how it can manifest itself in the enterprise support world. With that understanding we can determine how reducing or eliminating it can help us improve our operation. We should also note that certain wastes can generate other types of waste:

  • Transportation – is focused on the work items, and can equally apply to physical movement as well as to transitions of ownership or responsibility. For example, are we moving cases unnecessarily between teams and individuals? How much extra work is created every time we change case ownership? For hardware support organizations, are we moving returned equipment between departments or locations unnecessarily? Excessive transportation can sometimes lead to increased inventory – another waste
  • Inventory – this is another work item waste. Are we keeping cases open unnecessarily? Why do we have so many open cases? In what stages are they open? Do we respond to all recruitment candidates as soon as a decision was made? How much equipment do we have waiting to be repaired? What’s keeping us from getting that equipment to the lab? How much inventory do we keep to support potential customer failures?
  • Motion – this is a resource waste. For example, are we asking support engineers to move physically in order to do parts of their job, for example go to the lab to recreate a problem? Are we requiring our employees to repeatedly switch contexts? For example, do we instruct support engineers to drop whatever they are doing to answer incoming phone calls?
  • Waiting – this is another resource waste, where people or machine are idle due to lack of work or the need for another person’s knowledge or a constrained resource. Typical examples include access to systems needed to recreate a customer’s problem, access to a specialist with knowledge that can help make progress with a case and generally waiting for any other person or resource required to complete a task
  • Over Processing – this is a work item waste, where the organization invests work that does not add customer value. For example, complicated forms acting as a barrier to escalations rather than facilitate rapid problem resolution, repeated analysis of a customer’s problem by multiple engineers and repeated attempts at fixing a customer’s problem or repeated repairs of returned equipment
  • Over Production – this also is a work item waste. It usually refers to making items ahead of customer demand. A typical example could be preparing and publishing knowledge items for a one-time problem that will never be used again, or repairing products to be used to replace customers’ failed units in excessive quantities
  • Defects – again a work item waste. It focuses on an end product that does not address a customer’s need. From providing the wrong fix through creating a knowledge item that misguides customers to hiring a person that’s not qualified to do the job
  • Unused Employee Talent – I have written in the past about the impact of untapped employee talent from a knowledge management perspective. Additional examples include forcing escalations to happen within a certain time, even when the front line engineer has all the knowledge required to resolve the customer’s case

I am sure every reader can think of many other examples for wastes in their own environment. This post does not purport to provide an exhaustive list. Rather, it was an attempt to give readers a glimpse into the world of Lean as an additional tool to use for improving process flows. I hope you begin to make use of Lean thinking and that it works for you as well as it did for me

Observations on Customer Experience Improvement

Good Better Best Keys Represent Ratings And Improvement

Just came across a very interesting article courtesy of McKinsey Quarterly‘s Classics mailing, discussing the need to optimize service delivered to customers needs. While it focuses on consumer businesses there are a few key learnings for enterprise technology support operations, demonstrated by this quote:

“Finding these savings requires rigor in customer experience analytics: […]. It also requires a willingness to question long-held internal beliefs reinforced through repetition by upper management. The executive in charge of the customer experience needs to have the courage to raise these questions, along with the instinct to look for ways to self-fund customer experience improvements.”

The McKinsey article identifies two main drivers of resistance to implementing effective improvements:

  • Organizational momentum and deep held, but often misplaced, beliefs. Frequently these beliefs are shared across management levels, causing the company to operate with ineffective goals that remain unchallenged
  • Lack of both rigorous analysis as well as the ability to present those results convincingly and effectively

A future post will discuss analytics techniques. Here I’d like to focus on a few causes preventing organizations from progressing towards effective and efficient improvements and combine a few of the blog’s previous posts for the benefit of new readers. First, we previously had a high level discussion of several differences between consumer and enterprise support, namely the different roles we interact with and the many more opportunities for friction.

We also wrote about complexity and volume. This is a very useful distinction, touching on the operational differences between high volume/low complexity operations and those with low volume/high complexity, as most enterprise support operations are.

Having said that, we frequently find concepts and metrics from consumer businesses that are not beneficial for the unique challenges of supporting enterprise technology. It becomes our job to educate the organization and provide deep, actionable insights to help the company excel efficiently. We’ll cover that in future posts

Basic Concepts of Six Sigma

Six Sigma Blue Stripes Horizontal

I recently came across a post titled Using Six Sigma to Improve Customer Experience and Service. As it touches on several topics close to my heart I read it with great anticipation and sadly even greater disappointment.

Since I feel the author misses the basic concepts of six sigma and the many improvement opportunities it offers support and services organizations I decided to attempt and correct some of the misconceptions and offer a different perspective to some of the points discussed.

First and most obviously missing is the fact that six-sigma is an iterative improvement process. DMAIC, therefore, is circular as shown in the chart attached to the original post rather than being a one time linear activity, at the end of which is the six-sigma nirvana stage of 3.4 defects per million.

Second, the statistical concepts behind six-sigma are never mentioned, nor is the meaning of 3.4 defects per million opportunities (known as DPMO). Here is a brief explanation. Sigma is a measure of spread of a normally distributed population, and measures the number of standard deviations fitting within a certain range. That range, in turn, is the acceptable performance range, so performance within that range is considered good, and outside of it is bad. To achieve one sigma, for example, about 68% of the population must be within the acceptable performance range, for two sigma, 95.5%, and so on, as shown below:

Sigma ValuePercentage Within Range
~68.2%
~95.5%
~99.73%
99.993666%
99.9999426697%
99.9999998027%

The last item I’d like to touch on is the need to make quality improvement, six sigma or otherwise, an inclusive initiative. It should ensure each and every employee understands the improvement process and expected results, and is able to make contributions.

Obviously, it is not possible to increase sigma levels by going through the phases of DMAIC without transforming service delivery, and it is extremely unlikely that a single journey through DMAIC will take you into six sigma performance levels. However, the benefits of improvement, even from one sigma to two sigma are enormous. So, the value in six sigma is in the journey and in creating the continuous improvement culture rather than reaching the elusive ultimate destination.