One of my friends, in a meeting about improvement, ended his presentation with the quote, “The biggest room in the world is the room for improvement.”

Helmut Schmidt, a prominent post-World War II politician and statesman, is credited with this statement. It’s about optimism and knowing that we can do better – and be better. On the flip side, Taiichi Ohno is credited with saying, “Having no problems is the biggest problem of all.”

Thermal processing is one of the most critical steps in manufacturing supply chains, and it often represents a significant portion of the cost and takt time of the supply chain. Thermal processing is also capital-intensive – bringing capacity into the market is not a trivial thing. Traditional supply chains employ thermal processing that includes reheating of primary raw and semi-finished raw materials for deformation processing. In this category, the high temperature achieves a phase and set of properties that enables the processing – typically, by reducing flow stress and making deformation much easier. At the component stage of traditional supply chains, “heat treatment” transforms properties into the required finished component the function requires.

The relatively recent emergence of additive manufacturing, particularly metal 3D-printing processes, redefines the “thermal process” to a very localized thermal treatment that is, in effect, a blend of welding and “heat treatment” as the process. The material is simultaneously optimized. This column will cover improvement that has more applicability in traditional “subtractive” manufacturing, where materials are formed, machined and, in some cases, precision-finished to achieve functional requirements.


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The scientific method has been a transformative force for several centuries, with giants like Galileo, Copernicus, Sir Francis Bacon and Sir Isaac Newton in the mix. As industry moved from mechanization to mass production to automation of manufacturing processes,[1] a new revolution that brought defined efficient standardized work procedures and recognition of statistical variation evolved over the past century.

The improvement methodology called DMAIC is credited to W. Edwards Deming in the 1950s, and the statistical enabling of rapid improvement called Six Sigma is credited to Bill Smith of Motorola in the mid-1980s. There are some debates on when and how these evolved from a methodology to a management philosophy, but there’s no doubt that Jack Welch of GE brought the combined disciplines of Lean Manufacturing and Six Sigma in the DMAIC process into industry in a most public and prominent fashion, creating a movement that has been emulated by many high-performing manufacturers.

DMAIC stands for Define-Measure-Analyze-Improve-Control. The first step is often the hardest, and Define is the most critical step in doing anything worth improving. In a Lean Six Sigma class from my past, I recall the instructor, a brilliant statistician from a highly regarded university, started with something like (my paraphrase), “The first thing you need to do to be a Lean Six Sigma practitioner is have a good project (implied, scoped well)! Now that you have a good project, here’s how you work the DMAIC process.”

Well, that is the challenge, isn’t it? What constitutes a good project can vary, depending on where the stakeholders’ heads are at regarding the project scope, its place in their priorities and how well it stacks up with everything else currently in play. So, let’s take a quick look at some principles that bring alignment and effectiveness in the Define phase of DMAIC. Generally, there needs to be a business-relevant metric (throughput, cost, first time quality, etc.) that will be significantly improved if the DMAIC project is successful, and stakeholders should understand that metric and its relevance to their personal success.

Better-defined DMAIC projects are in a position to see the time to benefit reasonably clearly, and the ability to estimate the size of the benefits should be better than a factor of two. This means that an initial opportunity assessment is embedded in Define. In other words, set a benchmark to achieve, then map out what it takes to get it. What is possible, how much improvement can we capture and how quickly?

There is a bit of slow down at this stage to get Define clarified so that organizational alignment is solid, and there is a common understanding of what can be done and by when. This creates a good start for DMAIC. Now let’s try to bring opportunity assessment into the context of thermal processing.

Thermal efficiency is one of the more scientific and, frankly, harsh ways to benchmark. It is typically defined as energy content in the material being processed divided by the total energy into the system to execute the thermal recipe.[2] In the context of process heating, for example, the overall thermal efficiencies are quite low – on the order of about 15-20% for reheating steel billets for rolling.[3] For component heat treatments, thermal efficiencies are higher, particularly when considering induction hardening processes. That said, thermal losses to refractories and furnace structure can be quite large, as are those losses that can be recaptured by reducing gas waste heat. Reducing those energy losses is economically attractive and also improves the environmental footprint of the operation.

Whether the approach for benchmarking is highly idealistic – the goal is 100% efficiency with no losses to exhaust gas, surrounding furnace refractories, etc. – or a bit more pragmatic, the point is to be as clear as possible about what the opportunity is and how it will be quantified. This gets the team, including the process owner who “owns” the energy-efficiency metric, all on the same page.

Remember, the biggest room in the world is the room for improvement! The next step … Measure.

Raymond V. Fryan is the former Executive Director of New Product Development at ASM International, the world’s largest and most established materials information society. After graduating from Grove City College with a bachelor’s in metallurgical engineering, he joined The Timken Company and subsequently earned a master’s degree and doctorate from Case Western Reserve University in Material Science and Engineering. He is also a graduate of the Executive Development for Global Excellence program at the University of Virginia Darden School of Business. Ray held multiple roles at Timken and TimkenSteel for over 37 years, spending his career in quality advancement, manufacturing, process and product metallurgy, technology and business development. He can be reached at


  1. World Economic Forum Agenda, 2016