Success in Simulation

By Jeff Joines (Associate Professor In Textile Engineering at NCSU)

This is a three part series on Six Sigma, Lean Sigma, and Simulation. The first blog will explain the Six Sigma methodology and the bridge to simulation analysis and modeling while the second and third parts will describe the uses of simulation in each of the Six Sigma phases and Lean Sigma (i.e., Lean Manufacturing) respectively.

“Systems rarely perform exactly as predicted” was the starting line for the blog Predicting Process Variability and is the driving force behind most improvement projects. As stated, variability is inherent in all processes whether these processes are concerned with manufacturing a product within a plant, producing product via an entire supply chain complex or providing a service in the retail, banking, entertainment or hospital environment. If one could predict or eliminate the variability of a process or product, then there would be no waste (or Muda in the Lean World which will discussed in a third part) associated with a process, no overtime to finish an order, no lost sales owing to having the wrong inventory or lengthy lead-times, no deaths owing to errors in health care, shorter lead times, etc. which ultimately leads to reduced costs. For any organization (manufacturing or service), reducing costs, lead-times, etc. is or should be a priority in order to compete in the global world. Reducing, controlling and/or eliminating the variability in a process is key in minimizing costs.

Six Sigma is a business philosophy focusing on continuous improvement to reduce and eliminate variability. In a service or manufacturing environment, a Six Sigma (6?) process would be virtually defect free (i.e., only allowing 3.4 defects out of a million operations of a process). However, most companies operate at four sigma which allows 6,000 defects per million. Six Sigma began in the 1980s when Motorola set out to reduce the number of defects in its own products. Motorola identified ways to cut waste, improve quality, reduce production time and costs, and focus on how the products were designed and made. Six Sigma grew from this proactive initiative of using exact measurements to anticipate problem areas. In 1988, Motorola was selected as the first large manufacturing company to win the Malcolm Baldrige National Quality Award. As a result, Motorola’s methodologies were launched and soon their suppliers were encouraged to adopt the 6? practices. Today, companies who use the Six Sigma methodology achieve significant cost reductions.

Six Sigma evolved from other quality initiatives, such as ISO, Total Quantity Management (TQM) and Baldrige, to become a quality standardization process based on hard data and not hunches or gut feelings, hence the mathematical term, Six Sigma. Six Sigma utilizes a host of traditional statistical tools but encompasses them within a process improvement framework. These tools include affinity diagrams, cause & effects, failure modes and effective analysis (FMEA), Poka Yoke (mistake proofing), survey analysis (voice of customer), design of experiments (DOE), capability analysis, measurement system analysis, statistical process control charts and plans, etc.

There are two basic Six Sigma processes (i.e., DMAIC and DMADV) and they both utilize data intensive solution approaches and eliminate the use of your gut or intuition in making decisions and improvements. The Six Sigma method based on the DMAIC process and is utilized when the product or process already exists but it is not meeting the specifications or performing adequately is described as follows.

Define, identify, prioritize, and select the right projects. Once selected to define the project goals and deliverables.
Measure the key product characteristics and process parameters to create a base line.
Analyze and identify the key process determinants or root causes of the variability.
Improve and optimize performance by eliminating defects.
Control the current gains and future process performances.

If the process or product does not exist and needs to be developed, the Design for Six Sigma (DFSS) process (DMADV) has to be employed. Processes or products designed with the DMADV process typically reach market sooner; have less rework; decreased costs, etc. Even though, the DMADV is similar to DMAIC method and start with the same three steps, they are quite different as defined below.

Define, identify, prioritize, and select the right projects. Once selected to define the project goals and deliverables.
Measure and determine customer needs and specifications through voice of the customer.
Analyze and identify the process options necessary to meet the customer needs.
Design a detailed process or product to meet the customer needs.
Verify the design performance and ability to meet the customer needs where the customer maybe internal or external to the organization.

Both processes use continuous improvement from one stage back to the beginning. For example, if during the analyze phase you determine a key input is not being measured, new metrics have to be defined or new projects can be defined once the control phase is reached.

Now that we have defined six sigma, you may be wondering what is the bridge to computer simulation and modeling. Simulation modeling and analysis is just another tool in the Six Sigma toolbox. Many of the statistical tools (e.g., DOE) try to describe the dependent variables (Y’s) in terms of the independent variables (X’s) in order to improve it. Also, most of the statistical tools are parametric methods (i.e., they rely on the data being normally distributed or utilize our friend the central limit theorem to make the data appear normally distributed). Many of the traditional tools might produce sub-optimal results or cannot be used at all. For example, if one is designing a new process or product, the system does not exist so determining current capability or future performance cannot be done. The complexity and uncertainty of certain processes cannot be determined or analyzed using traditional methods. Simulation modeling and analysis makes none of these assumptions and can yield a more realistic range of results especially where the independent variables (X’s) can be described as a distribution of values. In Six Sigma and Simulation: Part 2, a more detailed look at how simulation is used in the two six sigma processes (DMAIC and DMADV) will be discussed.