Six Sigma and Simulation: Part 3

By Jeff Joines ( Associate Professor in Textile Engineering

This is the final installment of the three part series on Six Sigma, Lean Sigma, and Simulation. The first part explained the Six Sigma methodologies and linkages to simulation while the second part discussed where simulation could be used directly in the two six sigma processes (DMAIC and DMADV). The final installment will demonstrate how simulation can be used to design Lean Six Sigma Processes.

Recently, the Six Sigma continuous improvement methodology has been combined with the principles of lean manufacturing to yield a methodology named Lean Six Sigma. Recall Six Sigma is a continuous improvement methodology used to control/reduce process variability while Lean manufacturing is a management/manufacturing philosophy that deals with elimination of waste and is derived from the Japanese Toyota Production System (TPS). When people think of Lean, they conjure up Just-in-time (JIT) manufacturing (i.e., parts or information arrive just when you needed and not before). The elimination of waste is key in Lean systems and Toyota defines three types of waste: muda (‘non-value-added work), muri (overburden), and mura ( unevenness). Most people think of the non-value added form of waste when referring to Lean (e..g., a part sits in queue for ten minutes before being processed for one minute which represents ten minutes of non-value-added time). Many of the Lean tools deal with eliminating this form of waste (muda). Toyota indentified seven original common wastes (paraphrased from “Lean Thinking”) that Lean tries to eliminate.

  • Transportation (moving products that is not actually required to perform the processing)
  • Inventory (all raw materials, work-in-progress and finished products not being currently processed)
  • Motion (people or equipment moving or walking more than is required to perform the processing)
  • Waiting (waiting for the next production step (i.e., queue up))
  • Overproduction (production ahead of demand causing items to have to be stored, managed, protected, as well as disposal owing to potential)
  • Over processing (due to poor tool or product design creating unnecessary processing, e.g., over engineered product that the customer doesn’t need or pays for or having a 99% defect free rate when the customer is willing to accept 90%)
  • Defects (the effort involved in inspecting, fixing defects, and/or replacing defective parts)

Lean Six Sigma utilizes the continuous improvement methodology (DMAIC) as a data-driven approach to root cause analysis, continuous improvement, as well as lean project implementations. Lean encompasses a wide range Lean tools hat are used to implement changes as seen Figure 1. Many of the tools still use the Japanese words (e.g., Poka Yoke or mistake proofing)

Figure 1: Graphical Representation of 24 Lean Tools and Their Broader Categories (Kelly Goforth’s Master Thesis at NCSU)

As was the case for Six Sigma methodology, simulation modeling and analysis can be used in many facets of the Lean implementation and can be quite critical in making decisions. Most improvements have to be documented and analyzed where simulation modeling and analysis can be used easily to ascertain the benefits of the improvements to the current process before actual implementation. The following is just a few cases where I have applied simulation.

Value stream maps are a critical step in becoming lean and should be used first to identify areas of improvement before applying tools randomly. Value stream maps differ from process flow maps in that VSMs contain all the value added and non-value added steps/activities, include the information flow along with the material flow to make the product, are a closed circuit from the customer back to the customer, and take into account customer’s Takt time (i.e., the time needed to deliver the product at the customer’s pace). In developing a VSM, typically a snap shot of just a few key products are mapped for a particular day. Once the current state VSM is developed, areas of improvement as well as the lean tools to achieve these improvements are identified; future state maps are then generated to illustrate the improvement potential. The value stream map can be used to develop a simulation model and a wide variety of demand streams and SKUs can be experimented with to determine the VA and NVA times, etc.

Ford in the early 1900’s utilized fixed flow assembly line (i.e., one production line made up all the machines to produce one car in a sequential line) to maximize throughput. However, when the number of products and part categories increased while lot sizes decreased, manufacturing moved to functional layouts (i.e., job shops) where machines were grouped based on function (i.e., drilling machines). Now parts would flow to all the groups necessary to be produced which introduced great flexibility but also increased travel time, waiting, WIP, defects owing to machine setup, etc. The lean concept of cellular manufacturing decomposes the manufacturing system into groups of dissimilar machines that can process a set of part families which ideally decreases transportation, setups, balances load. These are a mix of smaller job shops and flow assembly lines combined. Determining these part families and groups of machines is quite complicated. Simulation can be used to establish a base line for comparison of the proposed new systems. The new systems can be simulated with varying demand variation, maintenance issues to test the design of the cellular groups before the machines are moved or setup in the new manufacturing system.

When people think of Lean they associate it with JIT and simulation has been applied the most in this area. Pull scheduling systems differ from push systems (i.e., a forecast of a set of parts is sent to the first process and are then pushed through the system until completion) in that parts are not produced until they are needed. Kanbans (signals) are sent back to the previous process to replenish parts only when they have been used by the current process. Pull systems ideally have lower WIP and faster through puts but typically only work for stable demand streams. For example, we worked with a large company building a new plant with fairly large lead-times. Parts of the organization had been very successful in implementing Pull scheduling systems to fill their stock inventories. The company had put in place demand leveling as way to deal with widely customer demand variations. They initially asked us to evaluate where should they place supermarkets (i.e., places to store inventory (Kanbans)), what should the size of the respective kanbans for each SKU, etc. After building several simulation models utilizing their historical demand streams, we determined that the total volume that was being placed on the plant was like a tsunami that would engulf the supermarkets essentially turning it into a push system anyways (i.e., everything sent to the first process (raw materials) and the processed to the end). We demonstrated through simulation the supermarkets would have to be large to be effective and these Kanban sizes were just impractical. The simulation model told them before an enormous amount of money and time was spent developing the process and information system to handle it and they could focus on other Lean areas.

Most people are familiar with the last form of waste (mura) and its elimination through Heijunka (production leveling). Production leveling/load balancing works in conjunction with pull systems and these systems can be simulated again to see their impact as well as to determine where supermarkets (e.g., inventory buffers) need to be placed to reach balancing. Total Preventive maintenance (TPM) is another area where lean practitioners can benefit from simulation modeling to ascertain affect of different policies and schedules on the system.

For more information on Lean Manufacturing and the lean philosophy, I recommend two books by the James Womack et al.: “The Machine that Change the World” and his latest book “Lean Thinking.”


The three part series has hopefully shown how simulation practitioners possess a skill set that is extremely beneficial for Six Sigma, Design for Six Sigma, and/or Lean Six Sigma project. These types of projects are not very unique but just general simulation models that require us to learn their particular language. I find it easier to work on Six Sigma projects because the Lean and Six Sigma practitioners understand statistical analysis necessary for input and output analysis even though they typically have only used the Normal distribution.

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