Discrete Event Simulation and Lean Six Sigma

Subject: Tech & Engineering
Pages: 5
Words: 1391
Reading time:
6 min
Study level: PhD

Lean Six Sigma and Design for Six Sigma defined

Lean Six Sigma is a robust, data-driven approach used to improve a process. It also uses a result-oriented strategy. Lean Six Sigma was a methodology that was conceived by companies such as Motorola, Xerox, Caterpillar, and so forth. It always integrates two management strategies of businesses. Lean Six Sigma uses the policy of working less to produce a valuable product. This policy assists in saving time, it ensures that they make something tangible out of the little time they have. Just in case of any adjustments, it can be done in less time possible to suit the needs of the client.

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With the help of the Six Sigma scheme, Lean has been able to discover and eradicate faults during product development. Decision-making procedures of a company have been hastened by the use of Lean Six Sigma (Jerry, John, Barry & David 2005). As a result, an increase in the efficiency of production and an increase in product quality has been realized. Customers’ perspective determines the products’ quality. Therefore, Lean Six Sigma has enabled managers to tackle issues related to quality, speed, and cost of production.

DFSS is an amended scheme applied to formulate modern products which possess a six sigma quality. It is always implemented when there is a need for incremental improvement on the current product. Tools used in DFFS are green belt, black belt, and master black belt of Six Sigma experts. The main intention of the DFSS method is to produce the quality of a product that meets the requirement of consumers (William & Erminia 2000). For these reasons, producers must be in a position of understanding the requirements of a customer. DFSS helps in increasing the market shares of companies. It helps in the production of quality products at a lower cost.


Simulation is the process of formulation of a conceptual model which undergoes translation to a computer model. This process is always carried out during the planning process. These concepts are always expressed by the use of logical and flow diagrams which are nontechnical. Simulation is a data-driven process that forces the producers to know where they require the collection of data and at what stage is it necessary. Before using the data, it must be analyzed and reviewed.

Simulation is used to compare advanced alternatives of how to face a challenge. This can only be known by identifying input factors that can undergo drastic change. The relationships of main elements are provided by a valid computer model (Basem & Raid 2006). After the building and the validation of the model are done, a lot of tryouts will be carried at differently. This helps in the prediction of the system performance when exposed to different environments and conditions. Thereafter, the simulations are delivered to stakeholders.

How discrete event simulation can be used in Lean Six Sigma

Discrete event simulation is a computer modeling tool. It assists engineers to know the effect that variance can attain on the system of production. Discrete event simulation can be used in the planning phase to model proposed changes of a system (Jerry, John, Barry & David 2005). Changes are always catered for during the improvement project phase. Modeling changes assists in gaining the confidentiality of the designers. They always believe that whatever they are modeling at this phase will lead to its intended goals and benefits.

Simulation systems also deal with service operation and logistics apart from manufacturing. Discrete event simulation can be done on many levels beginning with the micro. Important customers’ perspectives can be drawn well for designers’ understanding by the use of the CTQ tree. Discrete event simulation causes a lean six sigma to increment in performance and behaviors. Lean Six Sigma involves a system that has conditional procedures. The sequence of steps depends on the previous operation. Discrete event simulation is used in lean six sigma to carry out what-if analysis (William & Erminia 2000). This makes it easier the prediction how the performance of a system will be in the future.

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Benefits of discrete event simulation in lean six sigma projects

Discrete event simulation has been of greater help to Lean Six Sigma designers. One, it has cut down the cycle time of the project. The cost of production has also been reduced and the application of the effort from the conception to a time of validation has decreased as well. Secondly, a breakthrough in performance has been brought out by discrete event simulation. This is as a result of analyzing the requirements of customers and businesses. Coming up with several diverse solutions which are not vulnerable has contributed to the same. In the same line, discrete event simulation has brought a breakthrough by coming up with the optimum solution of validation and verifications (Paul & Cyrille 2012).

Discrete event simulation provides the designers of Lean Six Sigma projects with minimum trust in physical prototypes. It fastens the time of delivery of the product to the market. Discrete event simulation minimizes defects as well as post-design rework. It also provides a way of testing the consequences of changes that may be done to a system before its selections and implementations. This enables the stakeholders to visualize their systems before they come to reality. Discrete simulation helps in quantifying improvements made in Lean six sigma. Simulation is a key element that assists in the reduction of variability of a complex production by enhancing understanding and experimentation in various ways.

Discrete event simulation helps stakeholders to foster the view of their proposals therefore they can know their shortcomings and improve where necessary. With simulation, uncertainty is a thing of the past when producing a model. It shows how a system will react when certain inputs are subjected to it prior to its actual production begins. The simulation highlights the process of a structure by conceptualizing which step comes after the other (Steve 2006). Clear information is needed on the same to enhance the rate of production without any confusion in the order of sequence.

Since a lean six sigma is a data-driven process, discrete event simulation has been of benefit in terms of parameter quantification. This is done via data estimation and analysis. Parameter quantification can also be done by stating the distribution of probability. Parameters such as the timeline for the production can be determined through data collection. Another benefit that Lean Six Sigma enjoys is, that there is an inexpensive and quick way of weighing the performance.

This can be done by determining how a system can react when subjected to different environments. Collaboration during Lean Six Sigma has been improved. Discrete event simulation has enabled the stakeholders to agree on things like performance measures, decision variables. This takes place just at the beginning of the project where its objectives are also incorporated.

Simulation can also result to open grounds for the decision. After simulation, stakeholders always come to a central point of arguing out their thoughts based on the results (Basem & Raid 2006). This may as well create an opportunity for adjustments in the project. They finally come up with a decision that is approved by all the shareholders without excluding some members. Therefore, the level of performance of the system depends on simulation which helps in understanding the process collectively. Recognizing the problem and rectifying it, all comes to experienced while simulation a model.


With the help of simulation, stakeholders have managed to save a lot of time that could have been wasted. Building a project without understanding the repercussions it can be faced with used to take place before the invention of simulation. Simulation has also saved on the cost of the resources used. Projects can only commence once the stakeholders are sure of whatever they what to construct. The rate of performance has not been left out as well.

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Discrete event simulation has assisted the shareholders to know how effective their projects are before they come out on the grounds. Visualization has helped them to know where they should adjust. Therefore modeling is nowadays considered a perfect activity. Whenever a project reached the validation point, it will obviously satisfy the need of the client beyond doubt of uncertainty. With the help of the Six Sigma scheme, Lean has been able to discover and eradicate faults during the product development


Basem, E. & Raid, A. (2006). Simulation-based Lean Six-Sigma and Design for Six-Sigma. New York: Wiley-Interscience Publishers.

Jerry, B, John, C, Barry, N, & David, N. (2005). Discrete-event system simulation. New York: Pearson.

Paul, H. & Cyrille, I. (2012). Models, Simulations, and Representations. London: Routledge.

Steve, C. (2006). Virtual Decisions. New Jersey: Lawrence Erlbaum Associates.

William, D. & Erminia, V. (2000). Dynamic Models and Discrete Event Simulation. New York: Dekker INC.