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H. James Harrington

Quality Insider

Move Over Flowcharts

Simulation modeling is a dynamic tool for improving systems.

Published: Tuesday, June 9, 2009 - 15:00

During the 1980s using flowcharts was the “in thing” to do. Today, technology has provided us with a much more effective and useful tool: simulation modeling. If a picture is worth a thousand words, then one that logically simulates tasks and collects data has to be worth a million. Simulation models have the capability of considering complex interrelated tasks and structurally projected outcomes in a matter of seconds, providing users with validated, and usually quite reliable, results.

Author Charles R. Harrell defines simulation as “a means of experimenting with a detailed model of a real system to determine how the system will respond to changes in its structure, environment, or underlying assumptions.” It allows for a better understanding of processes with a goal of improving performance. Simulation modeling provides a means to evaluate, redesign, and measure or quantify customer satisfaction, resource utilization, process streamlining, and time spent.

Process simulation is a powerful means by which new or existing processes may be designed, evaluated, and visualized without running the risks associated with conducting tests on a real system. Dynamic process simulation allows organizations to study their processes from a system’s perspective, thereby gaining a better understanding of cause and effect in addition to predicting outcomes. Simulation’s strengths and capabilities make it an ideal tool in reengineering. It aids in evaluating, redesigning, and measuring:

— Customer satisfaction and quantifying it for the new process or system

— Resource use in the new redesigned process or system

— Processes and how to streamline them

— Time with a goal of minimizing it


During a reengineering effort, simulation can assist in the following areas:

Feasibility analyses. Examining the viability of new processes in the light of various constraints. Conducting a cost-benefit analysis or process evaluation.

Visioning. Exploring the possibilities for the system in the future state

Performance characteristics. Examining the performance metrics of a system either in its current or future state

Prototyping. Once a future-state vision for a reengineered process is generated, prototyping using simulation can help in implementation planning, risk assessment, and process design.

Communication. Disseminating information about the new reengineered process to the organization


Simulation can assist in creative problem solving, too. Fear of failure prevents people from coming up with ideas. Simulation allows for creative experimentation and testing, followed by selling the idea to management. It encourages an optimistic, “let’s try it” attitude.

Simulation can help predict outcomes. For example, it could help in predicting the response to market demands placed on a business system, analyzing whether the existing infrastructure can handle the new demand placed on it. Simulation can thus help determine how resources may be efficiently used.

Simulation can account for system variances. Conventional analytical methods, such as static mathematical models, don’t effectively address variance because calculations are derived from constant values. Simulation looks at system variances, taking into consideration interdependence, interaction among components, and time. It allows for a broader perspective.

Simulation promotes total solutions by modeling entire systems. It offers insight into a system’s capabilities, and the effect process changes will have on the system’s inputs and outputs. Additionally, simulation modeling allows for experimenting with system parameters without tampering with the real system. It provides more alternatives, lower risks, increases the probability of success, and generates information for decision support.

Simulation can be cost effective. As organizations try to respond quickly to changes in their markets, a validated simulation model can be an excellent tool for evaluating rapid responses. For example, a sudden change in market demand for a product can be modeled using a validated system model to determine whether the existing system can cater to this need.

Simulation can help quantify performance metrics. For example, the aim of a system may be to satisfy the customer. Using a simulation model, this requirement could be translated to the time required to respond to a customer’s request, which can then be designated as the performance measure for customer satisfaction. Simulation can help measure tradeoffs associated with process designs and allow for further analysis on parameters such as time to market, service levels, market requirements, carrying costs, SKU levels, and so forth. It thus provides a quantitative approach to measuring performance.

Simulation is an effective communication tool. It can be used to introduce a new reengineered process in a dynamic and animated fashion. This is a powerful means of explaining the function of various components to those who will use the new system, helping them understand how it works.

You don’t lose customers over averages; it’s the extremes that make them unhappy—e.g., you commit to a three-day delivery, and it takes two weeks. Simulation modeling is the only effective way to do Monte Carlo analysis of a total process.


About The Author

H. James Harrington’s picture

H. James Harrington

H. James Harrington is CEO of Harrington Management Systems, which specializes in total quality management (TQM), Six Sigma, lean, strategic planning, business process improvement, design of experiments, executive management mentoring, preparing complete operating manuals, organizational change management, ISO 9000, ISO 14000, and TRIZ. Harrington is a prolific author, having written hundreds of technical reports, magazine articles, and more than 35 books. He has more than 55 years of experience as a quality professional. Harrington is a past president of the American Society for Quality (ASQ) and the International Academy for Quality (IAQ).


Jim Harrington Responds

Thanks to all of you for your comments. As you can imagine, and as some of you pointed out, a topic like Process Simulation and Flowcharting isn't easily dealt with in a few hundred words. My column was meant as an overview, not a complete treatise on the subject. For more detail related to simulation modeling, and hopefully address some of your questions, please read "Simulation Modeling Methods To Reduce Risk and Increase Performance" by H. James Harrington and Kerim Tumay published by McGraw-Hill.
Quality Digest on behalf of H. James Harrington

Comparing Flowcharts to Simulation Models?

I think using flowcharts in the title and lead paragraph is a bit of a straw man. Most flowcharts are not used for as-is vs. to-be process modeling. Most are used just to document existing processes.

I've been tangentially involved in one simulation study 15 years ago, and as I recall, the it took a lot of work to characterize the process steps in order to build the model. This article makes it seems as though the computer is magic and simulation tools act as an oracle.

I would much rather read a real world case study and an evaluation of which types of processes lend themselves well to simulation. I imagine you would get better results with low-mix, high-volume, or highly automated processes than you would with a high-mix, low-volume, or highly manual processes.

Flowcharts vs Simulation

I'm sorry I hadn't read this until now, and missed these comments. As a simulation analyst and black belt, it is not surprising to me that those in the quality field are making these comments. To say that flowcharting is not used for as-is vs to-be process modeling is the biggest gaff that I have to respond to. I'm not sure what they call value stream mapping (VSM), if not a flowcharting method, and VSM is the most prolific tool in use now for analyzing processes! It's true that simulation has been around for awhile now, but the power of simulation to characterize and experiment with process variables has been nay-sayed for just about as long by the process improvement community. I'm not sure why, but my theory has always been that process owners don't understand their processes well enough to adequately characterize the details of their process, and therefore the process of building a simulation model is difficult. I have been doing manufacturing process analysis for a long time, and almost every time I go into a new facility, I will find details about the process that the engineers and managers of the process never knew. This needs to happen first, and so I'm encouraged by the VSM efforts the manufacturing community has made, but what Mr. Harrington is pointing out is that there is more to life than VSM and flowcharting. By the way, there are about 10 of us currently analyzing a low volume operation (1 ship every 5 years) with much success using simulation, and I have analyzed three robots passing product to each other using simulation, so the applicability of simulation is just up to the imagination of the analyst.


I agree with Breeze89, what does flow charts have to do with simulation analysis? Isn't this just another quality tool to further the goal of continual improvement of processes, no better or worse than a flow chart just a different application? Solid modeling and simulations have been used in the aerospace and other industries for years, but never has it totally elimnated the need for real world testing. You can control variables, but never eliminate them, after all isn't this why there is real life testing for simulation software as well?

Process Simulation Models Aren't New

I was using process simularion models (visually based, not text) ~20 years ago. Not sure why this article seems to make it sound like some new technology.

Surprisingly light on value

I agree with the others. More meat needed. Surprisingly light on value for a Harrington piece.

Missing the meat

As always, a well considered column and a topic more people need to be aware of. Yet, like most such pieces on simulation, it lacks the necessary details on implementation, and ultimately falls short in terms of its applicability to real-world problems. What exactly is the next step for the reader, who is now impressed with the efficacy of simulation? And where should the reader look to learn more? A stronger column would have delved into the software, which is a critical component of (and often barrier to) simulation, and would have guided readers to additional resources and case studies.


I thought, "Harrington- he's sharp. Let's see what he says about simulation." Nice article, but a little disappointing from the standpoint of missing the greatest pitfall. Mentioned that most simualtion produces reliable results. That's true, but these software programs still run on computers and the simulation output is only as good as the input. Very old catchphrase was "Garbage in, garbage out." This is still true, no matter how sophisticated the programming and it remains the grain of salt that must accompany all simulation reports. Second pitfall - the more eye candy in the report/presentation the more the uncertainty regarding the results. Trust, but verify.