The Effective Use of Simulation with TOC. Part 2 – In Production
On my entry titled Beyond DBR - the need for basic data, I talked about some of the reasons that simple data collection was needed. I also talked about that it was often not required in the early phases of process of finding bottlenecks, which I’ll call Phase 1. In Phase 2, the working data collection system generates Basic Data for each workstation that gives us some idea of the current bottleneck location. This is most effective in simple, serial systems with large buffers and few feeder lines. As the process to attack bottlenecks becomes more mature and the system becomes more balanced, it typically becomes more difficult to isolate the bottleneck. Here, analysis tools like simulation can be used. Finally, as the process changes the culture, the desire to know where the bottlenecks will be in the future becomes apparent.
For my purposes here, I’ll assume the reader is familiar with Phase I, and I’ll cover Phase 2 & 4 in future blogs. In this entry, I’ll talk primarily about Phase 3 – Analysis using simulation tools.
1. Observation – looking at where the flow is interrupted
2. Basic data – Stand Along Throughput (SAT), Efficiency
3. Analysis – Using C-More or Simulation
4. Prediction – Determine where the bottlenecks will be in the future.
There are a few algorithms that can be used with simulation to help predict the throughput improvement that can be generated by improving each workstation. I’ll be glad to talk to people off line about how to set this up. For now, let’s assume one of those algorithms has been run, and the bottleneck has identified – and quantified, often an important difference between methods. The quantified improvement in JPH can be used in financial decisions – is it more cost effective to run overtime, for example, or spend money to improve the performance of the machine? If the goal of the company is to make money, then quantifying the financial impact of improving the bottleneck ties in very nicely, making it difficult to make sub-optimal decisions.
How is an Analysis using simulation better than the Basic Data method? The basic reason is demonstrated in the dice game, where the student concludes that variation plays a role in throughput. With the average role being 3.5, many students assume that, after 10 rounds, 35 parts will appear at the end of the production line. As Alex found out in The Goal, it never does. The “good” variation (when a player roles a six) does not make up for time when the student experiences “bad” variation, when the student roles a one), since the incoming buffer usually does not have six parts in it when the six is rolled.
With that said, I have found the following conditions influence the location of the bottleneck:
Downtime duration – When looking at two workstations that both have low SAT’s, we may find that they have different downtime characteristics – one fails very often for a very short period of time versus one that does not fail very often, but when it does, it fails for a long time. In most cases, the later will be the bottleneck, since the buffers can cope with the long downtimes.
Variation in part cycle time/model mix – Two workstations have the same low SAT, but one has a long cycle time for part A, and a short one for B. The second workstation has about the same cycle time for each part. The first is more likely to the bottleneck, especially if the model mix results in a large number of part A’s being send through the workstation in a short amount of time. As with downtime, the buffers may not be able to recover from the long cycle times, and short cycle times end up causing the bottleneck to be blocked.
Location – Two workstations may have the same low SAT in a production system, but the one that is closest to the end of the line will mostly likely be the bottleneck, especially if large buffers are present throughout the system. The same is true is one station A has more buffer around it than station B. The larger buffer may absorb variation, thus making station B the bottleneck.
Are these differences worth devoting the time and effort to install a simulation methodology? My answer is to let the system tell you that. Use Observation until it no longer is dependable. Then install data collection, based upon the benefits of finding the bottlenecks more consistently. The same is true at the simulation phase – buy the simulation system when the Basic Data method proves to be inadequate, and then with a business case. Many companies, with simple production systems, will never get there, but other larger, more complex systems, will. As with most cases, default towards action – it’s usually better to improve a workstation than to get trapped in “analysis” paralysis. Improving a non-bottleneck workstation may not improve throughput, but will help make the bottleneck more obvious the next time around.
What has changed that allow simulation to be more viable? At one time, the time and cost to construct a simulation model was prohibitive in production, and was usually only used in the designing of a new system. With the maturity of simulation products, however, this has changed. Current simulation packages have stencils to allow for rapid model development of plants that have similar operations. “Drag and drop” tools allow for rapid model creation. Finally, connectivity to data bases allows for easy assess to data. Once set up and tied to a production database, they have the potential of doing “near real-time” analysis, something that was impossible just a few years ago.
The speed of today’s computers also has shortened the analysis time to predict throughput. A few years ago, computers toiled for hours just to a complete one analysis. Now, they can be done in minutes, and with C-More, in seconds. Thus, the time from end of shift to the posting the bottleneck report on the web is just minutes, allowing shift management to make decisions to improve throughput before they go home for the day.
Thus, simulation has become a viable tool for the plant floor, as long as it stands upon the bedrock for analysis – accurate and timely data.
