The answer from Jean-Pierre Goulet, “It’s easy, we used Factory Physics science to achieve the goal!”
This case study is about a manufacturer of kitchen cabinets (we’ll call them “Cabinets Inc.”). Their annual sales average US $3M. Their earnings before taxes for their last 3 years were, 1.2% to 1.5% as a percent of revenue. Jean-Pierre owns Cimetech International Inc. (www.cimetech.com), a consulting company in Quebec, Canada. He has a long history of applying Factory Physics® concepts in practice having read the books and attended a Factory Physics seminar in 2008. He works with clients to increase capacity, earnings before taxes, and cash flow while also reducing lead time.
Our thanks to Jean-Pierre for the following guest blog demonstrating how Factory Physics operations science works in practice to help managers manage better. Jean-Pierre’s example is with a small company. Factory Physics concepts are also applied with the same kind of success at Fortune 100 companies. That’s the beauty of the operations science approach, it applies in every company and provides a foundation for correct application of all continuous improvement tools. Let us know if you have a similar story you would like to share. If so, send an email to firstname.lastname@example.org.
For precision in description, we describe cycle time as the time it takes a job to complete a routing. For Cabinets Inc., the primary routing is from saw through final packaging. Cycle time is a random variable. Lead time is a management policy that is entered in the ERP/MRP system and used by the IT system to calculate promise dates. For on-time delivery to be 100%, cycle time must be less than or equal to lead time.
On to the case study:
Cabinets Inc.’s typical clients are home owners looking for new kitchen cabinets, or contractors requiring sets of 25 to 75 kitchen cabinet assemblies for multi-floor apartment buildings, Additionally, Cabinets Inc. does custom work for restaurants, hospitals and other public buildings.
Base case performance:
That was the picture we had in front of us, including a very large amount of WIP all over the place. Management was looking to increase the size of the actual building or to move to a larger building.
In order to improve the process, we needed to measure it to make sure we would be efficient about applying our improvement efforts. We were completely familiar with the tools of Lean, Six Sigma and Theory of Constraints. Applying the Factory Physics operations science analysis enabled us to quickly determine which of those tools, or others, to use to get the most benefit from our efforts. We applied three primary Factory Physics concepts as described in Factory Physics (Waveland Press) and Factory Physics for Managers (McGraw-Hill).
1) Little’s Law: WIP = Throughput x Cycle Time
To reduce the Cycle Time in Queue (CTq = VUT), we need to reduce the WIP inventory on the floor. WIP is directly related to variability (V). In order to reduce WIP properly, we needed to measure it. To do so, we provided our client with an Excel template that needed to be completed manually at the end of each production day. Data collected included: WIP level per work station, working hours and throughput per day. Over the long run (steady state), we got a good picture of WIP, Cycle Time, Throughput and location of the bottleneck.
2) Variability Law: the high level of WIP and low throughput were mainly caused by variability in the processes and high level of utilization (U) at the bottleneck station. In looking at the performance of the bottleneck, weak performance was mainly due to maintenance problems on motors and drives. All this reduced the throughput.
3) Production Flow Graph (PFG) analysis: based on the accumulated data we collected, we were able to determine quantitatively what the future state performance would be. By creating a production flow graph, we were able to predict what kind of performance could be achieved. By targeting high value variability reduction, we decreased the amount of WIP required, increased throughput and reduced lead time very quickly. The initial and final state Production Flow Graphs are displayed here:
Results (over the 11 weeks following implementation):
As a result of the efforts, earnings before taxes (EBT) are now 10% (after one full year) and during peak months EBT has been at 20%. Management believes that they don’t need to increase the size of their building or to move to a new building until they reach $6M sales per year.