As product complexity has grown, so has the challenge to manage the data behind it. With thousands of variants, it’s a lot of work to manage the data on a part number by part basis. And if products are configured, the work content may vary by the specific options ordered or the combination of those options.
Poor routing data has been used as a big reason why a company can’t go lean. But it shouldn’t be. In fact, the orientation of lean can provide a lot of relief for poor routing data. Here are some strategies that have worked.
- Use averages except where variation is high
Traditional routings that require you to maintain the data on an item by item basis don’t lend themselves to using averages. But if the difference between items is only a few seconds, it may be perfectly fine to use an average.
- Focus only on the bottleneck operations
The capacity at the bottleneck is the limit to your overall capacity. Manage the rest of the processes with averages that are “close enough” but get into the details at the bottleneck where wringing every last drop of performance will have a bottom-line impact.
- Go to the gemba and ask the operators
Don’t get caught up with industrial engineers with stopwatches to calculate lowest repeatable cycle times. The operators know how long stuff takes to do, so ask them. We were working in a shop with a machining cell where the routings were 10 years out of date. Starting from a quick value stream map, we walked the floor and asked the operators about how long it took, and timed one or two cycles just to confirm. What we got back within 3-4 hours was a reasonable expression of time to build and a bunch of insights into the problems as well.
- Use a simpler lean repository than an ERP
Routing data normally sits in an ERP which forces you to manage part by part instead of with more aggregate techniques. Areteium from MCA Connect is a lean repository that allows you to manage by average, by average with exceptions, by individual item, or even by detail based on specific configuration.
- Start with what you know and refine when you must
Using these strategies, don’t let yourself be hamstrung. Start with what you know, get it quickly into a flexible lean repository, then refine when you must. But get started… lean brings a refreshing bias for action. Don’t let poor data quality stop you, get out there, engage the operators, and use the right tool to produce results quickly that are “close enough.”