Evaluating Advanced Planning and Scheduling Systems
Recently I was working with a supply chain client where the company was seeking to implement manufacturing systems that included an advanced planning and scheduling system (APS). I asked them: “What are your chief business goals in seeking to implement and use [specific ERP]’s Advanced Manufacturing capabilities in your business?”
Two of the crucial factors they mentioned in their response were to “gain insight into [their] business in regard to capacity… [and] ship dates.” The mentioned, specifically, challenges they were currently facing in “capacity planning” and the “scheduling of work orders.”
In light of this request and their business goals, I proceeded to ask them some important questions, such as:
- What determines the quantity of units to be produced on a work order?
- What determines when a work order is to be released for production?
- How are manufacturing batch sizes determined, if different from the quantity on the work order?
- Are your manufacturing times in control (where in-control means that variation within the process is attributable only to random events; and not in control means variations are large and are not clustered around a statistical mean)?
- Do you know (or have you calculated) your typical manufacturing (work center) cycle times for all production steps to be included in your scheduling? These would include (where applicable):
a. Queue times – the average time a production unit (or job) spends waiting for processing at a work center, or to be moved to the next work center
b. Setup times – the average time a production unit (or job) spends waiting for the work center to be set up for processing the unit (or job)
c. Move times – the average time a production unit (or job) spends being moved from the previous work center to the current work center in a routing
d. Process times – the average time a production unit (or job) spends actually being processed or handled by the work center
e. Wait times – including
i. Wait-to-batch time – the average time a production unit (or job) spends waiting to form a batch for either simultaneous processing or moving
ii. Wait-in-batch time – the average time a production unit (or job) spends waiting for its actual processing
iii. Wait-to-match time – the average time a production unit (or job) spends waiting for other components for an assembly operation
As you can see from the accompanying figure, traditional advanced planning and scheduling systems rely on these data in order to produce a schedule.
The problem is, if operations are not in statistical control (see number 4 above), even known or calculated averages will be of little value. And, while it is not unusual for us to encounter clients that have a pretty good handle on average set-up times and run-times (processing times) for various manufacturing operations, they almost never have any information on queue times, move times, or wait times in their operations.
This is a huge problem if their goal is to produce a schedule out of their system by which they might hope to actually drive the production floor—or calculate estimated shipping dates (as our client wanted to do).
What you get, without accurate data for these crucial time components of production is GIGO – garbage-in, garbage-out.
APS: Garbage-in = Garbage Out
The Advanced Planning and Scheduling module will dutifully follow the programming rules and produce a schedule that is precise to the minute (see figure above). However, it will be precisely wrong and can never be executed upon.
A better way
There is a better way. I told this client that what really controls their operations—and determines delivery dates—is(are) their bottleneck(s) or constraint(s).
A relatively small data-set could help them determine schedules much more simply. That data-set begins with (1) knowing the number of hours on the constraint(s) for each product manufactured and (2) knowing the current load on the constraint(s).
I told them, while there are a number of solutions that could help them start a POOGI (process of ongoing improvement) for their manufacturing and supply chain operations, perhaps they should start by looking at a product such as DBR+. Such demand-driven solutions will provide them with the tools they need for rapid ROI (return on investment) and control they would find very difficult—perhaps, impossible—to achieve using traditional APS methodologies.
The principles incorporated in demand-driven solutions such as DBR+ make decision-making and priority-setting much simpler and more effective and require a far less complex set of data inputs to be effective.
Manufacturer ROI Case Study
Manufacturers use Adaptive Insights Adaptive Planning for forecasting, planning, and accounting. Read how one medical device manufacturer delivered additional profits to their bottom-line.
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