Optimizing Production Planning with APS Systems under Constraints

This is often the promise of APS systems: to take into account constraints – capacity, material supply, grouping campaigns, and optimizing costs.

AUTOMATICALLY!

Depending on the capacity and shift pattern of each piece of equipment, depending on the day we should receive that bloody critical missing component, we determine how we will be able to start the production, synchronize the different operations of the routing, and eventually deliver our customer.

Of course, we have to optimize the OEE, reduce material losses, and make the best use of our direct workforce.

In one of my previous lives, in a German industrial group, the planning of the operations had been entrusted to a well-known APS and its obscure “optimizer”.

This project had to account for every constraint. They were encoded by a project team—business rules, synchronization rules, all logical considerations. Nothing beyond the capabilities of a powerful computer.

- Yet lead times kept inflating.
- No one really understood why planning worked that way.
- In the end, everything landed back in Excel.

You’ve seen it before—the small grain of sand that jams the gear. Everyday operations are full of them. The so-called optimizer couldn’t handle it, and users lost trust.

Choose your constraints

Not everything is constrained. Of course, every means of production and every individual has a capacity per day. But you have some driving constraints – over a range of time. These are only the assets you must monitor at a finite capacity. Don’t try to optimize everything, it’s a waste of time, you’ll skid on the next sandy turn.

The more you define constrained equipment on your flows, along complex routings, the more you will elongate your lead times, complicate your planning, and the more you risk losing the understanding of your real hard points.

That goes for supplies. You need all the components in the BOM to make your products. Secure the vast majority with a replenishment process (stock buffers) that allows you to reduce the background noise, so you can focus on those few components where there is a real problem.

Not everything deserves the teams’ constant attention. Pick your battles.

Dampening between constraints

Between the crankset and the hub of your rear wheel, the chain of your bicycle passes through a spring-mounted derailleur, which dampens jerks and allows a smooth drive.

In an automated production line with several machines in sequence, accumulation buffers – flexible conveyors, stackers, etc. – are used. If the upstream machine stops or slows down, the downstream machine maintains a certain amount of autonomy. If the buffer drops below a certain level, a signal – an Andon – is triggered so that the support team can intervene and solve the problem before it spreads.

Between your constraints, you must establish amortization mechanisms. These are either stocks, queues or capacities that can be activated from time to time. When these buffers fall below thresholds, you trigger actions, by exception.

Automate human decision support

With a judicious selection of constraints and clearly defined buffers, you define an operating model. This allows you to automate a large part of the control, but more importantly, it provides our planners with the relevant signals to take action. It’s not just about automating the process with an obscure “optimizer”. It’s about automating everything that can be automated, to give humans who are far more creative than our software the information they need to better meet their customers’ expectations.

For further exploration on this topic, consider watching our webinar on managing in a supply-constrained environment: