Why Isn't Capacity Planning Enough for Good On-Time Delivery?

Example: the electrician in the fourth work step

Welcome to Circonomit Labs. Today is about planning: how do we plan well, not only based on capacities but also on the time dependencies at the site? I'll show this using the target KPI on-time delivery and the question: outsourcing or in-house production?

Our goal is to increase on-time delivery, or at least to meet it. Depending on the industry, missing on-time delivery costs money, and we want to avoid that. As an example we take a typical contract manufacturer of various metal and electronic components. Production consists of five work steps, and in the fourth step we need an electrician.

Suppose this electrician handles 10 orders per hour and works 8 hours in his shift. In an ideal world he could process 80 orders. Realistically there are breaks, setup times and walking distances, so we plan with 70 orders.

What planning tools capture and what reality does

Then an order comes in, sales gives out the delivery date, and we manage to produce these orders. Planning tools often work well here, depending on how complete they are: they capture capacities, meaning how long an order takes and how many orders can be produced per hour.

And then reality kicks in. This is where we combine simple planning with business driver trees. Planning is a great start but not enough, because reality already falls off its chair laughing on Monday morning at 10:17 am, when seven things have changed since planning on Friday.

So we build in: whether on-time delivery is met depends heavily on whether the work steps run in the correct time sequence. That's not rocket science. And then we ask further: what is the effect if the electrician at this work step calls in sick for a day? Or if there are so few electricians anyway that they are chronically our bottleneck? How do we then act best from a company perspective, for on-time delivery and ultimately for the profitability of the orders?

Outsourcing or in-house: calculating the tipping point

Now it gets exciting. In the ideal state, nobody is sick and orders flow through, we have enough capacity and even a buffer. Then it makes no sense to give work to an extended workbench, which is much more expensive.

But if we know that the electrician is absent on average in two of 20 to 25 shifts a month, whether because of winter, cold season or something else, we need a total cost calculation: what does it cost if orders are delayed in two out of 25 shifts? And where is the tipping point? For example: what does it cost if we put 5% into inventory via the extended workbench? The costs of outsourced production and the inventory costs go up. But in the optimized state, in which we simulate reality properly and factor in sick days, for instance because more people are sick in winter than in summer, we can counteract well ad hoc. We actually have this information but usually ignore it in planning.

Planning with real influencing factors

The key message: planning is not about filling the plan as well as possible once based on capacities. It's about asking: which real conditions keep changing in my plant and are highly likely to recur? We factor those in and then know: what is my optimal state in January, what in the second week of July? That way, depending on the disruptions, from war to flu, we continuously find the optimal state.

That's why we love optimizers: every decision is the question of the optimal state given what I can change and what I can't. In contract manufacturing, outsourcing is not a simple yes or no. The question is: where am I on the timeline right now, which disruptions will affect my company in the coming days or occur randomly, and how do I steer well under them?

Are we a planning tool? No, we are a decision tool: the right hand in daily operations between site management and controlling, always with an eye on the company KPIs. We can build on an existing planning tool. It's about getting the 20% that matter when planning falls off its chair laughing again.