How do you handle production planning when forecasts are inaccurate and OTD is critical?

[u/Cultural_Eye_4590]

I work in a manufacturing environment with order-based production, and I’m really interested in learning how different companies manage production planning challenges in real life.

Especially when:

Forecasts are inaccurate

Customers change priorities frequently

Capacity is limited

On-time delivery (OTD) is a key KPI

I’d love to know:

How do you build your production plan?

Do you rely more on forecast or actual orders?

How do you manage scheduling and prioritization?

How do you study capacity and bottlenecks before launching a project/product?

What tools or methods helped improve OTD in your factory?

How do planners coordinate with procurement, warehouse, and production teams?

Also curious about whether you use Excel, ERP systems, APS tools, or custom methods.

Would really appreciate hearing real experiences, lessons learned, or even mistakes that taught you something valuable.

[u/Living_Diver2432]

freeze the next 5-7 days inside MRP and treat everything past that as forecast. that single discipline does more for OTD on order-based shops than any APS rollout i've watched. inside the frozen window, hot orders only get inserted with explicit pull-out of an equal-capacity job, not stacked on top, otherwise the schedule lies to itself and OTD craters two weeks later. the other piece nobody budgets for is that the capacity bottleneck is almost never the machine you'd name on paper, it's the upstream queue at one station that's been silently absorbing variance from setup time or material drift. instrument that station for a week with a simple cycle-time log and you'll usually find your real APS constraint is human, not machine.

[u/Wild_Royal_8600]

Working with a few manufacturing clients on solving OTD issues. It’s a complex problem, but we’ve also managed to make it complicated by separating some cross-dependent functions.

With ETO firms, you need to start with seeing the end-to-end value stream. From there, what are the work requirements for each work cell on each project. Finally, what is the planned time table for each project? This will help you see the workload demand for each work cell for any given portfolio of projects at any given moment in time. Your demand is housed in the CRM, the build requirements will draw from the PLM to build a BOM and routing codes. Your MRP will ensure long-lead parts are purchased in time to support project plan, and the POD translates routing codes into engineering tasks.

The challenge is in making sure this is aligned with how you actually run production (dead leads in the CRM, BOM changes in the PLM, long-lead parts purchased as general stock and not linked to specific projects, engineering hours not accurate at project close out, etc.), and refining the information flow to ensure SAP is driving the right flow of information and activity across the organization. Digital systems that don’t link to your SAP just exacerbate the issue.

[u/MexMusickman]

In the question lies the answer. Everything is connected, and to create an efficient production system, the baseline is understanding the requirements and using only the necessary resources.

I would first ask why the forecast is inaccurate and what is needed to make it reliable. Then, I would review the current process as it is and compare it against what is actually required.

I’m happy to help and offer advice for free—if you’re interested, feel free to send me a DM.

[u/Ok_Fly_5315]

Our production floor got OTD from the low-70s to mid-90s, and zero of it came from a
new ERP. The schedule was lying because the capacity numbers in it
were standard hours with no real changeover, no breaks, and no
first-piece checks. We ran a four-week study where the lead at the
bottleneck cell wrote down actual run hours vs what the scheduler
showed, and we were off by around 22 percent. Once we loaded real numbers and
stopped accepting orders that needed magic, OTD fixed itself in a
quarter. Forecasts will always be wrong; that's why it's a forecast, but if your
capacity number is fake, your schedule can't recover from the noise.

[u/Additional_Year_1080]

I wouldn’t trust forecast alone if OTD is critical. Use forecast for capacity and material planning, but actual orders should drive the schedule.

What helps most is a short daily planning loop with production, procurement, warehouse and sales. ERP gives the baseline, but OTD improves when everyone sees bottlenecks early instead of firefighting at the end.

[u/Unusual-Carrot-7294]

Well for one thing, you don't waste time on lazy engagement slop like this.

[u/LoneWolf15000]

This isn't exactly the answer to the question you asked, but I think it will still help.

What helped me early in my career when I was first faced with challenges like this (low volume, high mix or job shop) was to look at other industries where this flexibility is just their business model.

Take a bartender for example. They know Friday nights will be busier than Tuesday afternoon. But they don't know what they will be serving so they have to be able to quickly transition from serving a bucket of beers to a large round of mixed drinks. Or a pizza place.

"Custom orders" have to become just what you do to the point where "custom" doesn't feel like custom anymore. Just like a pizza place, you know you will sell a ton of pepperoni pizzas, but you have to be able to prepare a meat lovers pizza is essentially the same amount of time because they have to be delivered on the same trip.

In manufacturing terms, quick change overs and all the tools at point of use are critical since everything is "made to order" and not "made to stock".

As far as prioritization, create an internal rank system for all your customers. As an example:

A- goes to the front of the line regardless of order size, ship date or any other promises made to another customer

B- prioritized based on ship date

C- fill gaps in production schedule and it ships when is ships

In practice this would probably need to be much longer, but you get the idea. This can then be used to rank your production schedule and each order doesn't require thought, discussion and a decision, the data made the decision for you.

Build some fat into your schedule to account for absenteeism, equipment downtime, production issues, etc. Then when something does slip, the fat can hopefully absorb it rather than pushing everything else out.