Rough-cut tactical production plans are often not feasible in the short term, which results in firefighting and manual interventions. The implementation of sequence-dependent changeover times is an answer to this challenge.

Production is overloaded and already working overtime. A smoothly running serial production has turned into an overwhelmingly complex process due to ever-decreasing batch sizes. The shop floor is trying hard to cope with an increasing product variance and the daily demand variability. Meeting due dates is only made possible by a few highly engaged employees chasing every single order and delivery. That’s the unfavorable reality of many production environments today. So, how can this constant firefighting be stopped?

Are buffers the cure-all?

The situation described above is comparable to the situation on our clogged streets. You know that on your daily commute you have to expect traffic jams. So, what’s your natural response? You start your journey earlier to make sure you still arrive on time. In supply chain management we call that a buffer. And there are two types of buffers: inventory buffers and time buffers.

While the introduction of buffers sounds like a logical thing to do – and admittedly eases the situation at least slightly – there are also downsides to buffers. They increase the throughput time in production, they tie up working capital, they introduce another layer of complexity. And if not managed carefully, the introduction of buffers leads to a bi-modal distribution of inventories, in which you end up with too much of the wrong and too little of the right stuff.

The root cause

In most cases the problem starts already in long-term production planning, where a rough-cut planning process is used to simplify the task. But this simplification very often turns out to be an oversimplification, planning on aggregated levels and disregarding optimized production sequences. Across industries including pharma, chemicals, consumer goods and manufacturing this oversimplification leads to the same result: the tactical production plan proves to be infeasible in the short term, and manual intervention is required.

Sequence-oriented changeover times

Improvements in the quality of production plans can be easily achieved by a more accurate representation of lead times in the long-term plan. And a key component to that is the consideration of sequence- dependent changeover times. In companies using SAP solutions for production planning processes (PP/DS on SAP S/4HANA) for instance, such sequence-dependent changeover times can easily be considered in production planning by using a so-called setup matrix.

A setup matrix is often built based on a product’s characteristics and is used to derive the required changeover time for a transition from an order for a product with one set of characteristics to an order for a product with another set of characteristics. Almost all production processes have examples of product characteristics driving changeover times. In pharma it’s the change of an active ingredient. In extrusion processes it’s often the color of the plastic. For beverages it’s the ingredients in the drinks.

Real-world results

At CAMELOT, we are strong advocates of adopting the setup matrix to increase the quality of planning. In a recent project we have been able to detect an overestimation of monthly capacity utilization by 27% comparing the rough-cut plans of a client to the result after introducing the setup matrix. It is the first step into the right direction, and a key to improving the quality of production plans. If you’re interested in learning more about how to improve your production planning outcome, keep following us on this blog.


2 Responses to “The key to better planning results”

  1. Aniket

    Great Article. Is it based under the assumption that lead times are known and the activities planned will be executed in the same order? What if the plan changes, will there be an overlap?

    • Jan-Luca Jaborek

      Thanks for your comment. You’re right, a key assumption in planning is that the underlying parameters are known. This includes lead times, changeover times, capacities, machine run rates, and further. Of course in the short term there are often changes. But this is only another reason why it is critical to model such sequence dependent changeover times. When plans have to be adjusted in order to react to short-term changes it is important to understand the exact implications on the capacity utilization. Otherwise it would not be possible to evaluate the feasibility of a production plan.

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