Simulation - Finding the Optimum in Production Scheduling

Published on March 13th, 2020

The state of the market is in constant flux. Big changes are currently occurring in the automotive industry, where new technology is replacing the outdated; products and processes are affected by global innovation.

The main example is digitalization – Industry 4.0, which poses a huge challenge to production scheduling. As suppliers reduce capacities in some areas, they boost them in others – or develop completely new processes. Digitalizing the business is necessary, however due to the challenges it poses, it almost always comes to a premature halt.


The Unseen Problem

The prevailing problem schedulers face is decades-old: the problem of short-term changes, so-called “firefighter activities.” The schedule constantly needs to be revised, due to customer-requested changes to order dates or quantities, late suppliers, or machine breakdowns. No one knows what must be done in any one specific case, because Excel tables and on-the-fly mental calculations cannot address the complexity of these problems.

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By simulating the entire supply chain, the short and long-term effects of schedule adjustments are immediately recognizable.


Simulation is Calculating the Optimum

The ability to simulate is an integral element of effective planning and scheduling. Through simulation, changes to the schedule and the resulting impact on the overall schedule are made clear. The scheduler simulates different scenarios such as overtime or downtime for a certain machine, weekend work, and others. Then, the effects of each scenario are checked, so that the best possible alternative scenario can be selected and the important business targets (such as on-time delivery) are met.

This is why a modern scheduling system is essential to stay competitive, since it is capable of this simulation. Important functions of such a system include the following:

  • Realistic simulations: This is possible because all product properties, process rules, process restrictions, customer priority, resource priority, planning rules, and scheduling restrictions become mapped in the software.
  • Visibility: Potential problems are made visible weeks or months in advance, allowing for appropriate measures to be taken in time.
  • Comparison: What-if analyses allows for comparison of various scenarios.
  • Short-term scheduling (two to six weeks):
    • Feasibility of each customer order can be precisely checked.
    • Exact delivery times can be calculated.
    • In the case of interruptions or other problems, an alternative plan can be created immediately.
  • Middle-term planning and long-term planning (two to 36 months):
    • Capacity load and availability are checked.
    • New shift plans or adjusted number of workers can be simulated and the effects of different workforce schedules analyzed.
    • Investment in new machines for the bottleneck can be simulated, while tracking looking at the effects of capacity load and availability, production lead time, inventory changes, manufacturing costs, ROI, etc.
    • Various business scenarios and prognoses can be simulated, to serve as a basis for making profitable management decisions.
  • Kaizen planning: Simulate impacts of Kaizen measures, whose effects on lead time and inventory can then be understood.
  • Speed: calculate entire scenarios within minutes.

“Future KPI”—including delivery reliability, lead times, inventory, manufacturing costs, etc. enable you to choose the best scenario among many.


industrial manufacturing needs precise planning

Investing in an orchestrated end-to-end scheduling system means a significant increase in efficiency for every production company.

 

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