Numerous groups and companies have grown to the point of having plants in different regions, with huge differences in their characteristics (labor costs, technologies, level of automation, among others). These companies share a complex decision problem: considering all their production units and their particularities, the different markets served and the full range of products, how to allocate and distribute their production? The nomenclature for this problem tends to vary considerably, but in general, it is known as production network optimization, product allocation to plants, or even as multi-plant production planning.
The list of factors impacting this decision is long and varies between different contexts. With regard to costs, it usually includes logistics, arising from the distribution of products to consumer markets and the transfer of intermediate products between plants, the production costs at each plant - which mainly comprise material and labor costs -, in addition to other costs related to the use of resources, and, when applicable, the outsourcing of part of the production. Aspects such as existing technologies in each plant, productivity, yield, setup time, and maintenance cost can also guide the choice on how to allocate your production.
With such complexity, the benefit of using advanced analytics and mathematical models to support this decision is clear. The application of an adequate optimization model, aiming at the minimization of the overall cost and considering the numerous constraints and all the factors mentioned above, can turn the diversity in the production network into a real competitive advantage when it comes to strategic planning. At Cassotis, we encountered this situation in the ceramics sector, but there is no doubt that many other industries could also gain from optimal production planning.
With an appropriate and customized mathematical model, it is possible to guarantee more efficient usage of the production capacity from different factories. Also, the use of resources, whether human, electricity, energy or raw materials, would be optimized, considering their costs and availability. Such model would also enable the comparison between internal and external production, determining whether part of the production should be potentially outsourced.
The results obtained from a more efficient allocation may vary depending on the context and sectors. However, the efficiency gain can usually be translated into a reduction in variable production costs of up to 15%, based on Cassotis’ experience.
Another key aspect is the time spent to perform the analysis of different scenarios. In times of crisis or opportunity, it may be necessary to review the entire planning, and this may include factors such as changing the production profile and reducing labor or increasing shifts, for example. Through scenario analysis via the optimization model, it is possible to achieve a quick response in this type of situation. A good example is the Covid-19 pandemic, in which large companies needed to review their entire production strategy and planning. (1)
As we are talking about overall allocation, technology transfer, or hiring more people, this planning decision falls within the so-called strategic level, in which decisions are made less frequently, often annually. At this level, any decision should be made through what-if analyses, considering scenarios that change the configuration of the manufacturing network.
Considering the macroeconomic movements and their cycles, as well as the technological and political evolution of certain regions, part of this type of planning is to analyze the transfer of production lines from one plant to another. For example, due to variations in production costs, such as raw materials and labor, logistical opportunities, and changes in the demand profile, it may become less costly to transfer the entire production technology from one plant to another, taking with it equipment, tools, and expertise to start manufacturing the same product line in another factory. Another, even more strategic, decision that can be made using this same model is the choice of where to locate a new plant.
Therefore, it is evident that this type of problem, which is quite common among large companies, poses a complex decision-making challenge and carries with it a huge opportunity for gains when using mathematical modeling and algorithms. Whoever lets this opportunity pass may end up falling behind!
Author: Cassiano Lima - Senior Consultant at Cassotis Consulting
Co-author: Emmanuel Marchal - Managing Partner at Cassotis Consulting
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