What was considered a goal only for the absolute minimalists in school is the highest aim in capacity planning for a logistics operation. Having enough capacity to meet the delivery promise, the service level, but also no idle capacity. From economical perspective, this is certainly desirable, but what is the effect on the process participants?
The customer is satisfied as they receive the goods as expected.
For the motivated employees, it is satisfying that there is enough work to do. For the less motivated, the shift passes by more quickly. Good planning also has a positive effect on all employees, as it eliminates the stress triggers of overload or unplanned overtime. In times of lack of qualified resources, the available resources are optimally utilized.
Capacity planning in practice
Essentially, it involves “only” estimating the necessary effort for the volume to be processed and planning and providing the required capacity (employees and operational resources). In long-term planning, the volume estimation is usually accompanied by greater uncertainty, as only a few reliable pieces of information are available. However, as we get closer to the execution time, more information becomes available. This also means that the planning needs to be updated with each new piece of information. Initially, one can only count on seasonality, trends, and a few known special influences, but by the end, all order data is already in the system and the efforts can be estimated with considerable accuracy. Thus, various computational models are needed that take into account the information available at the time.
The efficiency mentioned in a previous post is also considered. In a warehouse close to the capacity limit, inefficiencies due to congestion and blockages will occur, while at low utilization, the workload is not sufficient for task interchanges, and restrictions can be expected as well.
Even in the worst case, when it is foreseeable that the available capacity will not be sufficient for the current workload despite all planning, it is better to recognize this as early as possible. At least it ensures that the most important orders are processed through prioritization. We can try to influence customer expectations or perhaps a pragmatic solution can be found. After all, logisticians are solution-oriented.
Based on the flumiq 3P model, various forecasting models can be created that optimally consider the information available at the respective time.