Introduction
The Production Planning optimizer (or PP optimizer) integrates purchasing, manufacturing, and distribution so that comprehensive tactical planning and sourcing decisions can be supported and implemented on the basis of a single, global consistent model.
The PP optimizer is one of the available planning heuristics that you can execute for a selection of your products.
The above figure shows a possible positioning of PPO within a planning scenario with PP/DS.
The PP optimizer offers cost-based planning which means that it searches through all feasible plans to find the most cost-effective solution (in terms of total costs).
Total cost covers the following aspects:
- Production, procurement, storage, and transportation costs
- Costs for increasing the production capacity
- Penalties for violating (falling below) the safety stock level
- Late delivery penalties
The PP optimizer uses advanced optimization techniques, based on constraints and penalties, to the plan product flow along the supply chain. The result are optimal purchasing, production, and distribution decisions; reduced order fulfillment times and inventory levels; and improved customer service. Starting from a demand plan, the optimizer determines a permissible short- to medium-term plan for fulfilling the confirmed and estimated sales volumes. This plan covers both the quantities that must be transported between two locations (for example, from a producing location to a distribution center), and the quantities to be produced and procured. When making a recommendation, the PP optimizer compares all logistical activities to the available capacity.
PPO planning makes the decisions on tactical planning and source of supply determination. The strengths of PPO planning are: The selection of the source of supply taking into account the costs, and the determination of the approximate production date taking into account both the procurement costs and the storage costs.
The result of the PPO planning is the answer to "What is produced, where and when?". The answer to "when" cannot be more precise than a planning bucket, and it does to some extend the sequence-dependent setup activity into account.
The PPO determines the following:
- The production quantities, the procurement quantities, the stock transfer quantities for each product and period
- The selection of the resources and the plans for the production
- The selection of the plants, the warehouses, the suppliers, and the transportation lanes
As stated, the PPO works on the basis of periods. The sequence of orders within a planning period is NOT defined.
As a result of this, limitations of the PPO approach are:
- The optimization run results do not include pegging orders back to the original individual requirements because requirements are bucketed.
- Since orders are not pegged back to the individual requirements, the PP optimizer does not support order-based planning. After the optimization run, it is not possible to determine information about links between specific planned orders and original sales orders.
- In the event of a capacity overload, the PP optimizer, depending on the system settings, either does not cover the requirements on time or increases the capacity based on a penalty cost calculation. Or does not cover the requirements at all.
Master Data and Profiles
In order to define the constraints for the optimization model that PPO uses, it is necessary to maintain additional master data in the system. Profiles with configuration (or: outcome influencing) settings are also needed to determine the way the optimizer will work.
The PP optimizer tries to minimize costs. These costs could either be associated with products or resources. For example: costs that are incurred when an extra shift needs to be scheduled for a resource. This information is crucial to the successful implementation and accurate output of the optimization model that PPO uses.
The following are a few examples of types of costs that exist in the context of the PP optimizer:
- Production costs
- Transportation costs
- External procurement costs
- Storage costs
- Penalties for shelf life violations
- Penalties for safety stock violations
- Penalties for late deliveries
Most of these costs are maintained for the relevant master data objects for the PPO run.
The concept of bucket capacities in PPO involves dividing the planning horizon into discrete time intervals, commonly referred to as buckets. Each bucket represents a specific period, such as a day, week, or month, depending on the planning requirements of the organization. You can define a profile, called a time bucket profile, that consists of a set of time units (such as years, months, weeks, and so on) that are used to generate time buckets for the PP optimizer run. Time buckets are time durations in a bucketed format within which the planning is carried out.
The optimizer utilizes linear programming to simultaneously consider all relevant conditions for the planning problem in an optimal solution. As more constraints are activated, the optimization problem becomes more complex, requiring more time to solve. Generally, the optimization should be executed as a background job.
The optimization problem can be solved continuously or discretely. The optimizer compares alternative solutions based on the costs incurred and determines the most cost-effective feasible solution, based on the constraints and objective function defined in the system. The prioritization of demands is done through penalty costs.
In the so-called optimizer profile, you determine the optimization method to be used during the optimization run (linear or discrete optimization) and the constraints to be considered.
End Result
As previously stated, the PPO is specifically designed for bucket-based planning. This means that it does NOT handle sequencing. To obtain a finite sequence-optimized production plan, you can execute a DS optimization run after the PPO optimization run. However, it is important to note that the DS optimizer considers the production plan generated by the PPO, including the allocation of quantities to resources and buckets, and does not relocate orders from one bucket to another once these orders have been created. To address this challenge, a special optimization profile is available.
So you can either execute a standalone DS optimization run after the PPO run, or you can execute a combined PPO and DS optimization run, leading to other results.