In the many SAP Advanced Planning & Optimisation (APO) Supply Network Planning (SNP) implementations that Olivehorse has been involved with over the years, we have identified a common mid-term production planning issue – especially in the consumer products and food industries.
The standard APO planning solution will plan to demand, however, such a plan will usually not be efficient to produce due to cost of changeovers and reduced capacity utilisation etc. There is therefore, a requirement to group production orders of different SKUs together into the same period to ensure that a more feasible and efficient plan is created in the mid-term. The standard solution in APO, whether it be Heuristics, Capable-to-Match or Optimiser, does not support the concept of Group Planning.
The rules that need to be adhered to for this grouping can be quite varied and is usually dependent on upstream or downstream constraints. In addition, planning cycles and production wheel rules are also often required to be introduced to ensure stable production processes and appropriate goods inventory levels whilst meeting customer service levels.
To meet this requirement, and the many variations that go with, Olivehorse has developed an APO SNP Optimiser enhancement that enables the automatic grouping of planned production orders in the mid-term plan. It allows grouping according to any attribute or characteristic as defined in the product master, as well as considering upstream and/or downstream constraints and allows for periodic planning cycles.
The principle behind the Group Planning concept is that a demand on any of the SKUs belong to the group will trigger a group requirement. The total aggregate level group requirement will usually be of a larger quantity than that of the individual SKU and the solution will therefore look to fulfil additional demand of any other SKU belonging to the group to fulfil the larger group requirement. This will result in some SKUs in the group being pre-built to meet the group requirement (see Fig 1 below).
Fig1: Group Planning Solution Schematic
Typical Planning Scenarios Supported
Below is a sample list of planning scenarios that our clients have utilised the Group Planning solution for;
- Grouping according to;
- Any Product Attribute or Characteristics
- The Product Attribute and Primary Resource Combination
- The Lot Size of a Characteristic to simulate an upstream operation, e.g. bulk tank feeds to production resources
- The Lot Size of a characteristics to simulate major changeovers on a resource
- Limiting the number of major changeovers (Groups) within a week
- Not allowing some groups to be planned in certain weeks
- Not allowing certain groups to be planned together in the same week
- Forcing certain groups to be planned together in the same week (Multi-Level Grouping)
- Allowing production of each group according to calendars (Production Wheels)
- Respecting the inventory boundary condition in the Supply Network (Maximum and Safety Stock in Qty or Days’ Supply) of all the SKUs within the Group
- Considering average group setup time
- Consumption of Secondary Resources for the Group
The Olivehorse Group Planning solution allows for your business to generate a feasible plan in the mid-term that meets the grouping and setup conditions seen in most manufacturing facilities, whilst still ensuring that inventory boundaries and service levels are being met. The alternative is to plan the group manually, something that can be very complex and time consuming - and often very difficult to achieve!
The SNP Optimiser will now be able to consider factory efficiency properly together with managing the right inventory levels and service levels in a single solve. With Group Planning, the mid-term plan has now properly positioned the SKU planned orders into the right periods and can now easily be passed to short term scheduling without the need to change quantities within the scheduling period. This makes the plan feasible, more cost efficient for the factory, optimal for inventory levels and frees up planners’ time to focus on value add activities.
Our post go-live analysis from implementations has identified an average increase of ~5% in Service Level, with a decrease of ~20% of average projected stock across all production lines. This shows that solution optimises the utilisation of the production lines by grouping the orders, whilst meeting demand but not creating excessive safety stocks.
I do hope you have found this to be an informative read. Please don't hesitate to contact me for further information.
Adriana de Oliveira Alves
Senior SCM Consultant, Olivehorse Consulting