Expiry planning adds a whole new dimension to the supply chain planning and simulation process, but in those industries where expiry (or best before) is part of their manufacturing and distribution process (pharma, food, med devices among others), the ability to generate production plans that take into account expiry is absolutely vital to creating a realistic plan. Most ERP systems support expiry on some level, usually from a transactional point of view (what I will call simple expiry). Simple expiry allows the users to set expiry limits on a part by part basis, and the system will then calculate when a particular supply will expire. This is mainly used for 3 things in an ERP system:
- determining when a particular supply expires so it can be scrapped and/or replaced,
- allowing planning to determine in what order to use a supply in order to avoid scrap, and
- to enable inventory control and shipping to determine what supply can or cannot be used in production or order fulfillment.
The problem with this expiry model is that it does not realistically model actual expiry conditions in the supply chain, as actual expiry times can be consumed by production and quality inspection lead times, perhaps through several levels in the supply chain.
What this means is that supply that was projected to be available until a certain period in time will actually expire earlier, leaving an unanticipated gap in supply. This could lead to real shortages and dire consequences for customers if not recognized in time. What simple expiry does not do is allow expiry to be driven by dependent components in the BoM, which I will call complex expiry. In this model, component parts (perhaps several levels down in the BoM), drive the expiry date of the parent part. A true expiry planning system must be able to account for the lost expiry time as the driving expiry component is processed and tested up through multiple levels in the BoM. As well, when supplies are allocated to demands, the originating demand’s expiry requirements (minimum shelf life, or how long the product must last before expiring once it has been shipped) must be known when planning supply, otherwise a mismatch between the demands requirements and the supply’s ability to meet them can occur. Another issue to consider is safety stock being held at multiple levels in the supply chain, as by its very definition, safety stock can exist in inventory for extended periods of time, further consuming expiry time. In order to get a true supply/demand picture when planning or simulating in the supply chain, actual or planned supplies must be matched to their allocated demands. This can only truly be done in a CTP (Capable to Promise), bottom up analysis of the true available dates of the components, and how they affect the parent’s availability. In other words, you cannot properly fulfill demand for expiring product unless you have a true picture of when your supply will be available, and when it actually expires. A simple expiry model can be made to accommodate certain aspects of this planning model (by manually or programmatically accounting for lead times etc. in each level of the BoM in the expiry data), but without the ability to account for actual variations in the supply plan through a CTP analysis, the picture will always be incomplete. In summary, in order to get a realistic view of your supply chain under expiry, a complex expiry model is required (and a planning tool which supports this). In order to get a true picture of scrap, gaps in coverage, safety stock requirements, batch production timing, and the financial implications associated with each, the ability to support the complex expiry model is a requirement in any planning tool.