If your company manufactures seasonal items, then you know that these products often add complexity to distribution network planning. The answer for many companies is to use multi-period network design models to understand their seasonal storage requirements.
In theory, by representing the time-phased imbalance between supply and demand, a network design model will calculate the expected storage requirements, presumably by building inventory in advance of the imbalance. However, the theoretical and realistic operate on different planes.
Unfortunately, when it comes to modeling seasonal storage requirements, the level of detail required to produce confidence in a build-ahead inventory profile, from both a time and location perspective, is frequently at a level too granular to be consistent with the scope of an overall network design project. In the real world, establishing the appropriate level of build-ahead inventory is often the result of multiple factors, including complex and overlapping production capacity constraints, batch size requirements, and planning heuristics. Add in the manufacturing and operational realities of safety stock and target inventory requirements, transportation capacity, and transportation load building requirements, just to name a few, and the model becomes incredibly complex.
In truth, reflecting all of the constraints necessary to produce an intuitive build-ahead inventory profile would often require more effort than can be realistically expected to be included in a network design model. Furthermore, even with advancements in the enabling technology, mixed-integer optimization applications view the world in much starker terms than do production managers or production and scheduling systems when creating operational plans.
The chart on the right is a prime example of historical time-phased inventory behavior that would be challenging to replicate based solely on the imbalance between supply (i.e., production) and demand.
So even when a network design model is built in such a way that it contains most of the real world supply constraints, the time-phased answers can still lack credibility, especially at a location level. This is because the math used to solve the problem within the application is vastly different than the rules used to create operational plans.
However, there is an alternative approach that Chainalytics’ network design team employs successfully, when business conditions warrant. In these situations, production planning provides a location level, time-phased inventory plan that often already exists either in support of an S&OP or as rules of thumb in support of distribution planning. In either case these plans or rules can be incorporated into a network model as time-phased constraints. We have found that doing this, in conjunction with applying a handful of other appropriate constraints, increases confidence among operations teams which in turn increases the likelihood of implementing the results, accelerating the time to actual value.
If your organization lacks understanding of the seasonal storage requirements for time-phased production planning, seek the help of supply chain professionals with extensive network design modeling experience to determine the best strategy for your company.
Jeff Zoroya is a director in Chainalytics’ Supply Chain Design competency where he focuses on helping organizations optimize their supply chain network strategy.
