In our years of experience analyzing distribution and manufacturing operations, we have come to gain an intimate understanding of simulation and in-depth analytics and the value they bring to determining design for operations.
The use of data and representing reality through math allows for operational adjustments that are proven before implementation. The value of this cannot be overstated. An analytic or simulation-driven design can help design new facilities and can also help exploit the characteristics of existing facilities. Our advice is to do both.
In several discussions over the years, we have asked clients to use analytics on existing operations in order to squeeze out more capacity and better execution to allow more time to design new facilities. A new facility that is designed to maximize execution of the service proposition is the competitive coup de grâce. Anything short of designing the facility to blow away competition with service and to do so with a minimized cost footprint is foolhardy. Building a bigger version of what exists is putting lipstick on a pig.
The process should begin with clearly defining the service proposition of the business unit. If a change is desired (like reducing lead time or deciding to incorporate direct-to-consumer shipments), now is the time to run analytics on what that means to the operation as it is and how it is needed to be.
It is often true that the operation can be enhanced enough in-situ to get more time out of the existing facility. Creating capacity in the existing space frees time and resources to better design the new facility. These can be used to look for more economical rent or build costs, better location for labor recruitment, a location that isn’t landlocked for future expansion, the possibility of combining multiple facilities into one…. The possibilities are numerous.
In short, analyzing data to determine demand profiles, manufacturing and distribution supply profiles and product flow through either manufacturing or distribution (or both), can pay multiples in improved cost footprint, manageability and capacity. Modeling can identify constraints and help control statistical fluctuation. But most importantly, modeling can ensure that a proposed service proposition can be met. In the end, that is the most important of all. Without revenue, nothing else matters.