The old adage that those who fail to plan, plan to fail is as true as it is cliché. It is also applicable to the future of your company’s supply chain. To be positioned for growth, shifts in demand, and inevitable challenges ahead, planning for the future is an imperative. The use of network optimization modeling software can be an important tool to answer questions about the quantity, location, and mission of manufacturing and distribution nodes. However, simply using the tool apart from knowledge of best practices and the seasoned experience of modeling professionals is a potential pitfall leading to inaccurate results and lack of confidence within your organization.
What are the critical components of a robust and well-built network model that can be used to successfully plan the future of your supply chain network?
Data-driven solutions: The groundwork of any effective network analysis is data. The quality of that data permeates every other aspect of the network study and directly impacts results and recommendations. A model built with poor data quality will yield poor results. Therefore, there are several keys to building a model with accurately validated data. Communication with key stakeholders and owners of critical data points to collect the correct breadth and depth of detail is a needed first step. Attention to detail during data analysis is to catch voids and errors in the data and avoid time-consuming rework. Finally, the production of complete summaries focused on the key points is a critical step in the data analysis process. Like bricks in a wall, small details within the data will inevitably show up in the end results of the network modeling.
Model building with the end in mind – When deciding how best to structure a model, the experienced modeler will begin with the end in mind. What are the goals of the network analysis and what questions need to be answered by the model itself? For example, a network model may be used to choose an additional distribution location. Stakeholders may also want to know which specific customers should be optimally serviced by that additional facility. Perhaps the model will need to be constrained by service level or throughput by product type among a wide variety of other possible constraints. What outputs and level of detail will be needed for the results? The effective modeler will need to incorporate such considerations from the outset to ensure that the model is built with the necessary elements to satisfy desired outputs.
Effective Aggregation – Most models are not built with every individual customer and/or SKU loaded as elements. A robust model will have the right degree of aggregation, in which certain customers, items, suppliers, warehouses, transportation modes, and other such elements are grouped at some level of commonality. For example, ecommerce customers within the same 3-digit zip code can be grouped into a single model customer. In this example transportation rate differences within a 3-digit zip code would generally be insignificant and accuracy is not sacrificed by aggregation. On the contrary, modelers must be careful not to aggregate too deeply and diminish necessary detail.
Flexibility – A variety of scenarios may be built to answer a range of questions. Within the modeling software there are multiple ways to manipulate the model when building scenarios. Knowing the best methods to change elements in a desired scenario without causing unintended consequences is important. For example, if the modeler wanted to run a scenario which optimized only prepaid outbound freight to customer, it would be imperative to include the freight terms in the customer groupings. Failure to do so may still provide an accurate answer in total but would not allow the flexibility to isolate certain elements and summarize results accordingly.
Simplicity – The model should be built as simply as possible while still containing all the parts needed to answer the questions at hand. Are there opportunities to group fundamental data into a single model input rather than 2 or 3 interrelated tables? Could a transportation mode which accounts for an insignificant amount of freight be dropped or rolled together? Such simplification may reduce runtime, eliminate possible errors, and be more easily understood by stakeholders.
All the above factors must be balanced to build a cohesive model which incorporates the right level of detail in the model elements, considers possible constraints, provides appropriately detailed outputs, and optimizes with reasonable speed all without sacrificing accuracy and overall quality of the network analysis. The well-built network model will be robust, flexible, simple, and accurate. Supply chain infrastructure is expensive and complex. Inadequate planning can be even more costly, while a professionally built network model and network analysis is relatively inexpensive for peace of mind it will provide. Perhaps it is time for your organization to invest in a well-built network model to plan for future supply chain success.
—Eric Payne, St. Onge Company
