Note: Today’s post is the first in a new series titled “Editor’s Pick” where we highlight recent posts published by our sponsors that provide practical knowledge and advice on timely and important supply chain and logistics topics. Today’s post is by Diogo Quintas, formerly with LLamasoft, on techniques companies can leverage to improve and streamline their supply chain modelling activities.
Access to data is increasing in nearly every field, including supply chain design. In fact, collecting and leveraging that data is often cited by supply chain professionals as one of the biggest challenges facing their organization. Whereas in the past modelers dealt with spreadsheet sized datasets, they now have access to tera-sized data lakes. This growth has brought much success to supply chain designers allowing them to develop deeper and more meaningful models. However, the time to answer is increasing due to the difficulties of large-scale data modelling. By leveraging advanced analytics, many organizations are cutting down on time spent analyzing data and quickly delivering results.
This changing landscape has brought the ability to work at a more granular level, including at the customer and SKU level, increasing the success of design models by increasing their robustness and, in turn, the confidence that stakeholders have in the results. However, it has also brought many challenges. Uncovering issues with data can be difficult, time consuming and often times occurs too late in the project causing drastic impact to project timelines. Aggregating, summarizing, and understanding important factors in the data is still necessary to make sense of it, but how can designers know they are capturing the right group of SKUs or customers? And how can they distinguish between multiple factors and variables to discern which are the critical ones?
