With ever growing attention on leveraging data, supply chain executives are honing their approaches to applying analytics to distribution, logistics and transportation processes. Enterprise analytics for transportation presents a unique opportunity to increase efficiency and meet global customer requirements. Leaders are moving from static and backward-looking optimization techniques to forward-looking simulation capabilities that integrate with their existing technology platform to be able to operationalize models and make improvement stick.
Companies are building operating models to leverage investments in technology and available data from the growing use of analytics across the business. Their success is rooted in balancing two key facets of the business – understanding the evolving growth from new markets, product mix, and channels married with an understanding of how transportation industry and regulatory dynamics constrain the optimal deployment of assets and resources. This is where building quality scenarios to model is critical.
The power of analytics and modeling are reflected in the way in which operating scenarios are developed. In addition to traditional levers like load management, driver availability, and carrier capacity, models should consider a number of influencers including:
Although operational challenges vary – especially across industries – there are common drivers of successful implementation of analytics present in companies that are able to sustain value. What data to model, who drives the transportation analytics function and how logistics operations utilize analytics capabilies are key.
Companies embarking on the transformation of transportation to a data-driven operating model should start by taking a look at the influencers of value. These include: