Transportation is one of the most expensive components of modern day complex supply chains and drives supply chain challenges. But the good news is that it is also one of the most data intensive parts and therefore easy to apply analytics. If companies want to leverage their supply chains towards a competitive advantage, use of analytics is a requirement. Cost modeling and cost forecasting are activities within transportation management which involve extensive analytics.
With increasing complexity, two common supply chain challenges for transportation managers are
1. Inability to perform root cause analyses: One of our customers is a transportation manager responsible for managing the cost of their nationwide distribution centers. With geographically far flung DCs, they know that the cost of transporting their products varies across the centers. However day-to-day management offers them little insight into why this variability should exist in the first place. Knowing the root causes for this spread will enable them to optimize their operations. A logical strategy would be to reduce this variability, if possible. Cost modeling helps in addressing this challenge.
2. Inability to make fact based decisions: The same customer has another critical challenge. They manufacture several different products and ship them as differently sized batches. They intuitively understand why certain products and batch sizes are cheaper to ship than others, however without quantifying these differences, it is difficult to make fact based decisions. Furthermore they cannot make reasonable budget forecasts or identify strategies to improve gross margins, for example. Cost forecasting is needed to answer this challenge.
Analytics for supply chains focused on transportation (or in general) involves two main components: business intelligence and predictive analytics. A complete analytics solution will require both of these technologies.
The BI part of analytics is the “front end” for managers. It typically consists of an easy to navigate dashboard which helps them to not only visualize data, but allows them to ask and answer questions such as “How many”, “what happened”, “where are the problems” and “what action is needed”.
The predictive part of analytics is the calculation “engine” which allows them to generate data for future scenarios and plan for contingencies. It helps to answer what-if questions and enable them to make confident forecasts about their budget needs and expected margins.
Originally posted on Tue, Mar 13, 2012 @ 09:02 AM