http://data-informed.com/5-steps-supply-chain-executives-can-take-to-harness-big-data

5 Steps Supply Chain Executives Can Take to Harness Big Data

The term “big data” is the buzz word du jour. But to drive the opportunity, companies must bypass the hype. New forms of data do not fit traditional IT architectures. Traditional supply chains were architected to use structured data using software based on relational databases. The big data era will make many of the investments obsolete from the last decade. So, is it a problem or an opportunity?

In this recent study, companies see the greatest opportunity to use big data for “demand” (to better know the customer and improve the response). However, actual current investments are in the areas of “supply” not “demand.” Respondents view supply-centric projects, like product traceability (involving product serialization and real-time location information), supply chain visibility and temperature controlled handling of goods using sensors as important and immediate investment areas.

Going After the Prize 

While success in a big data supply chain strategy can ignite new business models and drive channel opportunities, the effort must be about more than the data. Seizing this opportunity requires leadership and the initiatives a corporation pursues need to be aligned to business objectives, with a focus on small and iterative projects. Here are five recommendations:

1. Build a cross-functional team that focuses end-to-end. 

Big data offers an opportunity to use new data forms and emerging analytics to build processes outside-in, from the customer to corporate headquarters. This can best be accomplished when there is a team of IT and line-of-business leaders that can work cross-functionally with a focus on end-to-end processes. This team is best led by a line-of-business leader and is guaranteed a higher level of success if it proceeds to work on the following steps.

2. Side-step religion. 

The term “supply chain” is fraught with issues. Some companies think of the supply chain as a limited function within the organization that focuses on logistics or inventory, while some companies think about the term as a much broader concept that encompasses end-to-end processes.  Do not get entangled in arguments of supply chain as a function or an end-to-end process. Don’t argue what to call it, just get on with it!

3. Start small and iterate. 

Do not get caught up in the ERP-like mindset of big projects with a series of releases. Focus on small wins and learn from the use of analytics to spread to other functions. For example, the use of in-memory reporting from Qlikview and visualization from Spotfire and Tableau are being used by a number of our clients to improve data usage today to win organizational support and funding for big data initiatives. Organizations have many technologies and systems, and IT architects need to separate the decisions for analytics from the decisions being made on their systems of record implementations (ERP). ERP is only one source of data, and over time, will become a less significant contributor to the overall supply chain response.

4. Provide innovation funding. 

Give these cross-functional teams dollars to experiment. Allow for trial and error in the process. Some companies have had success with having departments submit requests to a cross-functional business analytics or big data team for spending on analytics and use of different data forms by cross-functional teams working on big data initiatives.

5. Consolidate business intelligence centers of excellence and master data management efforts into big data initiatives with business goals. 

Why? Some of the new techniques associated with advanced analytics enable data enrichment and data parsing that previously had to be hard-coded into systems.   Organizations that are good at using data will win in driving big data opportunities and take advantage of these opportunities earlier. Solving business problems must be the goal.  The results come by harnessing of the cross-functional efforts of knowledgeable people, working on teams to solve analytical problems.