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How to Review Your Supply Chain Data Solutions

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Logistics professionals rely heavily on supply chain data, both in real-time and after the fact reporting. Supply chain data solutions can be tough to manage, however, particularly as the trend towards 'Big Data' impacts our industry.

Improved data clearly benefits a sector that is hungry for information, yet the sheer volume of data makes measurable improvement a complex task for any business and its supply chain.

In many cases, businesses have found that the promise of Big Data has not proven itself to be the reality. Research by Wikibon recently found that 46% of organizations that have invested in Big Data solutions simply aren't seeing the expected return on investment. At the extreme edge, a small segment of respondents even claimed their ventures into the field had been "total failures."


More Than 'Big': Defining Supply Chain Data

Defining Big Data itself is a good place to start before diving into your own supply chain data solutions, as the two can be quite separate considerations.

More often than not, the big data label applies to data sets too large or diverse to be processed by standard database platforms and analysis tools. This can easily lead to more confusion, especially if you don't have a great head for analysis, as data solutions vary greatly subject to the size and scope of the operation to be measured.

An easier way to approach a review of how you manage your supply chain data, is to focus less on labels and more on the specific data challenges of your business and its supply chain.

Data, big or otherwise, is the foundation of effectively managing complex order fulfillment and shipping operations.  Clients of Capacity LLC vary greatly in size, from small craft ecommerce sites to household-name retailers such as Macy's and Target. The most unique aspect of our data solutions isn't providing the biggest, feature-bloated data solutions, but in emphasizing client customization. We find that creating a comprehensive database of shipping details and rapidly, reliably deploying them for each new client/retailer relationship is the best approach to build efficient, effective supply chain data systems.

From this base of rapid EDI (electronic data interchange) implementation and customization, the challenge of data management shifts away from intangible terminology. The nuts and bolts of using your supply chain data is to deliver a detailed perspective on - and control over – the order fulfillment process. Offering this advantage puts logistics providers in a pivotal position to define data-driven business solutions for those we serve.

Zero In On Your Data Challenges


The crucial element of adjusting your data systems to define and improve distribution lies in understanding where exactly you need it to go to work.

As highlighted in this MDM article, perhaps the full extent of the Big Data movement is only fitting for global, techno-centric companies.

For logistics professionals, a more selective approach is needed. Rather than letting the tools define the work, we should look at the pain points along the supply chain itself and select those that need to be more deeply analyzed. From there, key data points can be identified, monitored, and drawn together to form a basis for better decision-making. In this sense, logistics on an individual business level benefits more from data niches than the somewhat overwhelming Big Data label.

Knowing where to look becomes the next skill and there is a great deal of potential for hiring in this area of expertise. Already the role of 'Data Scientist' is spreading into the language, propelled by such high-profile analysts as Nate Silver. Businesses seeking enterprise-wide levels of improvement through data will certainly need a team of these experts, but smaller firms may find that existing team members with a good head for numbers and analysis can do the job.

Whatever the size of your business, ensure that you have the right people crunching the numbers, and someone well versed in your industry requirements to check and put them information into context.


The Bottom Line

Specific areas of the logistics, warehouse management and order fulfillment processes will see clear benefits from time invested to improve supply chain data solutions. Digging out the right data and using it to define and solve existing challenges is the objective, rather than reporting vast amounts of information just because it's there.

We already see major benefits for our clients in the technology we use to manage orders, particularly when customized to fit a previously identified supply chain need. Getting into the nuts and bolts of data is certainly tricky and comes back to having the right resources at your disposal and the right people to manage them.

Grasping the moving parts of a supply chain will stand managers in good stead to confidently decide where practices can be made more efficient. Big Data, however we choose to define it, clearly has a role to play in this analysis but should be used as a foundation for decisions, rather than driving them in its own right.