You’ve probably heard them already. Most supply chain directors have.
Myth 1: You have to act really fast – like by yesterday
Myth 2: You never know what might be useful
Taken together, these myths have given rise to the ‘leap before you look’ approach to big data that’s already ending in crashes. Or as a December 2013 Harvard Business Review (HBR) article puts it: Companies are investing like crazy in data scientists, data warehouses, and data analytics software. But many of them don’t have much to show for their efforts. It’s possible they never will.
The title of that HRB article?
You may not need big data after all.
What’s more likely is that most supply chains will need big data. The big unanswered questions are:
- How supply chain directors should approach big data initiatives
- What they should be focusing on
(1) Start small
Big data is going to have a significant impact on supply chain planning. That said, the facts simply don’t support the idea that there’ll be an abrupt change in the way supply chains are designed and managed.
Instead of jumping on the big data bandwagon, thoughtful supply chain directors are taking small steps towards big wins.
For example, a delivery company might benefit from insight into the relation between the addresses it delivers to and delivery performance. Such a company might then start collecting and analyzing data on how long it takes to deliver packages to various addresses; the number of failed delivery attempts associated with each address; and the effect of weather conditions/drivers/vehicles etc. on delivery performance. These insights could be fed back into an intelligent supply chain planning and optimization system to enable more efficient, profitable operations.
Similarly, a manufacturer might be interested in the factors affecting the failure rate of various components, and what this means for certain product categories. What’s the percentage that’s really usable? What’s the quality of inflow? Under which conditions should you allocate more buffer stock?
Once again, an intelligent supply chain planning and optimization system will transform this input into real decision making power by helping planners create optimized schedules that minimize disruptions.
(2) Target your efforts and insist on results
While the details will change from company to company, successful applications of big data share a common approach. They focus on areas where high levels of uncertainty are having a significant impact on the supply chain. Once you have that figured out, it’s possible to ask intelligent questions about how big data and advanced analytics can be used to generate better outcomes.
It’s easy to lose sight of this goal in all the hype. In fact, that HBR article went on to say, The biggest reason that investments in big data fail to pay off, though, is that most companies don’t do a good job with the information they already have. Until a company learns how to use data and analysis to support its operating decisions, it will not be in a position to benefit from big data.
Here at Quintiq, we believe that big data is extremely helpful in developing predictive analytics – those patterns and trends that help you make sense of huge quantities of data.
These patterns then need to be combined with domain knowledge: your own intimate knowledge of your supply chain.
But it doesn’t stop there.
The final, crucial step lies in transforming that input into better supply chain decisions. And for this, you need to link big data and predictive analytics with the supply chain planning and optimization systems that will help you make better, more profitable decisions.
Because isn’t that what big data is really all about?