While many early adopters of big data analytics have focused their attentions on areas such as marketing, customer service and the financial sector, as the technology matures, it is attracting attention from a wide range of sectors and for a large number of use cases.

One area where the technology has particular potential is in supply chain management. It was noted by CFO.com that this part of a business is typically rich with information from multiple sources, while at the same time being a major cost centre for many organisations. As such, there are huge opportunities to leverage this information and make processes more efficient.

Regenia Saunders and Jason Meil from management consulting firm SSA & Co wrote that many current operations are not taking full advantage of this potential.

"They are optimising, but not strategically," they stated. "When applying data to [the] supply chain, it's critical to step back and look at what truly drives business value."

A common problem businesses encounter when they try to apply big data analytics to their supply chain is they end up focusing on the wrong areas. To counter this, businesses need to devote more time to the planning stage. This is often the most difficult part of the process to get right, but mistakes here can have the biggest impact on overall costs.

"We've found with our clients, again and again, that big data can have a measurable impact on driving greater accuracy in planning, ensuring that companies make the right amount of the right product," Ms Saunders and Mr Meil said.

By deploying advanced algorithms and machine learning, businesses can see increased forecast accuracy across their SKUs, which can lead directly to less waste, less inventory, and fewer stock-out issues.

This will be particularly important in today's retail environment, which is seeing increased volatility in consumer buying patterns. At the same time, the fast pace of growth in emerging markets can often make it tricky to predict where organisations should be focusing their efforts.

Therefore, strong use of analytics will be essential in meeting demand and ensuring companies are aligned to the needs of the market. For example, if the data suggests that customers prioritise convenience above all else, this can indicate that the organisation should explore how it can optimise its supply chain to get products to consumers as quickly as possible.

On the other hand, if quality is found to be a key driver, investments in R&D, product lifecycle management, supplier relationship management, and manufacturing should be prioritised.
"These supply chain decisions directly impact financial allocations," Ms Saunders and Mr Meil said. "Making the best decision for the organisation requires identifying and measuring key performance indicators (KPIs) directly related to key supply chain areas."

To be effective, supply chains must be lean, agile and externally-focused. Effective big data analytics solutions can help a business meet these goals and position itself for success.