Big Data And Analytics In Supply Chain Management

Supply chain management is a field where Big Data and analytics have obvious applications. Until recently, however, businesses have been less quick to implement big data analytics in supply chain management than in other areas of operation such as marketing or manufacturing.

Of course supply chains have for a long time now been driven by statistics and quantifiable performance indicators. But the sort of analytics which are really revolutionizing industry today – real time analytics of huge, rapidly growing and very messy unstructured datasets – were largely absent.

This was clearly a situation that couldn’t last. Many factors can clearly impact on supply chain management – from weather to the condition of vehicles and machinery, and so recently executives in the field have thought long and hard about how this could be harnessed to drive efficiencies.

In 2013 the Journal of Business Logistics published a white paper calling for “crucial” research into the possible applications of Big Data within supply chain management. Since then, significant steps have been taken, and it now appears many of the concepts are being embraced wholeheartedly.

Applications for analysis of unstructured data has already been found in inventory management, forecasting, and transportation logistics. In warehouses, digital cameras are routinely used to monitor stock levels and the messy, unstructured data provides alerts when restocking is needed.

Forecasting takes this a step further – the same camera data can be fed through machine learning algorithms to teach an intelligent stock management system to predict when a resupply will be needed. Eventually, the theory is, warehouses and distribution centers will effectively run themselves with very little need for human interaction.

Suresh Acharya, who heads JDA Labs, the analytics division of supply chain management and operations planning software developer JDA, tells me “What we are trying to do is derive insights which are both more predictive – they allow us to see what is going to happen, going forward – and prescriptive – now we know something, what should we do about it?”

“Whatever name we gave it, using data to improve our operations is something that we have always wanted to do, and something our customers have always needed. Of course some customers are more mature than others in terms of understanding what value data driven analytics can provide.

“But what has changed everything is the advent of unstructured data. Structured data has well defined fields, and we’ve always provided solutions around it, but the volume of unstructured data is growing and growing.”

One example Acharya cites is a demand from manufacturers for information about how their products are allocated shelf space at retail. This too can now be monitored and measured in real time thanks to sensors designed to detect which brands and logos are visible on the shelves.

But unstructured data has its place in the Big Data world too – particularly if it’s being collected for analysis in a novel or innovative manner. Supply chain planning is driven by forecasting – clearly if your job is to make sure the right things are in the right place at the right time, it helps tremendously if you understand the underlying demand. So here, operatives are learning to apply new technology to old fashioned, structured enterprise data such as is collected by point of sales systems, order books and shipping data.

“But additional data elements which have never been used before are now fundamentally driving forecasting in the supply chain industry. Social Media is one – what people are saying about a service or a product can indicate demand. Weather is another one – not just the big dramatic events like we’ve had recently, but subtle things such as an unusually warm fall or early winter can have an impact on sales which can’t be gauged just by looking at historical data.”

So in short, traditional data monitoring, which would involve sales and order tracking and point of sales data, is now being supplemented with weather, events and news, with the aim being to generate insights in the short term, such as how operations will be affected this week, rather than on a broad, annual timeframe.

As with just about every area of industrial operations, Big Data is starting to make inroads into logistics and supply chain management – large steps have certainly been taken over the past two years – but there is still a long way to go. Opportunities to create efficiency and savings through smart use of data are everywhere and concerted effort is being put into finding them.

“I read an article recently that said ‘Big Data has arrived but big insights have not’, and I think that’s where we are, says Acharya. “I’ve seen a lot of people focussing on collecting and storing data without really having the ability to do anything with it, and that’s going to be the challenge.”

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