Digital prowess: next generation tech to maximise use of data and minimise risk

It is hard to determine which is evolving quicker: the technology or logistics sector. In mature markets, the need to constantly evolve and adapt continues to drive innovation. In this piece, we look at how next-gen tech is boosting flexibility and resilience.

How retailers can maximise data to minimise risk

Worldwide, the accumulated amount of data generated since 2013 has increased tenfold, to more than 44 billion terabytes, according to PwC. From weather patterns to warehouse logistics, and from widespread trends to an individual customer’s shopping habits, data is being generated and collected in every second of every day. Retailers must now navigate an onslaught of data.

In logistics, data carries enormous potential for streamlining processes and boosting resilience. Access to data is only the start of the story.

“One of the key impediments today is that there is a lot of unstructured data, which sits with a lot of non-connected suppliers, and with a lack of standards,” says Carsten Frank Olsen, global head of digital customer channels and eCommerce at Maersk.

“That means retailers today have to deal with a range of different providers, each with their own differing demands for data integration, data management and data normalisation. You need to be able not only to capture that data, but to harmonise it.”

Only at that point, Mr Olsen says, can companies start to separate the “real signals from the noise”. Given the sheer volume of information involved, cutting through that noise cannot be done by hand.

Turning to tech

Increasingly, companies are using machine learning and artificial intelligence (AI) to analyse patterns in deep pools of data and then translate those patterns into real-world decisions.

This approach has been pioneered in the maritime trade industry, which is potentially vulnerable to a broad range of different disruptions such as storms or delays.

AI can help by analysing data generated by companies involved in ocean freight, and using that to forecast likely arrival times for the cargo onboard. “They are predicting arrival at the final destination 60% or 70% more precisely than the old, manual ways of doing things,” Mr Olsen says.

“Ideally, I would have something that can tell me there’s an 80% likelihood this particular shipment, with these particular stock-keeping units [SKUs], is going to be delayed getting into this particular warehouse by 14 hours.”

The end result is retailers can view disruptions in real time, with forward-looking analysis layered on top of raw data. This enables them to take action early and ensure their supply chains remain resilient.

“This means you can plan for, and respond to, local impacts, like a typhoon, a forest fire, an oil slick or port congestion due to a ship breaking down,” says Nitin D’Souza, head of international supply chain at Publicis Sapient.

Changes in demand

For retailers, the same concept applies to forecasting changes on the demand side, rather than in their supply chains. Instead of collecting data on external, physical events, it means gathering information on a customer’s behaviour on a micro level.

“In terms of demand planning, the most advanced retailers are able to use the best of what’s available,” Mr D’Souza says. “Online, it’s not just search queries or purchase data they are capturing, but where people are clicking down to the pixel level, for promotions and so on. All those data sets are taken into consideration.”

Again, making the most of that information is crucial. For that reason, Marks & Spencer (M&S) has “significantly invested” in building a digital and data team, says Neil Phillips, head of digital operations at the retail giant. “Embedding digital and data in all aspects of how M&S serves our customers is critical,” he explains.

In the case of M&S, a practical example is around its popular click-and-collect service. In August, M&S launched trials for a contact-free click-and-collect service, as well as a drive-up option that does not require customers to step foot in a physical store. Again, data is playing a vital part.

“On our drive-up collection trial we’re measuring customer feedback through the Doddle app, but we’re also looking at the time it takes for a customer to collect, at customer uptake generally and critically at the feedback from those who don’t use the service,” Mr Phillips says.

Cost barriers

At the larger end of the retail market, companies in mature markets have already developed their own bespoke algorithms for managing the vast amount of data they collect. In some cases, they have “entire data engineering divisions working on it”, Mr D’Souza says.

“If you think of very largest retailers, they might be looking to forecast hundreds of millions of items in a span of 12 hours, and this gives them a 360-degree view of the intent of the consumer, which they can translate into a supply plan.”

However, that luxury is not necessarily available to retailers of all sizes. As Maersk’s Mr Olsen points out, there is “a substantial cost associated with putting proper data structures in place, getting the technology in place throughout the entire chain”.

“In many cases, they need to have someone who can manage it on their behalf,” he says. That is where logistics providers can play a central role, providing “an open ecosystem, with visibility, predictability and intelligence across the entire supply chain”.

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