ANTICIPATORY LOGISTICS, A SUPPLY CHAIN’S PERFECT CRYSTAL BALL

A large part of every supply chain’s resources is not used to its full capacity but still kept available, for example as reserve capacity for peak situations or as buffer stock for potential demand. More detailed advance knowledge of customer behaviour and events impacting the supply chain would enable organisations to more effectively allocate their resources, thus reducing costs. And that is the exact promise of anticipatory logistics.

An ocean of data, public and proprietary

The amount of available data that is relevant in one way or another to the supply chain of, say, a large retailer is staggering. Public data, available for free or at a commercial price, includes statistics on Google search terms and trending topics in various social media, real time weather conditions and forecasts, news on the behaviour of competitors, and global public holidays. Proprietary data, generated by a company’s business processes, gives valuable insight into customer behaviour. In a multichannel environment retailers not only have at their disposal point of sale data at the level of individual transactions, but also clickstream data of relevant websites with an unprecedented degree of granularity — for example, not only actual clicks but also the time spent hovering over a link that in the end was not clicked at all.

The term ‘big data’ is fitting because the volume of available information is immense. With powerful predictive algorithms that are already available and rapidly being refined, both shippers and logistics providers can use anticipatory logistics to boost their process efficiency and service quality.

Improved efficiency and resilience for logistics providers

With a detailed understanding of the expected demands on their networks, logistics providers are in a better position to allocate the required resources and improve the efficiency of freight operations, resulting in better performance and lower costs.

A second benefit is improved supply chain risk management. Big data algorithms can be used to evaluate different supply chain scenarios for their response to potential risks, enabling service providers to select the approach that best fits specific customer requirements and avoids disruptions in delivery and manufacturing processes as much as possible.

From customer loyalty programme to anticipatory shipping

One of the purposes of customer loyalty programmes, introduced decades ago, was to collect information about the shopping behaviour of individual customers. Using today’s technology, by integrating a customer loyalty programme with social media platforms for example, the information gathered can be used not only to execute more targeted promotions but also to feed demand-sensing algorithms.

The latest development in this direction is anticipatory shipping: proactively initiating shipments even before the customer has placed an order. This would involve packaging and sending items without completely defining their final destination, which would be specified after receiving a final order, while the package is already in transit. This approach would obviously reduce delivery time. Because it relies on the power of large numbers, its applicability is limited to products that are sold in large volumes, like new smartphone models or books high on the bestseller list, and to delivery in urban areas.

More ways to reduce supply chain costs

Anticipatory logistics is a new and promising way to bring down supply chain costs, but there are many others. If you are interested to find out more, you can download our recent white paper Six ways to lower your logistics costs (without compromising on speed or quality) here.