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Big Data Drives Anticipatory Logistics

One of the core objectives of anticipatory logistics is to foster the supply chain management system by increasing the overall process efficiency and reducing the cost involved into transport, inventory maintenance and other factors across the trading world.

Earlier, when information technologies (IT), telecommunication and Internet were limited, the logistics was a response-based field. Well, the same cannot be said about anticipatory logistics, it is way beyond ‘response based’. Anticipatory logistics is confidently backed up by big data and predictive algorithms. The data procured from IT, telecommunication, and social media platforms help retailers and manufacturing companies to understand and gauze the consumer behavior and sentiments. Once this aspect is understood, it becomes easy for retailers and their logistics partners to chart out the commercially feasible logistics plan. And if the logistics plan is in place, then at the time of peak season or in the event of new product launch things don’t go pell-mell. A business is well done when the planning and predictions materialize well in time. For logistics and supply chain management professionals, anticipatory logistics is a thing of future and with it are associated many benefits. Let’s see some of its applications:

Applications of anticipatory logistics

Proactive shipping

The trend of shopping, whether offline or online, is on the rise because people now have access to IT and Internet services, due to which knowing about products description and ordering them has become easy. Thus, using anticipatory logistics, the retailers can move their products and commodities to the warehouses or distribution centers where they are likely to be bought by the consumers. During festival time, anticipatory logistics helps in achieving big business goals. The growth of e-commerce retailers is thriving due to anticipatory logistics.

Predictive maintenance

The data gathered from real-time monitoring of smart assets (heavy machinery, vehicles, smartphones) can be used for right-on-time maintenance. Using this data in the anticipatory logistics, manufactures can make people afford maintenance of their vehicles and smartphones instantly. It is the predictive algorithm that drives the automobile industry’s spare parts business.

Forecasting supply chain risk management

Operational delays and product expiry and damage in the transport are some of the risks involved in the supply chain management. With the data of topography, road networks and weather available at hand, reducing risk in logistics becomes a hassle-free affair. At this point anticipatory logistics helps in correcting the system and soothing the operational chores and duties.

Smart stocking planning

Predictive algorithm cuts down the chances of under or overstocking of commodities and products which further leads to mismanagement in supply demand chain. On the other hand, optimum resource location is a vital factor in stocking products. Thus, capacity planning has to be in accordance with the demand. Anything abundant or short will make it an awry situation for both: customers and retailers.

Key opportunities that anticipatory logistics opens up
  • Boost the sales of online retailers as now they can provide same-day or one-day delivery.
  • Logistics companies speed up the delivery time without spending more on fast shipping charges.
  • Prediction of demand becomes almost immaculate, thus results in lean inventory management.
  • Decision planning becomes better. Corrective actions take place in time.
  • Makes way for efficient utilization of resources in warehouses.
  • When the customer receives the order well in time, it increases their faith in the seller and overall satisfaction level.
Key encounters

Despite the above mentioned applications and advantages, anticipatory logistics can face some bad actors in long term. What could these be? Let’s see:
  • Since data plays an important role in anticipatory logistics, issues may arise while exchanging data between retailers and logistics companies.
  • Anticipatory logistics involves predictive data, analytical methods and other tools. It can be tough to handle these things.
  • Data security and compliance of data to fetch results can be tough if there is no proper provision for data privacy and other aspects like data audits and trail audits.


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