By Gary Allemann, MD at Master Data Management 

As lockdown restrictions ease slightly in South Africa and some economic activities resume, the logistics and transport sector is likely to become a centre point of the “new normal” as consumers increasingly shift to remote shopping. As a result, traffic is going to surge as people go back to work and school, all of which adds a layer of complexity to logistics planning. Under such challenging circumstances, the use of real-time data analytics and location intelligence to optimise operations and minimise delivery delays is one way to reduce costs and improve the Customer Experience (CX). 

Accelerated change
Companies in the logistics sector have already started embracing digital transformation, and the Covid-19 pandemic has only exacerbated the need for improved efficiency as economic conditions get tougher. Location intelligence – which Forrester defines as “the practice of collecting and managing customer location data, enriching it with other data sources, and analysing for contextual insights for the purposes of informing optimised actions, decisions, and customer experiences” - can vastly streamline and improve operations.

For example, fuel consumption analytics can improve driving efficiency; driver monitoring and fleet performance analytics can optimise running costs and reduce risks, while GPS technologies can reduce waiting times at allocating warehouse bays. For deliveries, courier companies are better able to manage real-time routing of deliveries to customers based on geo-location and traffic data. Additionally, understanding customer availability to accept delivery, combined with intelligent route planning capabilities can significantly reduce delivery times and fuel costs, which would mean drivers can complete more deliveries and increase productivity. 

Location based intelligence capabilities are increasingly useful in logistics management, because raw real-time data can now be used in ways that were not previously possible. By layering raw data with analytics that assess what that data means for the business and how best to respond to it, analytics can give decision makers the benefit of anticipating more implications of information, in real-time, which makes it possible to handle issues before they become problems. 

Garbage in, garbage out
Similar benefits can be achieved in supply chain management. Businesses that are integrating data management and analytics into their operations are already seeing a high return on investment in multiple areas of the business. Enhanced analytics capabilities, while dependent on data quality, can produce tangible benefits in areas such as order cycle time, stock management, revenue and customer service. A study by Gartner showed that it is possible to reduce inventory by 20 to 30% while augmenting average fill rate by 3 to 7%, which can have significant cost implications. 

In supply chain management, forecasts can only be as good as their input. This is where data quality through solid data governance becomes critical. A workable demand plan requires visibility, which means it’s necessary to know what and when customers are going to order and where they will require delivery within the distribution network. While visibility and insight can only be provided by quality data and an effective analytics capability, such demand visibility will also streamline supply chain functions like vendor-managed inventory solutions and fleet transport management systems. The ability to understand customer consumption and related inventory shifts is key to improving timelines, boosting customer service and managing costs across the supplier partner chain.

A viable and thriving logistics sector ultimately is essential to support every industry that is dependent on the movement of goods. To achieve data-driven logistics, businesses will have to rapidly evolve their digital platforms to unlock the value of their data. Investments in data management, location intelligence and analytics capabilities enable the necessary visibility to inform planning and execution decision making to improve the responsiveness of the logistics and transport chain.