Analytics is a Need, Not an Option for Retail Supply Chain 4.0

Calford Wong, Writer
Calford Wong, Writer


Retail has been turned on its head. From how supply chains are managed to the way people shop, things have changed in drastic ways. 

Quick Summary
  • Tesco yielded cost savings of $24.5 Million by making the same data available to different departments to deal with inefficiencies, reduce food waste and to remain competitive. 

  • Zalora applied data analytics across its online and offline retail strategies to better understand customer preferences and behaviours, to ultimately increase conversions. 

  • On the back-end, companies like Coca-Cola, McDonald's and Toyota utilise supply chain analytics to keep the smallest possible buffer inventory to reduce overhead costs and free up working capital. 

In retail, convenience trumps tradition. Popularly known as omnichannel retail, modern shoppers now have many channels from which they can browse and shop for products - whether that is in store, or at their own leisure through mobile devices. These changing consumer behaviours present retailers with some very real problems, but data and analytics can help boost efficiencies and outcomes in supply chains and in planning and forecasting processes.


Forecasting Consumer Behaviours

With the rise of omnichannel retail, data integration has become a key consideration for retailers looking to improve their operations. 

Tesco, UK’s largest food retailer, has used data and technology to deal with some of its most pressing obstacles. This ranges from evolving customer behaviour, creating efficiencies in their logistics and distribution chains, reducing the amount of food waste, all the way to facing up to new competitors – many of whom are digital first, and were built from scratch to operate in an online only environment.

“Using analytics and clustering and suchlike, we found that the way we thought products hung together – the way we buy products – is not really the way products behave.” Says Mike Moss, Tesco’s head of forecasting and analytics.

Data analytics should be looked at holistically. While different stakeholders require different sets of data, it also needs to be accessible. For example, the Finance Department, Supply Chain and Marketing are all stakeholders, but they all need sales information and forecasts. Tesco operates on a data lake model, with a centralized, cloud-based data store, that is accessible and usable by any department within the business - not just the sales department - as and when it is needed. Since 2006, Tesco’s data analytics on its supply chain optimisation has yielded cost savings of up to £16 million (approximately $24.5 million) annually.

Tesco’s large pool of data is pulled from its global network of stores and logistics centres. This data is intelligently utilised – and analysed – to keep the retail grocery chain competitive in the age of e-commerce. While this may sound complicated, modern data integration does not have to be a complicated process. A small capital investment can yield retailers a clearer picture of their data. This allows for improved forecasts on customer preferences and behaviours, and thus the ability to better serve them.


Increasing Conversions with Predictive Analytics 

These days, a successful sale is just as much about capturing purchase intent as it is about the product. Online targeted marketing campaigns are nothing without the data and the analytics behind the screen. Analytics are well-known to fashion retailer Zalora. The company serves more than 600 million online users and was founded on the premise that data drives sales. Zalora delivers personalised user experiences by tracking and interpreting visitors’ historical preferences, on-site behaviour and intent across all channels. Data analytics allows Zalora to predict customer preferences by tracking their transactions, by gaining a good understanding of customer’s interests; this in turn gives them the opportunity to offer similar, alternative product options that shoppers may not have otherwise found. Read more about predictive analytics here

Retail is particularly well-positioned to use data. Beyond e-commerce sites, retailers can influence in-store movement patterns that can encourage positive purchase decisions. When Zalora setup a pop-up store in 2014 targeting customers who preferred the physical store experience, it incorporated Smart Positioning Technology that collected and analysed real-time footfall, dwell time and heat map data. The system of data collection and analysis allowed them to understand customer behaviour and track sales conversion rates.

This tactic falls within Zalora’s greater marketing objectives aimed to connect online to offline, and it seems to be working. Zalora stores have been ‘popping-up’ across Asia, including The Philippines, Singapore and Hong Kong.

“We use the data from this and other popup stores to adapt and give our customers what they want.” Says C.X. Chua, MD of Zalora Hong Kong.


Optimising Supply Chain

Directly related to consumer behaviours and sales conversions is the ability for a retailer to manage their stock and inventories in a timely manner. Behind every successful retailer is an efficient supply chain, and data and analytics is having a major impact on supply chain design. An innovative supply chain design begins on a micro, systemic level. From tracking and tracing product flow and stock levels in real-time, predicting buying patterns based off customer data, and even the use of automated robots to fulfill orders, data is changing the way goods get to customers.

Just-in-time (JIT) is the grandfather of data-enabled supply chain logistics. Inspired by supermarket inventory management systems, car maker Toyota set out to make their production cycles much more efficient by implementing a system that forecasts demand and keeps the smallest possible buffer inventory on hand to cover that demand. This results in reduced excess inventory, lower overhead costs, and frees up working capital. To make this work however, requires analysis of past production/inventory data. JIT is only possible with robust data analytics in place. To date, Toyota still implements JIT at its production plants across the globe, as do Coca Cola, McDonalds, Dell and Harley Davidson.

In a recent survey of 100 retail professionals, 61% are implementing new supply chain strategies designed to meet modern consumers’ expectations. Of those, 88% indicated that data-driven supply chains are helping them to exceed customer expectations, while 84% claim they allow them to give customers more control over their experiences. As importantly, 99 of the 100 surveyed felt that big data analytics had direct or indirect benefits to customer experience. Click here to discover other ways that data analytics can help to optimise supply chains. 



Today, retail technology reaches further, deeper, and is far more insightful than just online shopping. True alignment of demand, planning, and inventory is now available to all businesses, yet there are still many retailers that show signs of uncertainty when it comes to implementing data analytics that truly captures insights. Whether it is forecasting, sales conversion or supply chains, quality data analytics is crucial in ensuring accuracy and success. 




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