As Big Data, IoT, and machine learning become more commonplace, all types of industries are starting to get in on the trend. However, some businesses are slower to adapt than others — even though the benefits of big data are incredibly obvious. The explosive impact of e-commerce on traditional brick and mortar retailers is just one notable example of the data-driven revolution that is sweeping many industries and business functions today. Few companies, however, have been able to apply to the same degree the “big analytics” techniques that could transform the way they define and manage their supply chains.

In 2014, just 17% of senior executives had made progress towards implementing big data and related technology into their supply chain management structure. Now, it is estimated that the number has grown to just under 50%. Additionally, research shows that the more a company invested in this type of technology, the greater the pay-off they experienced. Big Data is one of the hottest topics in the supply chain industry right now and has pride of place alongside artificial intelligence (AI), machine learning (ML) and automation. It’s the latest film in the cinema and the state-of-the-art phone everyone’s talking about all rolled into one — and everyone wants to get involved. In a bid to gain a competitive advantage, companies are leveraging Big Data for a host of reasons. Through Big Data, businesses can decrease costs, enhance efficiency, and ultimately make smarter decisions.

Big Supply Chain Analytics

Integrating big data technology into every step of the supply chain management process can bring amount tremendous results. By combining robust sets of data with predictive analytics and IoT, supply chain managers can finally have the tools they need for strategic decision-making. What is big supply-chain analytics? Big supply chain analytics uses data and quantitative methods to improve decision-making for all activities across the supply chain. In particular, it does two new things. First, it expands the dataset for analysis beyond the traditional internal data held on Enterprise Resource Planning (ERP) and supply chain management (SCM) systems. Second, it applies powerful statistical methods to both new and existing data sources. This creates new insights that help improve supply chain decision-making, all the way from the improvement of front-line operations, to strategic choices, such as the selection of the right supply chain operating models. Supply chain has been exploited in managing risk, leveraging customer retention and satisfaction forecasting predictions.

Managing risk and creating agility

In a recent study, it was found that 61% of leading supply chain management companies would consider risk very important. With better analytics, tractability has also improved, which is leading to shipped products being accounted for from the start of their journey until the end.

Based on all the data collected from supply routes, successful deliveries, and problems that have been reported, those running supply chains are now armed with data that will allow them to recognize potential problems before they pop up, and proactively address potential kinks in their distribution system. This also speaks to the modern supply chain’s flexibility. With this additional knowledge in hand, routes can be changed to account for issues that arise after the delivery process has begun.

Customers: increasing retention and satisfaction

We’ve all heard the phrase “the customer is always right,” so if a customer changes their order overnight, or after delivery has already gone out, they expect suppliers to meet their needs as soon as possible. While this can be a burden, 90% of customers who had a company fail to meet their demands will not do business with them again.

With this number in mind, suppliers will need to do everything they can to satisfy their consumers. Thanks to big data and analytics, suppliers will not only be able to get another order out the door efficiently, but they also have the opportunity to anticipate an increase in demand based on previous orders and market trends.

Forecasting prediction

Well-planned and implemented decisions contribute directly to the bottom line by lowering sourcing, transportation, storage, stock out, and disposal costs. Hence, using BDA techniques in order to solve supply chain management problems has a positive and significant effect on supply chain performance. For a long time, managers and researchers have used statistical and operational research techniques in order to solve supply and demand balancing problems

A case review of how big data drives the supply chain management in one giant e-commerce firm, consumer retail firm and a payment and card service will show how they are leveraging information to enable better decision-making, thanks to the knowledge of what their customers require;


By analyzing what a customer has recently bought, items in the shopping cart and what products a customer has searched for, Big Data enables Amazon to offer suggestions to the customer in a bid to generate more revenue. Its personalized recommendation system is thought to account for 35% of the company’s annual sales. Amazon has the drive to deliver its orders to customers faster than its rivals. In 2019, this was taken a step further through the launch of One-Day Delivery. Amazon collaborates with manufacturers to track their inventory before opting for the warehouse closest to the vendor and the customer in order to decrease costs by 10–40%.


With 90mn transactions made weekly across more than 25,000 stores, Starbucks is a renowned brand worldwide. The introduction of rewards apps via mobile devices has allowed the company an insight into its customers spending habits. Starbucks’ mobile app is popular among customers with over 17mn active users, while its rewards app sees around 13mn active users. These apps provide Starbucks with a plethora of information about their customers’ favourite drinks and entices them to use the app through complimentary drinks. Another way that Starbucks reaches customers is through targeted and personalised marketing. This is done by sending an email to a customer who hasn’t visited a store recently and advertising a new product similar to one they’ve previously ordered in a bid to re-engage them with the company.

American Express

US-based bank American Express is leveraging Big Data to track customer behaviour. With more than 110mn American Express cards in operation and over 1trn transactions processed, the bank handles around 25% of US credit card activity. As is the case with all other fintech banks, cybersecurity is considered the main priority and, as a result, American Express has positioned data analytics and ML at the heart of the company’s strategy to combat this. The firm has deployed a ML model that combines a variety of different data sources, such as card membership information, spending details and merchant information, to detect suspicious events in order for a decision to be made in milliseconds and prevent fraud. American Express seeks to connect cardholders to products and services. To that end, it can recommend a customer a restaurant that they are likely to enjoy based on previous purchase data.

While the aims of supply chain management (SCM) include saving costs, increasing productivity and delivering products and services quickly and safely, the presence of multiple manufacturers, vendors, distributors, and channels only add to the complexity. This makes data collection and analysis challenging even for an enterprise with huge resources at their disposal However, big data analytics can provide the right answers and eventually make processes simpler. Big data is designed to be compounding — data collected and utilized in one application can easily cross over into another. Additionally, the more data sources available, the more accurate predictions will be and the better the results.

So, this information begs the question: which areas in SCM receive the greatest benefits from big data?

Inventory Predictions

Businesses need to be able to capitalize on opportunities as soon as they present themselves. But predicting sales trends and inventory fluctuations requires rich data and intelligent predictive analytics.

Product Quality and Temperature Control

Many industries, such as food, agriculture, pharmaceuticals, and chemical processing chains need to closely monitor and control specific elements in the supply chain. Even a slight change of a few degrees in temperature can impact the quality of the product — or even make it completely unusable.

Order Fulfillment and Real-time Tracking

Efficient order fulfillment and traceability are essential, both for business productivity and customer satisfaction. Amazon has changed the game by offering incredibly short delivery times along with alerts for estimated drop-off times and minute-by-minute tracking.

Integrating big data technology into every step of the supply chain management process can bring amount tremendous results. By combining robust sets of data with predictive analytics and IoT, supply chain managers can finally have the tools they need for strategic decision-making.

Although investing in big data can seem intimidating, overall the outcomes far outweigh the cost for industries across the board. There is no doubt that businesses will be investing and leaning on big data technology even more in the near future.



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