C-Suite Agenda

As Supply Chain Woes Continue, CEOs Should Look to Advanced Technologies for Answers

Srini Rajamani

Advancements in new-age technologies like artificial intelligence, machine learning, blockchain, and connected devices/IoT are enabling organizations to reimagine nearly every aspect of their business, making them more connected, more data-driven, and more efficient. This is especially true for supply chains. Businesses are employing these advanced technologies to build intelligent supply chains that can use data more efficiently to increase transparency, forecasting, and efficiency from end to end.

Imagine doing your winter holiday shopping in April.

For the average consumer, this may sound ridiculous. For CEOs in the retail space, however, it’s become all too real as supply-chain disruptions continue to threaten manufacturing and distribution around the world. In a piece by Forbes, retailers said they were placing holiday orders three months or one whole season earlier than usual, hoping that suppliers would have plenty of time to get them their full orders.

The most shocking part of this? They can’t even say for sure that it will work.

Using advanced technologies to reimagine everything

Supply-chain visibility has been notoriously limited, leaving businesses with few options for how to prepare. Ordering more product more often may seem like an easy solution, but doing so puts additional strain on manufacturing and distribution — which risks exacerbating the issues businesses are trying to avoid while increasing the likelihood of lost revenue if the overstock doesn’t sell.

Businesses can look for other suppliers, vendors, and routes, but how do they know for sure that these changes will be any better? With traditional supply chain operations, the answer is simple: They can’t.

Advancements in new-age technologies like artificial intelligence, machine learning, blockchain, and connected devices/IoT are enabling organizations to reimagine nearly every aspect of their business, making them more connected, more data-driven, and more efficient. This is especially true for supply chains. Businesses are employing these advanced technologies to build intelligent supply chains that can use data more efficiently to increase transparency, forecasting, and efficiency from end to end.

The challenge of traditional supply chains

Until recently, supply-chain management has been fairly top-down. Sales or operations planning tools capture data and send it to financial models, which use the data to forecast events and trends so businesses can plan accordingly. This process can take months, creating lags in the system that prevent real-time decision-making. It can also be relatively opaque, offering little visibility into what’s happening throughout the supply chain.

With global supply chains becoming increasingly complex, businesses need an enhanced forecasting function that delivers on several levels:

Nimbler: Forecasters need to be able to respond to what’s happening around the world. Natural disasters, geopolitical conflicts, and major economic events can have significant impacts on the flow of goods. Businesses need to be able to identify events, understand the relationship between macro and micro trends, and adapt as needed.

More specific: Businesses today need specific insights on trends and their impacts. Weather, for example, has become more extreme with climate change, and different regions are likely to experience wildly different weather patterns that might affect production, distribution, or sales. If businesses can get specific insights into these various scenarios, they can make the necessary adjustments, like stocking more or less of a certain product, arranging for alternate production methods, or drawing up better distribution routes.

Speedier: Expediency is key. Businesses need real-time insights from throughout their supply chains and the ability to act quickly. This means greater connectivity and communication throughout the chain. It also means more advanced processing of that data.

The value of AI in forecasting

Supply chains are complex, data-rich environments. Production, shipment, orders, invoices, returns, quality control — there are so many touchpoints producing so much data, but that data is not always easily accessible. The better an organization can collect, process, and visualize data, the better equipped it’ll be to respond to events accurately and on time.

Artificial intelligence and machine learning have advanced data-processing capabilities that make it easier for businesses to get the information they need. AI can process huge volumes of data quickly and organize it to be easily accessible, enabling better performance across key areas of supply chain management:

Identification and classification: Identifying overstock, liquidation options, and maintaining product quality standards.

Prediction: Identifying demand fluctuations, labor shortages, shipment delays or roadblocks, and interrupted production.

Optimization: Strengthening near-term inventory and reverse logistics planning.

These are especially important topics with online retail sales on the rise. Instead of going to physical stores, people are buying various styles or sizes of products to try them out and returning whatever doesn’t work out. Managing these returns requires significant coordination.

Intelligent supply chains can help by enabling businesses to track production, distribution, inventory, and returns. Using connected devices and advanced data processing, businesses can identify and address inefficiencies throughout their operations, reducing timelines and improving customer service. For example, Wipro delivered a sales service forecast solution for a retail store, using Google Cloud and advanced AI to reduce lag time in forecasting from seven days to 15 seconds.

How to use AI in supply chains

Forecasting is an important part of supply-chain management, but intelligent supply chains can also improve other capabilities. Below are several key areas in which businesses can employ AI, including machine learning and predictive analytics, to strengthen supply chain functions:

  1. Decision support — AI and machine learning algorithms process the large volumes of data collected throughout the supply chain and generate deep insights that businesses can use to make more informed, targeted decisions.
  2. Anomaly detection — Continuous data monitoring can improve anomaly detection, allowing businesses to be more proactive and avoid disruptions.
  3. Identifying key influences — Trend detection is a key part of forecasting. AI can help identify trends in the data and make connections to help businesses identify potential influences on operations throughout the chain.
  4. Automation — Automating processes can increase speed and efficiency throughout supply chains. Businesses can automate data processing and inventory tracking, as well as aspects of sourcing and distribution.
  5. Human interference — AI pairs well with a human workforce. Its advanced data processing speeds can increase efficiency, while its natural language processing capabilities can improve communication between human workers and machines, streamlining operations.

To stay ahead of the curve, businesses need to know what’s going to happen before it happens. And though predicting the future may be an unrealistic goal, intelligent supply chains help businesses get pretty close by providing them with deeper insights into more events from a wider view of their supply chains.


Written by Srini Rajamani.
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Srini Rajamani
Srini Rajamani is senior vice president and sector head of Consumer and Life Sciences at Wipro Limited. Srini is passionate about helping companies reimagine their business: he has been delivering transformation to global clients in CPG, Retail, Manufacturing, and Finance over the last 25 years.


Srini Rajamani is an opinion columnist for the CEOWORLD magazine. Connect with him through LinkedIn. For more information, visit the author’s website.