By Aditya Ramachandran, vice president, Data, Analytics and Automation, Dell Technologies
It’s been a year like no other. From geopolitical tensions and natural disasters to a global pandemic, many business’ operations have been threatened. Each reminds us that the future is uncertain. But there are some things we can control, and absolutely must because we depend upon them so much, including the reliability of our supply chains.
To stay the course and respond to changing volatile demands, supply chains need to be digital, end-to-end, and data-driven. Only then can businesses optimize how they operate, narrow the range of possible outcomes, drive a better customer experience and achieve greater certainty. This is enabled through automation and leveraging artificial intelligence (AI) and machine learning (ML) to deliver critical business insights.
Data-driven supply chains are not exactly new but operating an end-to-end digital supply chain is still rare in many sectors. Understandably so, it’s no mean feat. It requires a multi-pronged approach that begins with robust data, is governed by strong stewardship, and includes traceability and data governance so businesses can trace the data’s lineage and understand where they’re from.
These insights allow companies to not only reduce costs, identify risk, and avoid pitfalls, they also positively impact their highest priority–their customers.
A digital, data-driven supply chain also lays the foundation for advanced analytics. In the effort to predict what’s coming down the pike, it’s a game-changer. Such a supply chain can withstand the ripple effects of changes in any link upstream or downstream, and then adapt to prevent serious whiplash when a disaster does strike.
The demand for this type of digital transformation is becoming increasingly vocal in the wake of the pandemic.
Eight in 10 global businesses surveyed for the 2020 Dell Technologies Digital Transformation Index (DT Index) had to fast-track at least some digital transformation programs this year.
Approximately a third of these (34 percent) transformed their services and consumption models. 89 percent observed that the “pandemic has shown the need for more agile/scalable IT to allow for contingencies.”
Although numerous aspects of the supply chain are digitized, often final decisions are made by a team member working with spreadsheets. Eliminating this manual process is the last step in digitizing the supply chain; arguably one of the hardest things to do and unfortunately, one that many companies haven’t been able to accomplish yet.
Use AI and ML in Supply Chains
AI may not be able to offer full transparency; however, it can deliver insights, identify anomalies, provide a better forecast, clarify patterns and improve performance. You can use AI to increase the quality of data and business outcomes, whether you’re looking at planning and delivery, procurement, manufacturing, or warehouse management.
For instance, at Dell Technologies, we use an anomaly detection ML model to predict unexpected demand based on irregular orders. These insights are important as anomalous demand can significantly disrupt supply chains if they’re not handled properly.
The AI models predict the estimated time of arrival of shipments and how many container loads will be needed. Order probability and date detectors forecast the statistical probability of a deal being converted, along with an estimate for the week when an event might happen. Such intelligence can help forecast demand and supply.
ML algorithms can help run stock inventory of expensive or niche parts. Instead of fully stocking expensive parts at each warehouse location, AI can determine the best locations to pool these parts.
The use cases of AI in supply chains are not only confined to advanced insights: We can use AI in the pursuit of better data—predicting missing data fields in large data sets, for instance—as well as in the pursuit of making better business decisions.
Preparing for the Future
The pandemic has sharpened the focus on supply chains and underscored the importance of resilience. It’s emphasized the significance of transparency and the necessity of making better decisions using data-driven analytics.
Supply chain management has moved from a univariate model optimized only for cost, to a multivariate one where better outcomes and greater resilience are factored in.
Will such a supply chain be able to account for black swan events, such as the sharp increase in technology demands for at-home working and learning during a global shut-down? Maybe, maybe not. But collectively we should be able to respond much quicker with the right automation, visibility, and predictive analytics.
Certainly, digital leaders should be able to act faster than anyone else, gaining a competitive edge and giving their customers the reliability they deserve. With a flexible, digital supply chain, these businesses can prepare for unprecedented events while better tolerating shocks, so they don’t have to fear unpredictable tomorrows.