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Machine Learning: Making Supply Chain Agile

Supply chain and logistics companies are adopting machine learning into their processes faster than we can imagine. Across the industry, companies are using machine learning to forecast demand, integrate systems, provide real-time visibility on shipments and improve efficiencies in last-mile deliveries, among a host of other uses. As per a recent Gartner Report, the use of artificial intelligence (AI) and machine learning was one of the top trends in the supply chain industry, and one of the top technologies, giving companies a leg up over their competition.

What is machine learning?
Machine learning is a complex technology. To put it simply, machine learning is the process of taking an immense amount of data and analyzing it to make smarter decisions. The goal is to save businesses money and generate greater profits, in part by better serving end customers. One example of machine learning in everyday life is image recognition. For instance, Facebook uses image recognition to identify and tag people in photos. When used in the supply chain, algorithms can be used to measure everything from weather patterns and rail schedules to SKU profiles and stock-out alternatives. Those hundreds or thousands of data sets can be compared and cross referenced to predict various outcomes, from inventory levels to estimated delivery, with higher accuracy than ever possible.

A recent study conducted by Deloitte showed that “more intelligent networks enable organizations to reduce the time between collecting data and take meaningful actions.” Only 40% of survey respondents said they are actively using AI, but those who are moving toward increased digitalization will outperform their competitors. For example, the use of IoT-enabled trucks is expected to reduce transit time by 50%. Supply chain today has become more digital. Today, supply chain companies and experts are looking at the big picture, they are thinking beyond the usual just to drive more fluidity and balance within their operations and in their processes.

Improved supply chain practices
A recent Gartner report has put Johnson & Johnson at No. 3 on its Supply Chain Top 25 List for 2020 for its ability to continuously improve supply chain practices to meet demands during the COVID-19 pandemic. The company scaled up its manufacturing to meet demand for ventilators, converted manufacturing lines to produce hand sanitizer and has taken on its own vaccine efforts. Today, businesses are looking to augment human plus machine workforces by integrating machine learning and other automation technology into supply chains. This they note, will help drive productivity and improve employee experiences. In fact, companies who are already on the path of technology adaption are already seeing dividends and plan to scale their artificial intelligence platforms in the coming years. During the pandemic, Accenture repurposed its SynOps platform to maintain real-time visibility into supplier inventory and automatically identify alternatives for its customers. Used as a procurement tool, the platform scrapes the web and uses machine learning and natural language processing algorithms to find suppliers that might be able to step in with missing materials or components. Data is the most powerful asset in the supply chain and integrating OMS, WMS and TMS with multiple data sets to help customers.

The new-age machine learning enabled supply chain will help customers decide which warehouses to stock with inventory basis the geoproximity to their biggest customer bases, warehouse capacity and required warehouse or transportation capabilities. With technology at their discourse companies will know how much inventory to carry and how to distribute it, what may be missing from (or extraneous to) their SKU profile and when they should reorder for optimal inventory carry costs and supplier bulk order discounts. Moreover, it also helps brands forecast demand based on historical sales and patterns of seasonality, since now they can track a majority of data to arrive at a more concrete point. This actually allows brands and retailers sharpen their inventory and customer approaches, allowing them to cater to the customer in a more pointed way.

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