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How AI and Automation are Impacting the Supply Chain

July 26, 2022
Read Time 3 mins

By Tim Proome

The supply chain (SCM) has evolved dramatically since the global COVID-19 pandemic sent the world online, and users adopted online ordering at an unprecedented pace. Now, in the aftermath, this shows no signs of slowing down, and the volume, combined with ongoing economic pressures, is forcing businesses to rethink their supply chain operations.

Many forward-thinking entities are investing heavily in machine learning (ML), artificial intelligence (AI) and automation tools, to help address faster inventory churns and to manage misalignments that have happened due to shifts in customer patterns. After all, poor visibility into the supply chain leads to delays and higher costs, which, in turn, translates into lost sales and a negative effect on the bottom line.

Every business with a supply chain is now focusing on aligning this with their multi-channel customer expectations.

Leading organisations have leveraged advanced data analytics to be able to respond quickly to shifts in the marketplace and lower their logistics expenses. This, combined with AI, ML and automation, is boosting the efficiency of supply chains and enabling the business to focus on core activities by automating supply chain decisions.

Supply chain operations across the board are looking at employing AI-based models to more accurately forecast demand, enhance distribution centre throughput, manage supply, and streamline delivery. In fact, AI is becoming a crucial element when it comes to creating the operational agility that is needed to rapidly respond to market opportunities and challenges.

This is why supply chain solutions that are harnessing the power of automation AI, and ML are helping retailers, manufacturers and other supply chain owners lower costs, and make the full supply chain, from warehouse operations to shipping and other logistics functions, efficient and seamless, which ultimately fuels customer satisfaction.

Take demand forecasting as an example. In the past, this function depended heavily on batch processing which used to take a significant amount of time because data was often incomplete. Here, AI is driving an excellent return on investment, by ensuring these fore-casts are up to date, and highly accurate. This allows businesses to rapidly adjust sales forecasts based on trends, customer behaviour and all the other variables, to guarantee that inventory levels are optimised, and products are available to drive sales.

Simply put, the faster the model, the quicker the organisation can make decisions that properly align with its procurement and delivery systems.

Similarly, companies looking to boost efficiencies across their supply chains can use real-time demand data and graphics processing units that are powered by ML to enhance sales forecasting by having the right figures at their fingertips. This is a game changer which can completely transform the performance of the supply chain, from purchasing to warehouse transfers, production planning and logistics.

Moreover, demand forecasting plays a key role in any industry that depends on quick service, by helping to regulate production levels to eliminate waste and increase margins. Entities in this vertical are employing advanced analytics to predict customer flow, set production levels more accurately, and optimise stock levels.

Looking at the retail industry, in particular, consumer demand for omnichannel experiences, online shopping, pavement-side pickup, in-store pickup, same-day fulfilment and more, has put a lot of pressure on distribution models, which has seen these companies turn to automation solutions.

Automated solutions such as micro-fulfilment centres involve making use of small, highly automated storage facilities that are close to the end customer to lower the cost and time it takes to deliver goods. These centres are made up of two key parts, software management systems that process online orders and physical infrastructures such as robotic pickers and self-driving vehicles. AI supports the software side of things, with advanced product recognition and navigation technologies that learn as they operate.

To address the needs of pick-and-ship and packaging operations that depend on fast visual inspections of items moving on conveyor belts, businesses are also harnessing the benefits of computer-vision-based visual inspection, which enables manufacturers to auto-mate product defect detection, which saves time and money while enhancing quality control.

Another way in which supply chains are looking to lower shipping and logistics costs is by utilising advanced “sortation” solutions that cut the costs of carrier handling. By implementing automation within the process of sorting to deliver goods to consumers’ homes, and concurrently optimising delivery routes for logistics providers, results in quicker delivery times, more successful deliveries, greater accuracy and less last-mile costs. AI, for example, can be used to predict delivery times and monitor the impact of any potential hurdle, enabling businesses to react quickly and choose alternate delivery routes.

All in all, if we look at today’s economic climate, and the challenges businesses face, the demand for better supply chain efficiency becomes clear. And it isn’t slowing down any time soon. Customers want omnichannel experiences and will turn to the providers that offer them. There has never been a better time for organisations to rethink their supply chains, and make them more intuitive, agile, and smarter

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