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3 major AI and ML Trends in Supply Chain Management | Sustainability and Profitability

Supply chain management is a complex and ever-evolving science. Several developments changed the technological landscape of supply chain management with new offerings. In this article, we will dive into their impact on sustainability and profitability.


Manual forecasts are outdated and often fail to cater to retail's current needs. Inaccurate forecasting and firms that lack data analytics capabilities for forecasting are several steps behind from the start. The digital transformation of firms has been booming since the pandemic in 2020, showing no signs of slowing down since then [1]. Especially firms in the retail sector are having to double down on technology investment or they will be left behind [2]. Additionally, there have been many new entrants in the supply chain management space, including leading cloud service providers, indicating that supply chain management is at the brink of a new technology era.


Studies show that companies with highly rated environmental and social values have enjoyed operating margins 3.7 times greater than companies with lower rated environmental and social values [3]. For many firms, efficient supply chain management is the key to reducing their footprint and becoming more sustainable and profitable. These three examples of artificial intelligence (AI) and Machine Learning (ML) trends are altering the way firms operate their supply chain and manage their profits and environmental impact.


1. Demand Forecasting Technology

Demand forecasting technology has become more accurate and more widely adapted due to novel technology that uses AI and ML algorithms. These algorithms help to develop accurate, flexible and scalable forecasts which can be immediately used by decision-makers. It will not be long before the norm is to use smart technology for demand forecasting.


Improved forecasting and demand planning can enable firms to optimize production and inventory levels. More accurate forecasting will reduce waste and improve the efficiency of the supply chain, leading to increased sustainability and profitability. If a company does not have the capacity to build these AI and ML tools itself, it can turn to a technology partner. Finding the right technology partner is key for firms to optimize their forecasting. To make the most of a forecasting solution, the technology partner ideally understands the retail sector in general, the business in particular and the necessary nuances for data cleaning, design, development, testing and implementation of an AI model. The algorithms then learn and improve over time, increasingly optimizing forecasts. Among other things, this helps retailers to reduce waste, overstock and the resulting steep markdowns.


2. Planning and Replenishment Technology

After an accurate forecast is made, the real work begins. A plan must be for all supply chain stages and the various sales channels. Replenishment is the movement from an upstream point in the supply chain to a downstream location where sales are made. Be it stores, or warehouses which hold stock for E-commerce. Following the proper demand forecast, it is imperative that the produce is in the right location at the right time to prevent unmet demand and lost sales. Technology can help in the following three ways:

  1. Integrated planning across all supply chain stages and covering all sales channels

  2. Accurately handling large amounts of data

  3. Advanced real-time insights into the performance of sales channels and products.

Omnichannel retailing is starting to become the standard and having all-encompassing planning is essential to optimize this process. Retailers often have a long and complex supply chain; this is made even more complex by having multiple sales channels. Currently, planning and replenishment often involve endless excel documents which are sent from team to team until a ‘final’ version is delivered to decision-makers. The perk of AI-powered planning and replenishment technology is that the planning can be integrated across all supply chain stages (distribution center/store) and can cover all sales channels. This gives decision-makers a bird's eye view of their supply chain to make the best decisions.


Accurately handling large amounts of data is vital for smooth-running supply chain management. Manual replenishing and planning is a cumbersome process sensitive to human error which can lead to many last-minute alterations and sleepless nights for the supply chain team of retail firms. Smart replenishment technology processes large amounts of data quickly and accurately; it can detect subtle changes which are hard to identify for the human eye.


The combination of integrated planning, accurate handling of large data quantities and real-time insights allows retailers to make fact-based decisions. This, in turn, leads to better supply chain management as retailers can optimize transport and store assortments leading to a higher profitability and sustainability level.

Inventory Management Application

3. Warehouse, Distribution Centers and Production Technology

AI and ML are also becoming more prevalent in warehouses, distribution centers and on the production floor. The novel technology in these places has allowed for a further optimized supply chain where there is less room for human error.


AI and ML can also help with predictive maintenance along the supply chain. For instance, by calculating when machinery is likely to fail or needs maintenance. Overall, these developments can help firms take proactive steps to prevent disruptions and downtime. This increases the reliability and availability of equipment and products, leading to improved sustainability and profitability.

Order picking is the process of selecting the correct items from a warehouse and packing them for shipment. This is a crucial part of the fulfillment process and can be time-consuming and error-prone if done manually. AI and ML can be used to automate and optimize order picking in distribution centers in several ways. For example, AI-powered robots and automated conveyor systems can be used to pick and transport items from storage to the designated packing area.


These systems can be trained using ML algorithms to improve their accuracy and efficiency over time. In addition, AI and ML can be used to optimize the order-picking process by analyzing data and identifying patterns and trends. This can help to improve the layout of the warehouse, reduce the number of steps required to pick an order and minimize the risk of errors. Overall, the use of AI and ML in order picking can help to improve the efficiency and accuracy of the fulfillment process in distribution centers, leading to faster and more reliable deliveries to customers.


Conclusion

Supply chain technology is a continuously evolving science. Due to the interconnectedness and global volatility in the field, more factors than ever before must be taken into consideration for an effective end-to-end supply chain. Fortunately, decision-makers can turn to AI and ML for data-driven guidance in forecasting and replenishment, a smart move for both profitability and sustainability.



[1] The Top 5 Technology Challenges In 2023 (forbes.com)

[2] Six key trends impacting global supply chains in 2022 (KPMG Singapore)

[3] Why all businesses should embrace sustainability & how they can do it. Knut Haanaes & Natalia Olynec. May 2022. (imd.org)


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