Is your supply chain ready for the Artificial Intelligence revolution? Don’t lag behind, adapt your strategy!

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Exploring the intersection of AI and future of supplychain has become increasingly crucial for businesses worldwide. In this article,Thomas Ghyselen, senior business consultant at Ordina, delves into the potential impact of AI on supply chain management and share insights on how companies can leverage this technology to optimize their operations and stay ahead of the competition.

Artificial intelligence is transforming the way businesses operate, making them more efficient, effective, and cost-saving. Its application in the supply chain has the potential to revolutionize the entire industry and bring numerous benefits to organizations.

Supply chains are facing a series of severe disruptions in the recent years, leading to major challenges for businesses and consumers alike. These disruptions take many forms, including natural disasters, pandemics and political tensions and have far-reaching impacts on the global economy. The repercussions are magnified due to the cost-saving measures adopted by businesses in recent years to enhance efficiency, albeit at the expense of their resilience. As a result, when the subsequent disruption occurs, many companies are caught off guard.

Supply chain strategies

In response to these rather unpredictable disruptions, companies are rethinking their supply chain strategies. They are looking to diversify their supply chains, reducing their dependence on any region or supplier. They are investing in risk management strategies, such as insurance and contingency planning, to better prepare for future disruptions. Finally, they are exploring new technologies such as automation and artificial intelligence to improve the resilience and efficiency of their operations.

The abundance of data generated from various sources, combined with advancements in technology, have made it possible to build sophisticated machine learning and natural language processing algorithms, from which our customers have greatly benefited over the last years.

Food supplier

Consider one of our customers which is a food supplier for major international retailers delivering various meat products to warehouses worldwide. Every year when the first heat wave hits, the food supplier expects the local shoppers raid the retailers to pick up sausages, hamburger fixings and to kick off the first proper BBQ of the season with family and friends. To capitalize on increased sales opportunities and avoid financial losses, the food supplier should incorporate weather forecasts into their demand forecasting together with historical sales data, seasonal trends, and other relevant data points.

Additionally, food suppliers must consider the challenges associated with perishable products and factor in the potential for spoilage when ordering and stocking raw materials. Ultimately, accurate forecasting and effective inventory planning are essential to successfully navigate the impact of weather on retail demand. We demonstrated in this specific case how machine learning can discover patterns between weather circumstances, holiday periods and sales figures. Those patterns enabled a 20% more accurate sales forecast of weather sensitive products.

One of the key parameters in the supply model of the food supplier is the processing time of each step of the production process, including mixing, cooking, packaging and labeling. To improve the accuracy of their processing time estimates, machine learning algorithms are used to analyze historical production data and identify patterns and trends in processing times. By taking into account factors such as batch size, ingredients, and equipment settings, the machine learning model can generate more and more accurate processing time estimates for each step in the production process.

With more accurate processing time estimates, the company can optimize their production schedule to minimize downtime and maximize throughput. This can help to reduce production costs and improve the overall efficiency of the production process. Additionally, the more accurate processing time estimates can help the company to improve their production planning and scheduling, and to better anticipate delivery times to customers. This can help to improve customer satisfaction and reduce the risk of stockouts or delays.

Natural language processing

Another type of machine learning algorithm will soon become crucial in your operations, the advanced natural language processing (NLP) algorithms. The “Generative Pre-trained Transformer 3”, in short GPT-3, is creating quite a buzz lately through its interface ChatGPT. Natural language algorithms are sufficiently advanced to be integrated in the supply chain planning solutions to understand and respond to human-like language, making it possible for the AI to interact with supply chain professionals and provide real-time information of inventory levels, delivery schedules, changes in trends or seasonality of products, resulting in recommendations to assist the supply chain planners in their day-to-day decision making. 

We are convinced the opportunities of artificial intelligence are still highly underexploited. In the future, we can imagine a self-learning system which would use historical data on past disruptive events and their impacts on the supply chain, as well as real-time data on current events and market trends, to identify potential disruptions and simulate their effects on the supply chain.

The platform would use a variety of machine learning techniques, including regression analysis, decision trees, and neural networks, to model the supply chain and predict the impact of disruptive events. As new data becomes available, the platform would continuously update its models and refine its predictions, becoming more accurate and effective over time.

The system would also be capable of learning from the actions taken by supply chain managers in response to disruptive events, including their success or failure in mitigating the impact of those events. This feedback would be used to further refine the platform's models and recommendations, building in greater resilience and adaptability to future disruptions.