Machine Learning and AI in supply chain management
AI is taking over more industries around the world, and retail is no exception. Gartner, via its research, predicts that 70% of organizations will have operationalized AI architectures by 2025. Artificial intelligence is an important tool that can be used in retail operations. It helps optimize processes, avoid mistakes that a human might make, and anticipate future opportunities or challenges. Thus, it makes it easier for a company to prosper. This article focuses on how AI will change demand forecasting in the supply chain. Read on to find out how artificial intelligence algorithms are helping businesses grow.
What is demand forecasting in supply chain management?
Over the past few years, supply chain management has become much more difficult. Longer and increasingly interconnected physical flows reflect the growing complexity of product portfolios. Market stability problems highlighted by the COVID-19 pandemic and the war in Ukraine have increased the need for agility and flexibility. In turn, increased attention to the environmental impact of supply chains is regionalizing and optimizing flows. As a result, companies and stakeholders have begun to focus more on the resilience of supply chains in an even more rapidly changing market.
With all of this, when developing demand forecasts, it is important to remember the basics. The skills needed for this task involve many moving parts. Supply chain planning – demand forecasting – a form of supply planning used to determine the price of a product. Similarly, it focuses on competitive intelligence by analyzing suppliers and looking for patterns to predict future performance in an industry. As supply-demand volatility increases, demand forecasting must account for uncertainty while focusing on the goal of customer service.
Why is forecasting important in the supply chain?
Forecasting is a crucial element of supplier management, aiding in filling orders quickly, minimizing costs for inventory, and planning for price changes. We can highlight several key elements that explain why demand forecasting is such an important component of the supply chain:
- Planning and scheduling processes. The planning process is made much more precise by forecasting. Paying attention to past and current product demand keeps the supply chain at the appropriate, most profitable level.
- Seasonal changes in demand. They are equally important and benefit greatly from demand forecasting. With reliable data to support the forecasts being made, there is no room for guesswork.
- Predicting product demand in even the most specific situations. While no company can predict the future with complete accuracy, relying on patterns and making informed decisions based on past and present data will get a company as close to that goal as possible. In doing so, we must remember about customer satisfaction.
- Streamline the shipment of a certain number of products without downtime on the shipping line, without the risk of orders not reaching customers on time. Forecasting in the supply chain at the shipping stage makes it possible to adjust the appropriate number of workers packing the goods. We then prevent the occurrence of shortages in the manpower required to pack such a large number of shipments or having too many workers who are not needed at the moment. Forecasting avoids high labor costs.
- Price forecasting. Forecasting prices at the demand forecasting stage allows you to take the power into the hands of the company. The impact of price changes on a particular area of the supply chain can be predicted and addressed accordingly.
- Anticipating customer demand will result in orders with short lead times being delivered on time. It will also have the effect of increasing trust between the customer and the supplier, as well as the overall image of the brand and the positive experience of the end user.
Such opportunities, in turn, translate into a reduction and containment of inventory. Safety surpluses are held as a hedge in case demand for a product increases. However, thanks to forecasting, this additional resource is not needed.
Just-in-time demand forecasting allows products to stay in the warehouse for the shortest time possible, thus wasting less money than if items were to take up space in the warehouse for a longer period. This action frees up space in the warehouse, saves time, and, by minimizing storage costs, affects the final price of the product. The price becomes more attractive to the customer than the price offered by competitors
Artificial Intelligence driven supply chain management
Supply chain management is a complex process that consists of many different tasks. Forecasting demand for products is one of the most important ones. Demand forecasting helps companies to plan their production and marketing activities, adjust their inventory levels and react to changes in the market.
In the past few years, artificial intelligence has been used for forecasting demand in supply chains. AI-driven supply chain management systems are able to forecast demand more accurately than humans could do it in the past. This will change the dynamics of this field and may lead to new jobs being created. Before the stock of goods runs out, a new shipment can be ordered in time through a dedicated system, thereby automating and integrating inventory in a smart retail company in real-time.
Amazon, one of the world’s retail giants, is already using AI to better control its supply chain. At this point, it has already transformed the eCommerce business through free shipping and 1-day delivery practices. Currently, Amazon is working on a system using AI and machine learning (ML) to automate warehouse and drone delivery processes.
AI will transform demand forecasting in the supply chain
Indeed, AI is transforming the Supply Chain. We already know that AI-based supply chain management solutions are effective tools to help companies solve their problems. In fact, from procurement itself to sales, an integrated end-to-end strategy can handle the capabilities and constraints of all business departments. AI’s ability to analyze massive amounts of data, comprehend relationships, provide insight into processes and help make better decisions makes it a game-changing tool.
As a technology capable of self-improvement, AI has become a marker of modernity and, above all, of development leading to an increase in productivity and, consequently, the efficiency of implemented processes in retail.
The future of retail lies in AI technology. Applying AI and machine learning-based solutions to stores help retailers transform data into powerful new insights.
They result in better shopping experiences for customers through streamlined shopping, timely access to products, highly personalized experiences, and improved store layout. Likewise, retailers enjoy more accurate forecasts, inventory efficiency, less waste, lower costs, and an overall smarter business. AI provides an opportunity for rational and data-driven management of inventory levels.
Many companies today want to leverage AI to provide faster and more accurate solutions for their activities. However, making the most of these solutions possible is not just a matter of technology; companies need to take organizational steps to take full advantage of AI’s potential.
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