The vision of a future in which food manufacturers are able to adapt to changing consumer preferences, minimize waste, optimize supply chains and ensure excellent product quality, is becoming more and more realistic. Through the use of artificial intelligence, data analysis and advanced forecasting algorithms, food manufacturing companies now have an extraordinary opportunity to achieve these goals.
How do sophisticated AI and Machine Learning algorithms contribute to revenue growth and cost optimization in the food manufacturing industry?
Fast-moving and fresh products
In the food production industry, fast-moving and fresh products are a kind of a backbone, a driving force for companies’ success and competitiveness. However, managing demand for products that are subject to time constraints has unique challenges.
Demand forecasting based on artificial intelligence and Machine Learning allows the use of advanced algorithms that analyze historical data, taking into account seasonality, consumer trends, weather forecasts, holidays and special events, and other factors affecting demand. The models are able to analyze huge amounts of data, detect patterns and trends, and then generate accurate forecasts based on them. By constantly learning from new data, the models are getting increasingly better at predicting demand for fast-moving and fresh products.
Thus, the strategy for fresh products, which have short shelf lives and are highly perishable, is optimal production line replenishment. For these products, the key is to deliver them to customers in the freshest possible condition while ensuring minimal waste.
With optimal production line replenishment, food manufacturers can accurately adjust their production processes to actual market demand. In practice, this means that they can avoid overstocking that could result in reduced quality and increased risk of product spoilage. On the other hand, they minimize the risk of product shortages that could lead to losing customers and a chance to increase sales.
Optimal production line replenishment also allows for better supply chain management. With accurate demand forecasts, manufacturers can plan the deliveries of raw materials and supplies needed for production in a way that ensures continuity of supply and minimizes production downtime. This strategy gives manufacturers more control over the production and sales process, allowing them to use resources efficiently and minimize costs.
Automatic generation of orders to suppliers and production orders
Speed, precision and efficiency in logistics processes and supply management play an equally important role in achieving success. In this context, there is increasing talk of support from artificial intelligence to introduce innovative solutions and improvements in generating automated orders to suppliers and production orders.
By using AI and ML in their daily duties, food manufacturers have the ability to analyze vast amounts of data regarding demand, market trends, raw material availability and many other factors. This makes it possible to automatically, yet accurately, forecast demand for fresh produce, allowing for optimal replenishment of goods required for production.
In addition, automatic production orders are generated in the next step. Advanced algorithms analyze the availability of raw materials, the efficiency of production processes, as well as costs and delivery schedules. On this basis, decisions are made on the quantities of raw materials to be ordered, the optimal production schedule and lead times.
As a result, food manufacturers can optimize their production processes, while minimizing waste and losses. They also have greater control over those processes and can perform accurate replenishment and generate production orders based on actual market needs. As a result, they can deliver fresh and high-quality products, while meeting customer expectations and increasing customer satisfaction.
B2B, B2C and export sales
In today’s global business environment, FMCG manufacturers face many challenges in export, B2B and B2C relationships. In this context, artificial intelligence has great potential to contribute to better collaboration in these areas.
AI, for instance, enables manufacturers to generate accurate demand forecasts for specific markets. By analyzing historical data, systems learn the relationships between various factors and product demand. Based on these patterns, demand forecasts for specific markets are generated, taking into account the individual characteristics of each market. Manufacturers can thus adjust their export plans, focusing on markets with greater sales potential and minimizing the risk of overstocking or product shortages.
Such forecasts can be generated for each sales channel and market separately, especially if the specifics of sales differ across channels and markets. This allows capturing overall trends and ignoring outliers. Sometimes it is also beneficial to combine some markets into one group, if the sales specifics for each market are the same or the sales volume is still small (emerging market).
Importantly, when establishing processes, there are three aspects to consider – what, where and when. This means breaking down what is projected (SKU, product category), where (sales channel, sales market) and when (granularity, horizon, frequency).
The responsiveness of artificial intelligence and ML is also an important aspect of demand forecast generation. With continuous data analysis and updates, it is possible to adjust forecasts in real time, taking into account changing market conditions and demand drivers. This enables manufacturers to respond quickly to changing trends and consumer needs in different markets, resulting in greater competitiveness and export success.
Machine Learning algorithms are also the foundation for generating long-term forecasts. With advanced machine learning algorithms, data analysis and predictive modeling, it is possible to analyze huge amounts of information and generate forecasts for a longer time horizon.
Such long-term forecasts bring many benefits to FMCG manufacturers. First, they enable better production planning and management. They allow them to anticipate changes in the market and adjust their production capacity to meet demand. In this way, the manufacturer avoids overstocking or product shortages, leading to more efficient use of resources and minimization of costs.
Moreover, and importantly, it is possible to generate such forecasts for a variety of product and time combinations. After taking into account certain business rules and depending on the forecast horizon, forecasts are made by sales channel, sales market, as well as for individual SKUs. For weekly forecasts, the forecast horizon is usually six months, and for monthly forecasts – 12 months. Of course, this is determined on a case-by-case basis and depends on the needs of the manufacturer, taking into account business strategies, production plans or frequency of stocking.
Ultimately, then, long-term forecasts generated using artificial intelligence can help make strategic decisions. Knowledge about the anticipated demand enables manufacturers to plan investments, develop new products and expand into new markets with greater confidence and certainty of success.