Demand Forecasting to Optimize Supply Chain Planning

The supply chain is exactly that—a chain full of interconnected links, all dependent on one another. Each piece needs the one before and after it, but also those further removed from its position, since the breaking of one link causes the whole chain to break.

As a result, the proper functioning of the supply chain is directly linked to the performance of every level that makes it up, from start to finish. Production, warehousing, purchasing, distribution—and even the companies it interacts with as suppliers or customers (the extended supply chain)—all need to work together in a continuous flow of activity. In this scenario, an error at any point is passed linearly on to the rest, just as when you shake one end of a rope, preventing efficient management of the whole. Technically, this action-reaction effect is known as the ‘bullwhip effect.’ A small variation in demand can generate significant imbalances throughout the entire supply chain.

Any organization must therefore function as a whole, coordinating its own production or assembly times, warehouse stock, inbound and outbound logistics network, supplier delivery times, promotional campaigns, and many other inputs. This requires the use of planners to align the actions of all departments involved in the supply chain, each with its own interests and objectives.

A still all-too-common reality is that the Sales department strives to meet its own targets without taking into account what’s happening in Production, which in turn works toward its own goals without factoring in the activity of Purchasing or Maintenance. The different departmental realities within a company have traditionally been reconciled—without much success—through long, tedious meetings, spreadsheets, and pivot tables, which are no longer effective in a volatile, demanding environment, with increasingly complex supply chains shaped by fast consumption, constant promotional campaigns, and the ups and downs of an economy subject to multiple disruptions.

From Demand Forecasting to Demand Prediction

The solution has arrived by way of algorithms and Artificial Intelligence, taking the step from demand forecasting to demand prediction . In the data era, new disruptive technologies use all the information available within an organization, process it, generate real-time reports on current needs and future projections, and provide recommendations to teams, making it easier to make decisions and increasing levels of efficiency and profitability.

Consequently, the first step in this process is gathering the raw material—the existing data, whether structured or unstructured, from ERPs, warehouse management systems, CRMs, or systems based on the Internet of Things (IoT).

Once internal information has been compiled (sales records, turnover rates, cross-reference outflows, stock levels, etc.) and contextual data has been added (weather, working calendars, promotional periods, etc.), it is processed by dynamic predictive algorithms that forecast future demand. This makes it possible to carry out proper planning for the supply chain as a whole, viewing it as a single entity rather than basing decisions on the objectives of each individual part.

In other words, demand prediction systems, unlike those based on demand forecasting, provide an early view of possible market behavior, based on real-time data analysis. This enables efficient planning of all operations (purchasing, production, transportation, marketing, sales, etc.) and minimizes the risks inherent to any business activity, resulting in greater efficiency, profitability, and service quality.

If we know that we’re going to sell N units of a particular item, X of another, and Y of a third, we can determine the raw materials required, the production we need to carry out, and the necessary stock levels. We can also optimize fleets, tailor marketing campaigns, adjust human resources, and even fine-tune pricing.

Furthermore, by using Big Data-based prediction systems in business management, we eliminate non-objective factors from decision-making (experience, intuition, etc.), basing business strategy instead on accurate, objective information processed by precise algorithms—free from individual perceptions.

Conclusion

The supply chain is highly complex, as it is made up of a multitude of business areas that, in most cases, operate individually, each with its own perceptions, outlooks, and objectives.

Using demand prediction systems in any type of organization offers enormous benefits for the supply chain. Having an estimate of future market behavior makes it possible to design a strategy for the entire supply chain, tailored to the estimated actual requirements of customers.

With a shared goal and common planning, the focus shifts from individual parts to the whole, resulting in greater efficiency, profitability, quality, customer service, and operational performance, while minimizing costs, risks, and unproductive time, among other things.