October 03, 2023 | Supply Chain Software
It is known that efficient procurement is dependent on supplier and spend data. A company with historic sales data can obtain a demand pattern for its products. But this is not always the case.
Demand is not easily predictable. Today, it not only depends upon seasonality, supply chain efficiency and the geopolitical landscape. It is also affected by how people talk about the business online – making it volatile in nature.
However, technology is allowing organizations to make near-accurate forecasts. The use of machine learning in demand prediction has revolutionized data availability for precise sourcing and resource efficiency. This goes beyond waste reduction, providing a competitive advantage by enabling proactive supply adjustments.
Modern systems can run multiple algorithms simultaneously, vastly improving forecasting accuracy.
Demand sensing now considers weather events, economic indicators, and public sentiment shifts, offering businesses rapid insights to adapt.
When machine learning (ML) is applied to demand forecasting, it goes beyond historical data and incorporates various internal and external components, significantly enhancing the accuracy of demand predictions.
Moreover, with increasing connectivity among businesses, there is a wealth of data visibility. Therefore, manufacturers are embracing ML, cognitive computing, and real-time data from Internet of Things (IoT) sensors.
One of the key advantages of using machine learning in supply chain forecasting is its ability to excel at capturing complex patterns and relationships within historical data. Unlike traditional methods, ML algorithms dig deep into the data, identifying seasonality, trends and non-linear dependencies.
Moreover, these models can incorporate a wide range of variables and data sources, including historical sales data, market trends, social media sentiment and economic indicators. This results in richer insights and more accurate predictions.
Enhanced forecasting accuracy has far-reaching benefits.
It means fewer instances of underestimating or overestimating demand, reducing the risks of stockouts or excess inventory. This, in turn, leads to cost reduction, improved working capital management, and ultimately, better customer service. Customers get what they want when they want it.
Accurate demand forecasting by machine learning allows companies to optimize their inventory levels effectively. Traditionally, businesses have grappled with overstocked or understocked situations, both of which are costly and inefficient.
However, ML-driven forecasts provide the precision needed to strike the right balance. By reducing overstock situations, organizations can free up capital that would otherwise be tied up in excess inventory. This not only leads to cost savings but also enhances working capital management, allowing businesses to invest in growth initiatives.
On the other hand, avoiding understock situations ensures products are available when customers demand them, preventing potential revenue losses and customer dissatisfaction.
Supply chain dynamics are constantly evolving, with new data pouring in from various sources. Machine learning models can be designed to continuously update forecasts as new data streams in. This dynamic approach ensures that forecasts remain accurate even in rapidly changing conditions, such as sudden market fluctuations or unexpected events like natural disasters.
Real-Time updates empower companies to make more informed decisions about production, procurement, and distribution. It allows them to adapt swiftly to changing circumstances, reducing the risks associated with outdated forecasts. This agility is a significant advantage in today's volatile business environment.
The bullwhip effect is a notorious problem in supply chain management. It refers to the phenomenon where demand variability increases as you move up the supply chain. This often leads to inefficiencies and increased costs as supply chain stakeholders struggle to synchronize their activities.
Machine learning helps reduce the bullwhip effect by providing accurate and synchronized demand forecasts to all supply chain participants.
With improved coordination and planning among suppliers, manufacturers, distributors, and retailers, the likelihood of overproduction or shortages diminishes significantly. This results in substantial cost savings and smoother operations throughout the supply chain.
In the age of data-driven decision-making, machine learning has emerged as a game-changer for supply chain forecasting. Its ability to enhance forecasting accuracy, optimize inventory, provide real-time updates, and reduce the bullwhip effect contributes significantly to more effective demand forecasting and overall supply chain management. As businesses continue to embrace the power of ML, they position themselves for greater success and competitiveness in an increasingly challenging marketplace.
Partner with GEP to leverage ML with your supply chain forecasting efforts.