A supply chain manages the movement of various supplies and inventory stock within a business. This involves planning, control and execution of daily supply chain activities and the storing of inventory stock. All with the goal to reduce waste, improve quality and to increase customer satisfaction.
Machine learning is a specific technology where a computer system learns useful information from gathered data. The greater the data entered into the machine learning system, the more intelligent the system will become. Over time the information generated becomes more adaptable and easier to interpret.
Many of us are already experiencing machine learning daily; it makes recommendations on our social media platforms, predicts text on our mobile phones and determines what is spam before it hits our email inbox.
Demand forecasting is one of the more difficult and demanding aspects of supply chain management. Managers have difficulty understanding market fluctuations, shifting trends and interpreting masses of complex consumer data. This hampers their ability to accurately forecast market trends and consumer demand to produce precise calculations and useful solutions.
In today’s dynamic and complex multichannel business environment, it becomes increasingly difficult to create reliable demand forecasting models for supply chains. Many forecasting techniques produce disappointing results because the they rely on traditional models that are not designed for continuous learning.
Using machine learning, businesses can develop robust algorithms that learn from market scenarios to create predictive and dynamic models. This results in real-time information that helps a supply chain to forecast efficiently and manage the information correctly.
Technology that provides forecasts months in advance, can be used to help determine supply chain availability and demand. Combining these analytics with current inventory stock information can help supply chain managers to make better strategic decisions for individual retail locations.
Seasonality is a major determinant to what, when and why consumers purchase certain goods. Sporting activities, holiday events and even what we eat, are all linked to the recurring fluctuations that happen throughout the year. Even the weather seasons will affect our purchasing decisions.
Companies can implement machine learning to optimise their entire supply chain in order to best meet the needs of seasonal demand. Predictive weather analytics from machine learning, will even create scenarios of likely weather conditions that not only influence what consumers buy, but also where and when they shop.
There is considerable pressure on supply chain managers to accurately evaluate and manage supply chain risks. Data collection, inspection and analysis is becoming a necessary part of supply chain risk management.
With the help of machine learning however, that data wouldn’t need to be inspected to guide risk forecasting. Out-dated data storage is transforming into information creation. Intelligent analytics now means the data can forecast itself and these predictive analytics are effective tools for evaluating and managing supply chain risks.