Machine learning in the supply chain
As companies become more globally integrated, their supply chain must become a key competitive differentiator. To maintain their competitive advantage organizations must be able to overcome new challenges quickly, while keeping associated risks and costs low.
For a company’s supply chain, improving inventory management, increasing productivity, improving distribution management, and reducing waste are key areas to optimize. Usually, getting better at forecasting, refining resource management and sales forecasts, monitoring manufacturing processes, as well as analyzing internal and external factors (e.g. traffic data and weather patterns) are ways in which the supply chain optimization is accomplished. However, machine learning (a type of artificial intelligence) is set to revolutionize the way supply chain activities are conducted.
Big data is one of many buzzwords around the industry today, and machine learning is changing the way we can benefit from big data. By utilizing machine learning with the existing data sets as well as adding new data over time, a machine can learn from the data in order to provide deeper insights into the collected data and potentially find hidden patterns that human analysts might never discover. Artificial intelligence needs significant volumes of data to display its full potential, but this also means that the machine learning tools will be able to provide better predictions and estimates of future results.
Technology is changing the way companies do business, and machine learning has the potential to take a business to the next level. When it comes to supply chain, three areas where machine learning will make a big impact are: operations planning, warehouse management, and supplier selection and supplier relationship management (SRM).
Supply chain planning is all about balancing supply with demand in order to achieve alignment with corporate strategy at known cost and risk. Using machine learning and intelligent algorithms, the agility and optimization of decision making within a supply chain will be revolutionized by continual analysis. The output would be a recommended, or even automatically executed (requiring no human analysis), plan to meet customer requirements through optimization driven by such data inputs like facilities and their capacities, effectiveness of transportation lanes, customer requirements, and other parameters of success.
In many industries, companies are innovating and leveraging robots to complete tasks that normal workers have historically been assigned to accomplish. Warehouses are becoming more automated, and robots are picking goods to prepare to send them to customers. In the most automated warehouses, robots currently account for up to 70% of the work and can move up to 500 kilograms. They are typically fitted with sensors to avoid collisions with each other, and they are connected to each other (like an industrial internet of things or IIOT) and the warehouse management system by Wi-Fi. The machine learning algorithms that run these robots are improving productivity and efficiency within the warehouse, as well as reducing the risk of human error.
With the help of machine learning, passive data gathering is becoming more active. By generating data sets from supplier relationship management actions, algorithms can continually adapt and make better recommendations when it comes to supplier selection. Typical inputs may include supplier assessments, audits, and credit scoring, as well as any other parameters that are set by the user in order to find the best scenario for picking the company’s suppliers and risk management, while also enhancing the supply chain sustainability.
Machine learning is driving innovation, as it is now making it possible to analyze today’s supply chain as a whole by using algorithms that are capable of analyzing the success of the chain and identifying areas of improvement. These algorithms are capable of forecasting demand and are able to identify errors quickly, effectively, and more efficiently than humans can. When utilizing machine learning, a company can make an impact in its inventory levels, supply and demand, production planning, quality, and transportation management.