||Sri Lanka : Using artificial intelligence for supply chain planning
||Supply chain planning and optimization, including demand forecasting, are among the key areas where AI is already beginning to be deployed. Experts say that global supply chains have become so complex and are affected by so many variables, that AI may be essential to help identify and predict problems and potential solutions.3
Supply chain managers must take into account more data than any person can possibly process, or Most companies simply do not understand the full depth and breadth of their supply chain risks, and are therefore not prepared to respond efficiently or effectively to the many potential disruptions. The inherent complexity of global supply chains, along with the dramatically increased volume of data, make it almost impossible to extract all the necessary insights and make informed business decisions. And the volume of data continues to increase, in part due to the trend to connect supply chain management devices to the Internet.
Accordingly, companies are already applying AI-based machine learning to automatically analyze vast amounts of supply-chain management data, identify trends, and generate predictive analytics the ability to predict problems and outcomes, that the benefits in global supply chain management include reductions in forecasting errors. Software solutions are beginning to apply machine learning capabilities that can automatically detect errors and make course corrections, while processing real-time data streams, with companies collecting mountains of data that can be used to train algorithms to learn where things went wrong, we are at the tip of the iceberg of how much companies will leverage these capabilities. For example, some supply chain management solutions use AI to gather and correlate external data from many sources, including social media, newsfeeds, weather forecasts and historical data.
As an example, one major food manufacturer used an AI-based demand forecasting solution to tackle a common problem: meeting customer demand while minimizing inventory. The challenge was complex, involving around 10,000 different products, each subject to variation in demand. By applying predictive analytics, the company was able to more accurately anticipate customer behavior by integrating the impact of promotions and other special offers into its statistical models.
A restaurant can be a complex business to run. Anticipating demand to order the right amount of ingredients at the right time, and handling it all manually for a business with notoriously thin margins to begin with typically constitutes a significant challenge even in the best of times. One restaurant chain decided to take advantage of advanced technology to gain a deeper line of sight into demand, and learn to plan better. The restaurant chain used machine learning and artificial intelligence (AI) tools to analyze point-of-sale data.While this process started out largely manual, the system was able to recognize patterns in the data quickly and learn from them, enabling the restaurant to move toward a fully automated planning process.
Rather than forcing planners to predict based on the information they had on hand at the moment, the restaurant chain used a seamless flow of current and historical data to begin to sense, anticipate, and even forecast demand and plan accordingly. The restaurants staff enjoyed reduced workloads, but the results also cascaded backward through the supply chain. Suppliers were able to plan more accurately, resulting in less waste, greater efficiency, and improved flexibility throughout the network.
Supply chain planning has always been a data-rich, analytical process. But as linear supply chains evolve into interconnected digital supply networks (DSNs), powered by advanced technologies and interconnected systems, the way we think about supply chain planning could fundamentally shift.
What is synchronized planning
Synchronized planning generally describes a state in which a constant flow of data from throughout the supply network enables organizations to accurately plan production to match actual demand. In an interconnected DSN, this filters across to other nodes, enabling suppliers, logistics, and fulfillment to more accurately plan and, ultimately, take action to provide the resources when and where they are needed. The result is a more dynamic, flexible, and efficient capability that combines traditional planning and execution.
Organizations have typically used historical data to forecast future demand; however, in the absence of a broad pool of information, planning often was somewhat based on conjecture and could not account for unexpected shifts, from demand fluctuations to weather. The dynamic and integrated nature of the DSN, however, can lead to even more complex planning demands and what we think of today as planning could fundamentally change: Different business functions should be fully integrated with each other as well as with an ecosystem of suppliers, customers, inventory, and production to drive the strategic initiatives of the organization. In short, planning synchronization is important to the success of the network.
Fragmentation of production. Global manufacturing does not affect just the smart factory but also customers. As production has become more interconnected and global, planners should account for the procurement and inventory demands of multiple physical locations each specific to local fluctuations in demand and supply, as well as the local availability of inputs. This fragmentation can bring new complexities and ever more data points to consider and adapt to, and can strain traditional planning to the breaking point.
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