artificial intelligence transforming the phase of supply chain management
TRANSCRIPT
Artificial Intelligence: Transforming The Phase Of Supply Chain Management
SYNOPSIS
• Introduction• Artificial Intelligence In Supply Chain• The Fusion Of AI In SCM• Inventory Control And Planning• Transportation Network Design• Purchasing And Supply Management• Conclusion
INTRODUCTION
• Professionals associated with logistics and supply chain are always on their heels to shape the operational chain innovatively that address the challenges more efficiently and minimizes the risk that caused otherwise.
• When the professionals hunt for new possibilities, technology is always there for help! Although the concept of Artificial Intelligence is six decades old, it is well on its course to take over the lives of people slowly by making it easy and efficient.
• Most of the businesses take interest in integrating AI into various processes as it is capable of performing various tasks efficiently within a short span. And use of the AI is not just limited for corporate, but also seen in various industries.
Artificial Intelligence in Supply Chain
• According to the Accenture digital operations survey, organizations are rapidly digitizing their supply chain to distinguish and drive revenue growth.
• The report also says that 85% of businesses integrate AI in their supply chain by next year.
• The main objective behind integrating AI is that it plays a vital role in optimizing business process and establishes agile supply chain.
• Assimilating Artificial Intelligence (AI) in Supply Chain eventually results in redefining an ecosystem where supply-chain link themselves to generate impeccable flow of products and information end-to-end.
The Fusion Of AI In SCM
• Artificial Intelligence is always used to solve complex problems.
• The sub-disciplines of artificial intelligence such as GA’s and expert systems are employed to address the complex issues of Supply Chain Management.
• It involves inventory management, location planning, purchasing, freight consolidation and routing or scheduling problems.
• Further, in this blog, let us outline the significant areas of supply chain management that have been explored for AI, identify the specific sub-disciplines and assess their involvement in supply chain decision-making process.
Inventory control and planning
• The inventory in supply chain represents the idle resources that are essential to maintain excellent customer service.
• According to the survey conducted by the Timme and Williams-Timme, maintaining a single unit of inventory may costs approximately 15% to 35% of the product value. Thus, the organization’s success in the highly competitive market often hinders its ability to control and plan its inventory efficiently.
• Aforesaid challenges can be successfully addressed by accessing the real-time information about the customer demands, size and type of the inventory and the estimated time to fulfil customer orders.
• The Expert System (ES) is capable of capturing the inventory patterns throughout the whole supply chain at all layers, in detail.
Transportation network design
• Transportation offers it’s own set of challenges including TSP, vehicle routing and scheduling problem, minimum spanning tree problem, fright consolidation problem, and to name a few.
• In specific, due to the combinatorial nature of these challenges, the GA, a sub-discipline of AI, turns out to be the most popular technique integrated to address aforesaid challenges efficiently.
• Besides, another popular technique i.e. “ant colony optimization of algorithm” of AI has emerged to address the transportation network design challenges such as TSP, vehicle routing and the minimum spanning tree problem.
Purchasing and supply management
• Business organizations should analyse make-or-buy decision at each step.
• Although the make-or-buy decision is simple and straightforward, it is filled with various “What-if” scenarios off screen. Some of the common scenarios are;
• What volume of goods should company produce?• How much capital investment is required to produce the goods?• How much risk is involved in producing new products or innovating technology to
stay competitive in the market?• Has the product become successful in the market and drive-in more profits?• Will customer feel satisfied?• Does the company employees have the expertise to produce goods that the
customers desire?
• Expert System (ES), a sub-discipline of AI, which helps purchasing manager to evaluate the performance of prospective suppliers, optimize the information exchange amongst purchase personnel and reduces the time taken to make the make-or-buy decision.
Conclusion
• Aforesaid examples clearly represent that in today’s dynamic world, embedding artificial intelligence into supply chain offers a competitive advantage.
• The AI is completely armed with predictive analytics that can analyse cluster of data collected through various sources.
• Analysing these data help companies to develop an efficient form of supply chain management.
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