Utilization of AI in Supply Chain Mangement Case Study Solution
Complex Patterns of Parameters
Supply chain is a bunch of various different activities which have different patterns and require huge attention to be managed. For example, under a job order manufacturing system, job orders are often seasonal and require a potential attention about the expectation of various job orders in order to fulfil a job order at its time. On the other, machines are utilized consistently at all the time despite of a job order. The differences in these parameters lead to a complex supply chain.
Synergies in Parameters
All the activities and parameters under a supply chain are interconnected with each bother and impact one another. The interconnection between the parameters results in more complex supply chain. The higher the interconnection would be, the higher the supply chain would be complex.For example, the inventory levels impacts the amount of finished goods and production level, which affects the utilization of machines available. If the inventory levels are low, production levels would be low and the machine utilization also would be also low, which leads to higher costs and low revenues.
Difference in Parameter Relationships
Another factor increasing the complexity of supply chain is the difference of inter connections between the parameters. The relationships among two parameters could be direct or indirect. Under a direct relationship, increase in efficiency of one parameter results in increased efficiency of other. For example, availability of substantial inventory levels result in efficient production levels and utilization of machine.On the other hand in an indirect relation the efficiency of one parameters is decreased due to increase in the efficiency of other. For example, increase in inventory levels increase efficiency of production and machine but decrease the efficiency of warehousing, which focus on reducing inventory levels.These complex interconnections between the supply chain variables result in complex supply chain.(Deshpande, 2011)
Dynamic Nature
One of the major factors leading to a complex supply chain is the dynamic nature of supply chain itself. Due to lack of potential information regarding the nature of product demand in the market, the nature of supply chain becomes more dynamic. One cannot predict long termactions and projections under a supply chain due to its dynamic nature. The dynamic nature of the supply chain forces the organizations to keep their supply chain more flexible to meet the unexpected demands of their customers efficiently.
Application of AI in Supply Chain Management
The world is going through a rapid technological change especially in manufacturing and service industries. The major revolution in technologies are in terms of transforming raw facts and figures into valuable information. These technologies have changed the operational style of various firms. Today, technology is used in almost every sector of an organization. The technologies include various applications regarding Supply chain Management. Organizations are shifting from manual to computerized operations to gain operational efficiency.(Singh, 2003)
The widely applicable and reliable technology that is being used and can be utilized further in SCM is the “Artificial Intelligence”.
In recent decades, AI has got a great attention of large firms. It has enabled the organizations to gain efficiency in products and processes.AI, due to its ability to determine business patterns, learn business processes, and gather and analyse quality information, has enabled the organizations to gain operational efficiency and helps the organizations in decision making processes.(Min, 2010)
AI potential in terms of SCM is related to solving problems and gathering information regarding different areas of SCM. Certain forms of AI like expert systems and Genetic Algorithms (GAs) are being used to solve various SCM problems including; management of inventory levels, purchase of raw materials, scheduling operations etc. Following are the various applications of AI in SCM.(Sofia Danielsson, 2018)
Inventory Management and Control
Organizations require idle levels of inventory in order to meet the consumer demands on time and provide high value to customers. However, high inventory levels require substantial amount of costs related to maintenance and warehousing. According to a report, the holding cost of one unit of inventory for one year is about 20% of the actual product value on average. This may increase the overall cost of production for the company, leading to declining returns.
Competitive position of an organization depends upon the ability of the organization to handle its inventory at such level, where it has optimal holding cost and inventory levels to manage customer needs.
Efficient inventory management requires current and accurate information about customer demands, inventory levels at hand, time required to fulfil an order, etc. However, the information related to market demand is difficult to estimate accurately using human intelligence.
In 1986, Allen designed an expert system called Inventory Management Assistant (IMA) to manage logistics. After implementation of the expert system, an organization can improve its efficiency inventory management.
Transportation
Application of AI in SC has been quite beneficial in terms of designing transportation networks across the SC. There are number of problems attached with the transportation in a SC network including; mapping road networks, scheduling vehicle traffic, efficient utilization of parking location etc. In order to solve these problems, organizations require accuracy of data and information regarding the distance of roads, road networks, time schedules of vehicles etc. However, human intelligence is unable to provide accurate data about these critical problems. Therefore, the organizations has found another way of solving their problems related to transportation. Many large firms are using the most popular form of AI i.e. General Algorithm (GA) to solve the problems related to transportation management. Another form of AI to solve the transportation problems is based upon meta-heuristic that is an ANT colony optimization algorithm.(John A. Sauter)
Supporting Make-or-Buy Decision
Make-or-buy decision involves evaluating various alternative options related to in-house manufacturing of goods or outsourcing the production to gain cost efficiency. The decision involves critical assessment over the scenarios stated below;
- Amount of goods that should be produced.
- How and where should the firm invest its resources?
- Risks attached to the development of new products etc.
The above stated scenarios includes various complex decisions which cannot be handled by human intelligence alone. Therefore, due to complexity of above scenarios various computerized tools are used to help decision making process. Most popular tool for aiding make-or-buy decision is Humphrey’s expert system.Managers in the purchasing department took help of expert system in evaluating performance of different suppliers in the market and exchanging valuable information with the employees in the purchasing department. The expert system enables the production managers to acquire efficiency by providing time efficiency operations, ordering purchases of goods online without any human personnel, managing online bid processes and analysing performance of different suppliers regarding order completion. The expert systems are also designed in a way that it conduct search of valuable suppliers based upon various characteristics defined by the managers. These systems complete the purchase order without any human personnel.....
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