A world of logistics in which artificial intelligence reduces inefficiencies, manages risk and maximizes profit is just around the corner, and the utilization of advanced AI-based supply chains will be significant.
Artificial neural networks, the basis of AI, have solved profound problems for the scientific community. For instance, ANNs are the foundation of facial recognition, which has been instrumental in the criminal justice system. Facial recognition has become so advanced through neural logic that everyday phone and computer devices can be set to lock and unlock through facial recognition.
ANNs can receive vast amounts of information simultaneously and instantaneously process it for a variety of uses. In the banking and finance industry, ANNs analyze data records in real time to identify fraudulent behavior. This allows the bank to immediately shut off credit and debit cards as fraudulent activity occurs.
Scientific research, even with the best of intentions, cannot answer a number of complex and adaptive questions. Complex problems are those that have layers and countless moving components that are oftentimes too complex for one to understand. The human brain has a constellation of neurons and synapses, which constitutes a complex and adaptive system. Studying the complexities and adaptability of the human brain gave rise to neural logic, which is a new way to understand complex information with a tool that can adapt, process and learn large databases of information simultaneously. An artificial neural network resembles the brain in the way it functions by using algorithms for adapting to new situations and learning, similar to how a brain processes and decodes information.
In an era of higher supply risk, greater demands, increasing competitive intensity and larger populations than ever before, supply chain excellence depends on the organizations ability to orchestrate a complex process that involves acquiring raw materials, converting them to finished goods and delivering them to consumers. Due to the need to have increased visibility across the end to end processes, organizations share information with supply chain partners in real time. Supply chain management (SCM) has become far more information intensive and supply chain professionals have been seeking a way to manage oftentimes unmanageable amounts of information. Artificial intelligence has been in existence for decades; however, it has been underutilized in supply chain management. AI can process unsurmountable information instantaneously, learn from its own experience, comprehend new concepts and develop efficiencies that neither one person nor company can do alone.
Optimistically, for the supply chain industry, some pioneering efforts have been initiated to explore AI applications in the SCM arena. Genetic Algorithms (GA) are being used to prescribe a solution to a given problem in a way that is both optimal and efficient. One way that it is being applied today is for inventory and control planning, which is paramount to SCM. Competitive advantages in this industry depends on a business' ability to plan and control inventory at a minimum cost, while making inventory readily available to customers. A GA can capture inventory patterns, understand information at every area of the supply chain which such rigorous detail that can also understand complexities and predict future issues with inventory. In addition, similar GA's are utilized for transportation and network design to solve scheduling, freight consolidation, intermodal connection issues, optimal road networks, fuel, parking and traffic tribulations.
Other areas that could utilize more sophisticated forms of artificial intelligence is in optimizing the formation of strategic alliances, supply chain coordination, collaborative demand planning and even business negotiations. The SCM industry has garnered greater attention from both scholars and practitioners, therefore AI has a promising future in solving some of the most complex issues that the industry continues to face.