Essay Sample on AI-Enhanced Business Productivity: A Revolution in Decision Making

Introduction

Artificial intelligence was introduced for the sole purpose of creating machines that mimic human intelligence. Since the beginning of the early 1970s, artificial intelligence and machine learning has shown great capabilities in enhancing the processes of decision making and notable productivity in various business aspects due to their capabilities of recognizing various business patterns, information analysis, and widespread data intelligence (Singh, 243). Notably, in circumstances of uncertainty in demand, supply risk, and heightened competition intensity, supply chain success has often relied on the ability of an organization to incorporate the entire organization processes like the acquisition of raw materials, production and distribution of the finished goods to the final consumers. This ability can be improved through increased visibility through back to back supply chain processes (Singh, 243). Therefore, many leading national and Multinational Corporation like Apple Inc. has strived to boost their information sources by sharing real-time information with other supply chain partners. Hence supply chain management has become more information-oriented by focusing on substituting assets including inventory, transportation, and warehousing with information.

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Therefore, recognizing the importance of information in the success of supply chain management, many organization management and professionals have placed various initiatives to improve better management of information to make effective sound business decisions (Singh, 244). One of the initiatives includes the adoption of artificial intelligence that has been in existence for many years but has only found fame in the modern contemporary business society recently and has not been exhausted in supply chain management. AI refers to the use of computers for recognizing, reasoning, understanding, learning and retaining human knowledge and behavior (Singh, 243). The simplest way of understanding the application of artificial intelligence and machine learning is its capability of value addition in the supply chain. Notably, artificial intelligence has got two main categories, that is augmentation and automation. Augmentation assists human beings in their daily task commercially or personally without exercising full control of the output. Such artificial intelligence is commonly used in data analysis and software solutions where they are implemented with the primary objective of reducing human errors (Singh, 245). On the other hand, automation operates autonomously without any need for human intervention. For instance, the integration of robots to undertake key organization processing steps (Singh, 247).

Machine Learning

Machine learning was developed to provide computers with the capability of learning without being programmed. Therefore, it examines how computers can easily acquire knowledge and information directly from various data sources and subsequently learn to handle several key business concerns (Wu, Desheng, Shu-Heng, and David, 2). Concerning the type of learning activity, machine learning can be grouped into different classifications. First is the concept learning category. Concept learning was developed to accurately recognize various aspects that are relevant to organization future decision making. The second category is the decision learning tree that focuses on the classification of all objects by ascertaining their values through the construction of the decision trees. The third category involves perceptron machine learning that focuses on attaining essential knowledge to reduce various errors arising from supply chain processes and locating solutions to these problems using a single layer known as the perceptron (Wu, et.al, 2).

Another category is the Bayesian learning that allows the computers to understand multiple representations of probabilistic and multiple functions and the last category involves reinforcement learning to permits the computer to give immediate and continuous response arising from various machine learning procedures and processes. Therefore, notwithstanding the variation in learning responsibilities, machine learning mechanisms mostly tries to mimic the attributes of nature based upon different experience and knowledge that human beings have demonstrated for a long period since their existence. Notable majority of the machine learning mechanics were developed and motivated by various neurological researches on human brain capabilities, evolution and mathematical evidence of human knowledge acquisition (Wu, et.al, 2). To be specific, the adoption of machine learning can prove to be a useful mechanism for understanding the concept of collaborating behavior demonstrated by numerous supply chain partners through organization learning procedures and techniques. For instance, various scientist and scholars have recently incorporated the concept and the capability of machine learning to forecast cumbersome demand information at the rescue of inefficient supply chain management. Therefore, machine learning has chipped in to relieve human beings from performing cumbersome tasks that consumes a substantial amount of time and cost particularly in the supply chain process. Thus in the long run promoted effective organization supply chain management through efficient use of resources (Wu, et.al, 4).

Synthesis of Machine Learning and AI in Supply Chain Management

Despite the development of AI and machine learning many decades ago, the capability of machine language and AI as a way of solving complex problems and locating information in supply chain management area has not been fully exhausted. However, some tremendous efforts have been made by different pioneers to integrate AI and machine language in the supply chain In general, the concept of AI and machine language has been applied in supply chain management to solve inventory problems, purchasing, planning, and freight consolidation among other areas (Wu, et.al, 3).Notably, the integration of artificial intelligence in supply chain processes has improved supply chain management through the improved decision-making process. This has in turn created efficiency and effectiveness in earlier organization, thus making other businesses to incorporate this concept in their processes and procedure. The application areas are discussed below.

Inventory Planning and Control

Inventory in business is a representative of idle resources that are needed to stabilize the high level of customer service; however, inventory has incurred substantial cost to the associated organization. It has been associated with the carrying cost of holding inventory. For instance, the annual cost of holding one unit of inventory has been estimated to range from 16% to 36% of the product value of most organizations (Wu, et.al, 6). Therefore, for a firm to have a competitive edge above other organization, it has to control and have a sound plan for its inventories in light of minimizing the carrying cost. However, this can only be achieved through incorporation of accurate real-time information in reference to demand, the size and the type of inventory needed and the lead time required to make the customer's order available.

Undeniably, this kind information can be difficult to estimate and predict, and the traditional tools that many organizations have incorporated such as the mathematical economic order quantity proved ineffective since they still lack some level of accuracy. Therefore, such mechanisms as the incorporation of expert systems can be best suited in handling inventory control and various planning decisions. Practically Wu (2014) suggested an expert system known as inventory management assistance system that was developed for the sole reason of helping the united air force logistics in managing different types of spare aircraft spares and to minimize safety stock. The results revealed that inventory errors reduced significantly as a result of improved effectiveness from 9%-19% (Wu, et.al, 7). Therefore as illustrated, machine learning and artificial intelligence mechanisms such as experts system can act as a promising approach to inventory planning and control concerns due to its ability to capture inventory patterns in the entire supply chain system. Hence the capturing of such complex inventory patterns can assist inventory managers to approximate adequate inventory level hence lowering the carrying cost of too much inventory

Transportation and Network Design

Another application area is transportation and network design. Notably, one of the widely used applications of artificial intelligence and machine learning to a specific supply chain has been to design transportation network problems that are proving to be intrinsically difficult to locate. This category of problems includes vehicle scheduling and rooting issues, consolidation of freight and intermodal issues (Wu, et.al, 7). Moreover, other related concerns comprise parking space issues, traffic assignment, gas distribution, and pipeline design. Therefore, due to the collective nature of these problems, the generated assignment based mechanism has turned out to be one of the effective forms of artificial intelligence mechanism employed to curb the various aspects of transportation and networking problems. Moreover, another mechanism has been the use of ant colony optimization algorithm. This method has been widely employed to deal with definite networks known issues such as vehicle routing (Wu, et.al, 7).

Purchasing and Supply Management

Make or buy decisions are mostly associated with the organization weighing various alternatives for producing and offering goods and services respectively. Despite the fact that, the make or buy decision may look straight forward and simple, it takes into account key number of consideration like "what if scenarios" like what volume does the organization need to produces?, how much capital is required in the production?, how much risk is involved in the development of new products?, and what are the main strengths of the company? What business does the company deal in? Therefore, due to the unpredictability and difficulty of the above scenarios, the make or buy decision needs an integration of system decision techniques such as an expert system tool (Aronson, Jay, Ting-Peng, and Efraim, 67). Aronson and his colleagues for instance developed expert system tools that could assist supply chain managers in analyzing supplier performance and information dissemination in the organization. Moreover, they also developed an agent-based purchasing system that automated the online ordering system for customers in the acquisition of various company products (Aronson, et. al, 77). All this have been to the best of improving supply chain management effectiveness.

Moreover, Aronson and co, developed knowledge and agent-based system to examine organizations online bids and operations to enable the fulfillment of urgent orders. Besides, they suggested an integration of intelligent software of the agent-based systems that are self-automated capable of automatically locating various suppliers through multiple online catalogs and selecting qualified suppliers for the organization raw materials and resources. In the prevention of any ambiguity it has been ascertained by many organizations that the proposed model can act as an alternative to the role played by human decision making.

Forecasting of Demand

The information concerning the future demand of an organization product is indeed uncertain as it involves many unpredictable external factors. Therefore, the accuracy of organization planning is the key to the development of new products, workforce planning, and scheduling and inventory control. The accuracy will help in the reduction of uncertainty level. However, it has b...