Introduction
The use of new technology is a growing area of research and discussion as the global community copes with transitioning into the modern era. Global enterprises adapt to the advancement of technology that occurs, gradually evolving with increasing complexities. Historically, researchers such as Jacob Bigelow have attempted to clearly define technology and its use since the 19th Century gaining recognition based on its influence on the transformation of economic and development growth (Carroll, 2017). Technology had a major influence over the past century with the emergence of the industrial revolution, which has advanced into the current fourth industrial revolution. Therefore, it is essential to understand the application and consequences of adopting new technologies.
Defining Technology in Supply and Chain Management
Consequently, technology is acknowledged as an instrumental part of supply chain operations management. The supply chain is a rudimental system that facilitates the interconnection between the supplier, production enterprise, and the customer (Azevedo, Pimentel, Alves, & Matias, 2021). In addition, research by Kar, Dash, Rebman, and McMurtrey (2019) describes supply chain management as an interaction of production, marketing, and logistics in a business enterprise. Its implications for supply chain operations are critically researched and comprehensively defined to understand its importance and ramifications of implementation (Azevedo et al., 2021). According to research by Azevedo et al. (2021), the definition of technology in supply and chain management can be described as the aggregate of various sets of advancement, including the use of Information, Digital, and Operation Technologies (IDOT). They facilitate the transition of industrial activity into a modern era that embraces technology as a tool for comparative economic advantage. According to Chung (2021), the new technologies have been the driving factor for the inception of the fourth industrial revolution that actively integrates the use of bulk data management systems and AI.
The area of focus of this paper is centered on the use of technology in the operations of a commercial manufacturing enterprise in the production of goods. This research looks into implementing new technological advancements in business supply and chain management. The discussion will entail a detailed examination of industrial operations, including the company’s advanced Information, Digital, and Operation Technologies (IDOT). The assessment involves the interoperability of the components to enable an effective supply chain for the company’s products. The research will specifically consider the company’s operation concerning disruptive technology such as artificial intelligence, the internet of Things, and big data management.
Use of Technology in the Commercial Business Supply Chain
The new technologies in the commercial enterprise discussed focus on adopting AI and other complementary technologies such as big data management and the Internet of Things (IoT). Big data management is used to support the identification of suppliers for raw materials, market needs, and customer preferences. As used in the discussion, bulk data refers to digital data of heterogenous mass consisting of individual and company characteristics requiring sophisticated storage and analysis by computerized systems (Riahi & Riahi, 2018). The data sets stored include personalized customer ordering patterns, shipment demands, product life cycles, and manufacturing data.
On the other hand, Artificial Intelligence commonly refers to the capacity of computer systems to learn and mimic human intelligence. According to Kar et al. (2019), Artificial Intelligence commonly denotes the capacity of computers and systems to operate independently and solve challenges they may not have been programmed to undertake. Further, research by Kar et al. (2019) argues that AI systems possess the capacity to collect data from their interactions with a particular environment and determine a choice of action that is estimated to produce the best possible outcome using logic and probability. Chung (2021) argues that AI is a concept of new technologies that facilitates the transition of operations into the fourth industrial revolution. The system’s capabilities depend on the interoperability of AI and big data collected and stored in servers which are processed to elicit an action by the system.
The system is expected to improve the computing system to adjust to managing large data sets and manipulating the data to support enterprise supply chain management. As described by Kar et al. (2019), the AI system can process the data and a set of algorithms from the data to make projections and predict the best combination of algorithms that facilitates the identification of market patterns and demands. Accordingly, AI systems can identify the number of resources required and the potential customer preferences that inform the quality of the product produced and the supply and delivery to various customers. Therefore, based on the patterns and predictions of the AI system, the company can adjust operations and make specific choices on stock quantities necessary for optimum operations (Azevedo et al., 2021). The machine learning system integrates historical data sets in conjunction with real-time data to ensure that the system is updated to the market demand (Kar et al., 2019). Therefore, the company systems can adapt to customer demand changes by restructuring the supply management and meeting market demand based on real-time analytics.
Factors Supporting the Use of AI and Big Data in Supply Chain
Agile Supply Chain System
The first argument is on the advantage of integrating AI due to its influence on establishing an agile supply chain system. The effective projections of customer demands facilitate the adaptation of the systems to meet these demands. Cheung, Chiang, Sambamurthy, and Setia (2018) presented that the transition can be defined as agile supply chain systems with flexibility in delivering goods and services based on a personalized level for its customers. The system allows a wide restructuring of the company supply channel and configures the value chain to adjust to the market structure. This means that the supply chain management will not have a permanent configuration supported by AI machine learning. Consequently, big data manipulation is essential to making predictions and facilitating timely, accurate, and efficient manufacturing and supply of our products.
Categorically, the system learning capacity acts as a comparative advantage that the enterprise gains against other enterprises in the same market. The characteristics of the AI system qualify its adoption as a major investment because of its adaptability to dynamic changes in market demand (Kar et al., 2019). Therefore, the system allows continuous relevance of the company products, which is an economically sound benefit of the system. The AI system can increase technology interoperability through machine learning, which manipulates big data, and the Internet of things to track products and ensure personalized service based on market demand (Chung, 2021). The advantage allows the concurrent evolution of the systems with dynamic market changes.
Quality and Brand Enhancement
Secondly, AI systems are essential in enhancing the quality of products in the manufacturing process and present a chance for enterprise branding. AI has been identified to effectively identify customer needs and process materials to meet these demands (Kar et al., 2019). The use of algorithms and large data that may be humanly impossible is done in quick processes that the AI systems adjust to and develop a high-quality product. According to Palanivelu and Vasanthi (2020), AI allows the company to minimize errors in production and provides a long-term advantage for its improvement over time through self-learning attributes; therefore, the quality of output increases. The business gains added comparative advantage by using machine learning technology in its supply chain operations.
The quality of products is a major factor determining the value and branding of a business. The use of technology is relevant in creating a trademark based on personalized service delivery. According to Bughin et al. (2017), AI technology is the central factor that has stimulated the quality of production in the manufacturing industry. The company benefits substantially by transitioning the robotics systems into company operations. The use of AI can transform the robotics portion of AI by integrating object recognition, which reduces the limitations of conventional picking systems that require products to be in specific positions (Kar et al., 2019). The systems are adapted through cognition and sensory advancements to eliminate the string of people lined up on conveyor belts to arrange and package products. AI systems advance into application in the packaging and delivery of goods. The implications for the enterprise are a reduced dependency on human capital, reduced cost of production, and elimination of human error. The systems facilitate quality products and prioritize the timely delivery of goods, enhancing customer experience, which is instrumental in branding.
Sustainability Implications
The third argument necessitates applying AI systems and complementary new technologies to meet sustainability goals. According to Azevedo et al. (2021), technology is evaluated on its impacts on three sustainability factors: social, economic, and environmental dimensions of the supply chain process. Accordingly, integrating AI into our systems targets can radically improve our economic sustainability by increasing the identification of market demands; as such, the company operations can adjust to the market needs, influencing production to remain consistent. According to Kar et al. (2019), the use of AI systems optimizes the sourcing of materials, which influences a low cost of production and management of resources. The enterprise can accurately predict market demands and material needs, ensuring that material sourcing is optimized (Azevedo et al., 2021). The attribute helps the enterprise to reduce overproduction and waste of material. The operation influences an environmental aspect by balancing the exploitation of resources. According to Azevedo et al. (2021), automated industries are energy and resource-intensive, which means production poses a risk to sustainability where overproduction exists. Moreover, the production process can be optimized using AI prediction systems that develop a high-quality product at lower costs. According to Kar et al. (2019), AI systems can modify product designs based on the machine learning system and develop efficient prototypes that minimize waste in production. The accumulation of these efficiencies encourages adoption AI systems as a strategic transition to sustainable productivity.
AI systems optimize the delivery system that allows the drivers to identify the supply routes. Robots are instrumental in the packaging and loading of delivery vans reducing the time consumption in the movement of products (Kar et al., 2019). The AI systems increase time efficiency and cost of delivery by assessing and choosing the best route for drivers that ensures the timely delivery of services (Kar et al., 2019). The efficiency of the delivery systems is economical and enhances the services provided that increasing product value at the same time. The company stands to gain by improving customer experience providing a competitive advantage against other companies that depend on traditional supply systems.
Factors Contributing to Failed Adoption of AI and Big Data in Supply Chain
Availability of Skilled Experts
AI systems and supporting technology components such as big data management are complex. The complexity implies that the adoption of AI systems requires several qualified experts to handle the installation and management of the technology, which will involve regular updating of data and maintenance of system servers, both hardware and software components. Further, cybersecurity risks necessitate an additional expert in the maintenance of the network protecting private data. According to Jahan (2021), there is a limitation in the availability of skilled experts that can manage intricate systems, causing a limitation in the capacity of an enterprise to adopt AI technology. The limitation hinders the adoption of the systems without adequate skilled labor. The lack thereof may influence the enterprise to forego adopting new technologies and instead opt for other simpler available channels of improving the supply chain.
Capital Intensive Start-up and Operations Cost.
Integrating technology into supply chain systems is a capital-intensive undertaking as the initial cost of installing AI and supplementary technology is relatively high. Objectively, handling big data requires the setup of large data centers strategically located (Demigha, 2020). The inference of the concept is that AI adoption necessitates designing several data centers that are costly. Consequently, the data centers require equipping suitable servers for data storage and processing. The combined needs for big data management increase the capital cost of acquiring equipment and establishing multiple data centers to facilitate localized information management. Further, the systems require skilled employees to manage and handle the systems considering that the systems are complex and intensive, requiring regular maintenance (Bintrup, 2021). The maintenance may include establishing security measures such as firewalls, data encryption, and malware-detecting software and ensuring the protection of digital data stored in the servers.
Resistance to Change
AI is considered a tool for enhancing operations that require less human influence on decision-making. The use of AI faces ramifications as people are opposed to rapid changes in their normal activities. The introduction of AI is bound to face resistance due to the fear of losing jobs; as such, the acceptance and functionality of the systems are impeded. Further, the human-AI interaction poses the risk of resistance associated with the deskilling of personnel. According to Mirbabaie, BrĂĽnker, Möllmann, and Stieglitz (2021), the integration of AI is expected to assist in the completion of tasks with efficiency. However, the increased use of AI poses a new challenge in the workplace, especially where operations require a collaboration of AI and humans to complete a task. AI has the ramifications of deskilling personnel who lose their professional identity to depend on AI systems to undertake their tasks (Mirbabaie et al., 2021). The combined risks increase the challenge associated with the employees’ adaptability to the use of AI systems in workplaces. Therefore, the risks of low productivity and positive growth of the company are challenged.
Ramifications of Adopting AI and Big Data
Integration of AI requires vast experience from its personnel to adapt to the change. According to Mirbabaie et al. (2021), without proper understanding and knowledge of how the systems and identifying the appropriate position to integrate the AI may cause redundancy of the system and professions. The cost is perceived to cause an identity threat and deskilling of the employees that lose self-esteem and become dependent on the systems entirely for decision-making (Mirababaie et al., 2021). The adoption of the complex technology requires a significant investment in training workers to manage and facilitate the system’s adoption.
Elsewhere, operation costs for the AI require continuous updating of the big data to ensure flexibility of the AI system to market requirements. As denoted by Demigha (2020), the systems are data-intensive, which means it relies largely on the availability of big data and its management. Research by Nadimpalli (2019) argues that the potential risks of the system are tied to the data stating the data AI system is just as good as the data provided. The ramifications hinge largely on data quality that may result in undesirable results (Nadimpalli, 2019). Research by Brintrup (2021) reveals that AI systems are prone to distrust caused by the use of biased data, which translates to low-quality outcomes. Therefore, the technology’s significant intricacies and complexities necessitate a critical evaluation of the employee’s capacity to handle data needs.
Additionally, the installation of AI requires adequate financial capabilities to withstand the system’s cost of operability. According to Azevedo et al. (2021), AI systems and complementary techniques such as data centers and servers consume large energy quantities and are resource-intensive as productivity increases. Therefore, enterprises are forced to increase investment in the cost of energy and sourcing materials to support the production needs. In addition, AI systems are characterized to acquire and process big data that includes customer details and personalized information. The risks of cyber security and privacy are considered to pose as a ramification in integrating AI systems (Jahan, 2021). Data privacy risks should be critical to protecting private data, such as personal customer details.
Conclusion
Integration of technology is an essential component supporting transitioning of enterprises into the fourth industrial revolution. The industrial revolution calls for a radical step and adopting flexible AI systems that increase the value chain. The adoption of the technology has substantial disadvantages posing a threat to the relevance of human labor and a risk to the professionalism vantage that humans have. Further, the literature reveals the significance of understanding system complexities in adopting the technology and adapting to the industry’s evolution. The systems’ susceptibility to data privacy and security risks are relevant factors that inhibit the operability of the systems. However, the significance of the technology outweighs the negative repercussions of adopting the system. An enterprise stands to gain tremendous traction in the economic realm with a comparative advantage over other businesses. The project is suitable for organizations that aim to make long-term investments to improve brand recognition since AI takes time to adapt and learn.
Moreover, as literature dictates, the longer the AI exists, it evolves further and improves its functionality and advancement. The quality of production and reduced cost of production feed into the global trends of transitioning to sustainable manufacturing processes. The initiative translates a risk on the start-up cost, training employees to adjust to the transition with the anticipation of greater yields with less cost of production due to the efficacy generated by technological advancement in the fourth industrial revolution era. The integration of AI, based on these potential outcomes, is suitable for increasing the comparative advantage of enterprises in a market environment.
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