The use of Artificial Intelligence in Supply Chain Management
The use of Artificial Intelligence in Supply Chain Management
Background and Research Questions
Problem statement: Supply chain managers experience a wide range of problems that require an urgent address to achieve a better outcome in the functioning of these service providers. Leedy and Ormrod (2013) suggest that one of the most significant challenges facing supply chain manager (SCM) lies in the use of technological innovations that facilitate the activities in this area of service.
Background information: Imagine the challenges that come with handling large volumes of data that supply chain operations handle on a daily basis and ponder about the hurdles that supply chain managers may experience in the future if nothing happens very fast to salvage the situation. The concept of artificial intelligence that came into existence about 60 years ago is here to transform several hardships SCM experience in their activities surrounding the movement of products and services from one point to the other (Leedy and Ormrod, 2013). AI was created to develop machines that think in almost the same way as humans thereby being in the position to replace humans in areas that require intelligence (Min, 2014). The device has displayed significant signs of developing the decision-making process and various areas that the business endeavors to strengthen which encompass learning the business phenomena, analyzing data intelligently, and acquiring information (Min, 2014). More firms are becoming aware of the need to incorporate AI in the SCM process, and it is encouraging that a significant number is making promising strides in using the introduction to transform how they acquire goods from suppliers, handle the raw materials to produce finished goods, and release the products into the market. The corporations that are yet to fuse the system which is the next big thing in supply management miss out on the many benefits that come with using AI in SCM. It is encouraging that entrepreneurs are now coming up with strategic plans that define how they employ the use of AI to handle the supply chain process. It is also satisfying that the institutions record positive outcomes in their application, and this should motivate corporations to employ the device that will become more useful in the coming years.
Research Questions: The project seeks to answer some critical questions regarding the use of AI in facilitating the activities of supply chain management. The first research question in this case to identify the level of awareness among business corporations on how AI boosts SCM practices. The research question is in line with the topic of AI’s use in supply chain activities because it informs about the level at which companies apply technology to handle operations in this area. The research questions also relate to supply chain management because it provides insight into an area that the managers in this area can find to be valuable. The second research question is to identify the factors that inhibit some organizations from using AI in the right manner to facilitate SCM. The research question reflects the chosen topic because it informs about the areas that require transformation to achieve a better outcome in supply chain operations. The field of research also relates to supply chain management because it tells managers about some of the weak points that they need to address to become successful in their application of the tool. The third research question is to identify whether institutions are willing to embrace the technology considering the hurdles that come with applying the innovation. The research question reflects the chosen topic in the sense that it gives a hint on whether companies in the future will implement the system or whether organizations will adopt cheaper forms that do not require high financial investment. The area of research relates to supply chain management because it provides insight on the future trend regarding the next big thing in SCM. The findings of the research project will confirm that being able to overcome the hurdles that come with using AI in handling the supply chain process results in beneficial outcomes that may improve the company’s performance not only now but also in the future.
Statement of Purpose
The purpose of the exploratory study is to equip supply chain managers with the awareness that it is fundamental to overcome the challenges surrounding the use of AI in SCM to attain better performance that may benefit the company. The findings of the research project will have numerous benefits to the practice of supply chain management thereby making it necessary for practitioners to pay considerable attention in this area. One of the possible benefits of the research project is that it reminds managers in charge of supply chain about the ways they can benefit from the technological improvement that is transforming the business sector. Managers in supply chain understand the need to shift from the traditional forms of managing the process which creates more chances of recording the satisfying outcome. The study is also likely to benefit the practice of supply chain management in the way it highlights the areas that require attention to achieve a successful result. Finally, the study will have a beneficial impact in the field of supply chain management, mainly, with the application of AI in handling the transition of goods the way it informs about some of the advanced AI forms that some of the leading companies apply the technology in their handling of the supply chain. It is upon the persons in the managerial positions to pick the information that finds valuable to their operations and put additional effort to achieve their wants.
Business organizations are rapidly digitizing their supply chain processes to differentiate and boost revenue generation. Chandra and Darbhe (2016) report of a survey by Accenture’s Technology Vision in 2016 in 11 nations and over 12 sectors which revealed that more than 85% of companies have adopted digital operations in their supply chain within the past one year. The firms are fast embracing technological ways of handling the process to manage the vast amount of data that is emanating from the process. AI is emerging as a useful tool for analyzing the data and further helps to gain a good comprehension of the variables in the supply chain and also provides insight on how to manage future scenarios. Apart from handling the large volumes of data emanating from the activities of the supply chain, AI is transforming the manufacturing of goods and how people intervene to oversee the processes. Chandra and Darbhe (2016) give the example of Siemens that is adopted an improved AI system that watches over the production line to a margin where they can go unsupervised for several days or even weeks. Siemens is also is also taking the initiative create the Industrie 4.0 program that will function as a fully-organized system that automates the whole supply chain. The program will automatically convert the demand and order information into production processes thus streamlining the creation of customized products.
Other than helping in the manufacturing process, AI is becoming handy in regulating supplier management and facilitating customer service and is also playing essential roles in logistics and warehousing. IPsoft, an American multinational company that helps firms to embrace automation, is a perfect example of a company that has created an AI program (Amelia) to assist in speaking with customers who bring in inquiries regarding the supply chain (Chandra & Darbhe, 2016). The company has trained Amelia to talk in more than 20 languages thereby making it possible to provide suppliers who come from diverse cultures. The program does not only give the company the chance to cut cost in hiring service providers in the department but also eliminates the possibilities of experiencing unethical issues that may emanate when employees who have poor communication skills get to handle customers who have a significant impact on the company’s performance. Logistics operations are set to undergo numerous transformations should all firms embrace the technological innovation. DHL that practices domestic and international transportation of goods is now applying autonomous forklift machines thus depicting a business that has attained the level of maturity in warehousing activities. The company employs specific codes that define the machine’s movement saving the company the need to hire people to perform the logistics operations. Chandra and Darbhe (2016) predict that the next step would be to see autonomous vehicles and drone ships taking charge of delivering goods to different destinations within and beyond the country.
AI is also helpful in handling the procurement process as it helps to achieve cost-reduction and compliance requirement by revealing the executed data within a good time. Special AI software automatically classifies the used data and is also checked for any errors and makes sure everything complies with the requirement before submission into another stage. Chandra and Darbhe (2016) give the example of Singapore City in China that is conducting a trial of using AI to recognize and stop incidences of fraud when going about the procurement process. The AI algorithm is an excellent example of software that aids in the procurement process in the way it analyzes data from the finance department, and also in the way it handles procurement orders and requests. The AI algorithm is also helpful in completing the procurement process in the way it approves tenders, arranges non-financial data for the government employees and workers in another sector, and also it assists in the identification of areas where corrupt deals are happening.
Indications reveal that AI is becoming a useful tool in the demand planning and predicting of future happenings in the area thus opening a broader avenue of achieving a better outcome in the coming years. Planning for demand appears to be a significant problem for many companies, but as Chandra and Darbhe (2016) point out in their article, several firms are trying to leverage analytics with the learning capabilities of machines to forecast changes in demand and patterns during future promotions. The companies that apply the model come up with a reliable and detailed framework that highlights the anticipated results of the trade fair for the sales and marketing unit. The firms that apply the device realize a 20% fall in forecast errors and a 30% reduction in losses that occur during transactions.
Artificial intelligence is also becoming useful in the reverse supply chain (RSC) that is widely applicable by companies that seek to recycle the products that reached the end of their useful period. Businesses around the globe are becoming aware of the merits that come with RSC and are introducing mechanisms that would give the chance to recycle used products to create profit-making opportunities. The increased desire to practice RSC compels supply chain managers to acquire techniques of improving the nature of reverse supply chain management (RSCM) which include the use of AI. The application of AI in the process can help to develop a network design that focuses on the establishment of an infrastructure that efficiently manages the reverse channel which often includes final-users, collectors, and remanufacturers. Xing et al. (2013) give the example of logistic suppliers’ development which they term to be a critical area of function within RSCM, EoL, and EoU. The variations between forward and reverse flow with regard to the cost and intricacy of transportation, storage, and handling operations compel firms to outsource their reverse logistics activities to third-party logistics operators that function using AI. Xing et al. (2013) provide the example of the artificial neural network (ANN), genetic algorithm (GA) and fuzzy system (FS) that function as some of the commonly applied third-party logistics providers used by companies that practice RSC. The AI programs usually abbreviated as 3PLs achieve multi-objective optimization that gives the chance to manage the incoming and outgoing data without experiencing significant shortcomings during the entire process.
The examples reveal that in the present dynamic world, companies that apply AI to complete the supply chain process have higher chances of gaining competitive advantage. AI fused with predictive analytics can not only assist to analyze big sets of data developed by the supply chain process but also assist companies to transform into a more advanced form of SCM (Chandra & Darbhe, 2016). As AI proceeds to handle new things which enlarge its competencies, it is rapidly becoming one of the best working tools in the current times and may become the next big thing in SCM.
Unfortunately, many supply chain managers are yet to understand how to apply AI in their operations due to some factors. Some managers, for example, lack the proper knowledge on how to use AI to achieve efficacy in their duties. Such leaders get the position without undergoing the proper scrutiny of their capabilities, or some fail to put much focus on this area of study while undergoing the training process thus acquiring insufficient on how the field ought to work (Msimangira & Tesha, 2014). Other than the manager’s inadequate knowledge in this area of practice, some institutions fail to put adequate investment in this area thereby making it impossible for the leaders to use AI to improve the supply chain process (Msimangira & Tesha, 2014). Some companies, for example, are afraid of the high financial burden that comes with using the technological practice to improve activities. Some organizations also feel that the undertaking requires the recruitment of highly qualified personnel that understand how the process function which is usually costly and highly demanding regarding their maintenance. Such inhibitory factors prevent companies that would benefit from improved supply chain processes the opportunity to gain from the technological form that is set to become the next big thing in supply management.
Msimangira and Tesha (2014) perform an exploratory study to explore the global problems and risks facing the developing nations concerning logistics and warehousing sectors while paying close attention to Tanzania. The exploratory approach compels the investigators to use the qualitative way of collecting data using the case study approach which they consider to be suitable for the exploratory and the interpretive research technique. Msimangira and Tesha (2014) interview senior supply chain managers and acquire additional information from secondary materials which include the companies’ websites and yearly reports. The case study framework enables the surveyors to comprehend the issues facing SCM, particularly concerning the use of technology in facilitating the process. The case study examines why two public firms that stand thousands of kilometers apart within the same country have diverse experiences regarding their executions of supply chain activities. The companies are the largest in Tanzania functioning efficiently as part of the international supply chain. The similar factor was that the managers in the firms had participated in global supply chain activities for many years.
The researchers outline the data collection and analysis processes appropriately to achieve the desired outcome. Msimangira and Tesha (2014) who apply the triangulation multiple data sources of evidence collect data using semi-structured interview questionnaires that the senior leaders respond to via e-mail. The investigators also interviewed a couple of junior procurement and supplies officers who participated in the SCM process in both public institutions with the intention of limiting bias from one fundamental group of the respondent. The investigators use other forms of gathering data including direct observations, participant observation, and documentation to limit the study’s biases. Msimangira and Tesha (2014) also feel that incorporating multiple respondents and using several highly knowledgeable sources of information reduces the bias that may harm crucial interviews. Msimangira and Tesha (2014) employ the cross-case analysis method to analyze data from both firms which encompass cross-case synthesis, risks, matching, and multiple-source data reviewing.
The investigation reveals valuable insight that may help corporations to improve their management of supply chain processes. The study confirms that some firms are yet to incorporate technological innovations in their control of supply chain activities as much as some are making significant strides to digitize their operations (Msimangira & Tesha, 2014). Apart from the absence of integrated computerized network that connects with the suppliers, one of the public organization shows no signs of apply AI in its management of the supply chain. The other that makes attempts complains of challenges ranging from lack of stable internet coverage and the suppliers’ culture that is bound to traditional forms (Msimangira & Tesha, 2014). The participating managers reveal common problems such as the prevalence of outdated technology, lack of trust between the interacting parties, and lack of qualified personnel. The findings of the study answer the research questions in the way it shows that some companies still have a low level of awareness regarding the use of AI in boosting SCM. The investigation also informs that lack of appropriate knowledge, expertise, and the organizational commitment to improving the use of technology serve as obstacles to the application of the tool. Finally, the survey confirms that companies have the desire to incorporate the mechanism but may first have to overcome the inhibiting challenges to achieve the desired results.
Business proprietors and supply chain managers must consider several factors to overcome the difficulties that come with handling the process. An efficient way to overcome the hurdles that may inhibit the application of AI in SCM is to pay more attention during the training process rather than viewing this area as being insignificant and without value. Individuals who aspire to become supply chain managers should pay considerable attention to the teachings focusing on how to apply AI to improve the transporting, storing, marketing, and selling which are the critical processes of the supply chain. The trainees ought to develop the interest to become experts in this area and should spend more time conducting a personal investigation on how the field functions. Furthermore, the persons undergoing the training process may choose to consult with other groups including the trainers and practicing persons to gain extensive insight on how AI improves the supply chain process (Msimangira & Tesha, 2014).
Apart from gaining broad knowledge on how AI functions in boosting supply chain, it is essential for companies to put more investment in this area to attain the long-term benefits that come with using the technological innovation in bolstering supply chain activities. Business leaders, for example, need to acquire the equipment and software that would enable supply chain managers to apply the technology to their operations. The leaders should spare enough time to find out the most efficient programs that would work without breakdowns and which are likely to yield the best results without threatening flaws. The leaders may also spend the time to hire individuals who have advanced awareness on how to use AI to improve the supply chain process. Even though the hiring process might be costly and threatening to the organizational financial situation, the intervention by experts is likely to have a long-lasting impact on the way the firm handles its supply chain.
Other than seeking knowledge when aspiring to become a supply chain manager and putting more investment on AI usage, it might be essential to borrow how other companies that successfully apply the technique go about their activities and utilize similar forms. The department in charge of SCM may identify the organizations that have made significant strides on how they use AI to handle the movement of goods and services from one point to the other (Msimangira & Tesha, 2014). The team should then settle on a suitable day on when to visit the firm for an exchange program where the employees will get the chance to share ideas extensively. The workers and their leaders may use the opportunity to gain new insights that they may apply in their operations to achieve a better outcome.
Finally, it may be necessary to invite experts at the organization who provide guidelines on how to use AI to handle supply chain management. The gurus should be widely accepted as qualified personnel who have helped other institutions overcome their challenges in using the innovation, and should be skilled enough to be in a position to provide quick solutions to some of the erupting cases. The guest should inform the workers in charge of the functioning technology that achieving success with the tool requires courage and dedication, and that feeling inferior or having the belief that the company cannot deliver good results when using the application may lead to poor outcome. The organization might have to cope with the high cost of inviting the experts if it aspires to achieve the long-term benefits. Otherwise, it can miss the excellent and long-lasting effects of using the tool to facilitate supply chain operations.
Companies that rely on an efficient supply chain management process for the better outcome should take the initiative to infuse AI in their activities considering that this is the next big thing in SCM. It is evident that AI helps to complete several functions dealing with supply chain management and this becomes apparent in various areas that form part of the process. AI assists managers to enhance the manufacturing process and aids in facilitating supplier management as well as customer service. The device assists firms to boost procurement process thereby giving the enterprise the opportunity to record positive financial gain that emanates from the cost reduction the institution experiences. The device helps in demand planning and predicting of future which creates the chance to strategize SCM. The tool also attracts the companies that deal in RSC, and this exemplifies the diversity of the innovation that is sent to become more useful in the coming years. Despite the usefulness of the tool, many supply chain managers are yet to embrace the apparatus due to lack of knowledge and the organization’s inadequate investment in the area. The challenges prompt business leaders and the managers in charge of the supply chain to take urgent measures that are multi-dimensional. The teams in control of SCM shall record improved performance should trainees pay more attention in this area, and should the institution put more financial investment in this area. Sharing skills with firms that are already using the program may heighten how beginners are incorporating the next big thing in SCM in their undertakings.
Chandra, M., & Darbhe, A. (2016). Artificial intelligence: The next big thing in supply management. Retrieved from http://www.financialexpress.com/industry/artificial-intelligence-the-next-big-thing-in-supply-chain-management/329033/
Leedy, P., & Ormrod, J. (2013). Practical research: Planning and design. New York, NY: Pearson Books.
Min, H. (2014). Artificial intelligence in supply chain management: Theory and applications. International Journal of Logistics Research and Applications, 13(1), 13-39.
Msimangira, K., & Tesha, C. (2014). Global supply chain practices and problems facing developing countries: A case study in Tanzania. Operations and Supply Chain Management, 7(3), 130-138.
Xing, B., et al. (2013). Artificial intelligence in reverse supply chain management. Resources, Conservation and Recycling, 6, 1-6.
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