Application of Artificial Intelligence for Supply Chain Management
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This paper focuses on applying Artificial Intelligence (AI) for Supply Chain Management (SCM) organizations. The paper cites scholarly articles, journals, books, blogs, and other sources that provide enough information on the use of Artificial Intelligence in the running of Supply Chain Management (SCM) organizations. In the literature review of this paper, much has been discussed, which first of all is to focus on how Artificial Intelligence (AI) contributes in whichever way to the SCM organizations. In other words, the paper focuses on how SCM organizations can apply the knowledge of AI in running their daily processes. With the technology change, many organizations have opted for using Artificial Intelligence to fit in the present world, which is moving into a digital future. Popular technologies such as cloud computing and, most importantly, cyber security have been researched by many organizations aiming at the accuracy of the results. Secondly, the focus of this paper also revolves around the justification of the advances in AI with the intelligent SCM management paradigms. In other words, the paper focuses on the contributions of AI to SCM. The paper seeks to find out how the current or potential techniques have been proved effective in ensuring that SCM practice has been enhanced. Thirdly, the paper goes further to describe the role played by Artificial Intelligence (AI) to run the SCM organizations. The paper focuses on questions like, which role do AI technologies play for intelligent Supply Chain Management? Lastly, the focus of the paper is on the effects of the shortage of supplies on the supply chain process during the COVID- 19 pandemic. It further establishes how Artificial Intelligence can be of great help in supporting this shortage. The paper will source data from sources that are dated from 2017 to 2022, and therefore some findings or information may be subject to past trends. SCM are dynamic systems, and therefore they are expected to change from time to time.
The changing world has kept many organizations on their toes towards a digital future whereby many technologies have replaced human knowledge. Among the technologies already on the market include Artificial Intelligence (AI), whereby many of the processes are automated to increase the speed of the operations, efficiency, and high accuracy of the results. In that case, therefore, Artificial Intelligence has been defined by Hamet and Tremblay (2017) as a general term used to describe the use or application of computers for model intelligence that requires minimal or no human intervention. In other words, it is the application of computer knowledge to perform the functions initially performed by human beings. On the other hand, Artificial intelligence is the capability of the computer to provide solutions to tasks that it had not initially been designed or solved (Dash et al., 2019). The mimics human knowledge and therefore carries out many human tasks with minimum supervision or intervention by the humans. The use of Artificial Intelligence is associated with the intervention of robots (Hamet & Tremblay, 2017).
One of the areas where the application of Artificial Intelligence is in Supply Chain Management (SCM) Organizations. Kohtamaki et al. (2019) say that many organizations, including the SCM, have experienced the shift from remote or normal operations monitoring to the most automated AI machines. The shift aims to improve the functionality of the organization’s systems and, therefore, produce better results. AI technology has emerged as the leading technology in business organizations. Since many SCM profits a lot from the application of AI, it is important to research how the SCM will apply the AI knowledge in problem-solving. To achieve this, four sub-topics are covering the most crucial parts in the relationship between Artificial knowledge and the SCM.

Literature Review
How Artificial Intelligence (AI) Contribute to SCM Organizations.
There are several areas in which the application of AI is important to SCM organizations. One of the important areas in which AI is useful is Value Creation. The system based on AI will present the best combinations of the known algorithms and data sets that are supposed to be considered to have an accurate prediction (Dash et al., 2019). In other words, AI is helpful in business whereby the business owners can now have a 100% projection of what the customer or the consumer wants. The AI will be helpful in forecasting production, demand optimization, delivery, promotion and pricing, smart manufacturing, and smart retailing.
To begin with, AI aids in forecasting Demand and Optimization. Many organizations are always aimed at balancing demand and supply. To balance demand and supply, the best forecast is required in this case for their supply chain and manufacturing (Dash et al., 2019). Artificial Intelligence system is programmed to process, provide the analysis automatically, and predict the data. The AI will provide the most accurate forecasting demand, which is reliable in allowing businesses to perfect their location of orders and purchases. At the end of it all, the expenses related to transportation will be reduced. Other costs that will be reduced include the expenses of warehousing and the SCM administration. According to Dash et al. (2019), the AI will also discern the trends and the most important ways, that aid design the manufacturing and retailing strategies in a better way. AI is also of great help in the Research and Development(R&D) departments, whereby it is used to assess the probability that the prototype will fail or succeed on the market. The assessment can only be made possible with Artificial Intelligence. In addition, AI has made it easy to develop the most efficient designs. Therefore, AI has played a huge role in smart manufacturing by ensuring that the waste in the design process has been eliminated.
Secondly, AI has been helpful in production. AI has been of great contribution to the production process for several reasons. Once the product has been automated, there is going to be perfect optimization of important processes and also assets, whereby better teams are going to be designed including the people and the robots, there is going to be a great improvement in the quality and the reliability which means there will be less or no errors and lastly but not least, the downtime aimed for maintenance will be prevented. According to Bughin et al. (2017), Robots are the branch of AI that has also played a big role in production. The Robots, which are AI-enhanced and have been equipped with very powerful cameras, have been important in SCM as they can recognize the empty shelves. For example, Dale (2018) says that the supermarkets in the UK have adopted the use of the AI systems in their warehouses whereby robots are seen steering thousands of the product filled bins and the conveyor belts then deliver them perfectly and interrupt the human packers to make sure that the shopping bags have been filled.
Thirdly, Artificial Intelligence aids in Promotion and Pricing. Businesses have come up with digital ways to reach their customers. According to Dash et al. (2019), approximately 25%of the current market budgets are done through digital methods, and approximately 80 percent of the SCM organizations make technology-oriented expenditures. Some of the activities supported by Automatic Intelligence include engine optimization, Lead filtering, outbound email marketing, Lead filtering, and scoring, among other tasks related to marketing. AI-related tools such as Wordsmith, Articolo, and Quil are essential in running adverts or creating news that can be clicked and viewed on their websites (Seligman, 2018).
The fourth use of AI in Value Creation is that it helps in delivery. Many businesses now aim at making customers feel very satisfied. It has never been an easy task to make your customer feel special. However, it has been made accessible through AI machines that have technologies such as computer vision and machine learning (Dash et al., 2019). For instance, a supermarket may display a bunch of bananas in the customers’ trolley. The camera and sensor could quickly tell that the customer likes the bananas based on the history of the purchases. The AI application will then create a video to understand that bananas can be eaten alongside other foods, chocolate fondue. Many businesses have come up with applications that could aid in service delivery.
Another good example is Amazon which has come up with a retail shop in Seattle. The customers can pick food from the shelf comfortably and then get out of that store without necessarily paying through the counter. A computer vision monitors the customer purchases then tracks their progress as they swipe into the store. The swiped information will be associated with the goods or products taken in their bags. The computer vision will debit their accounts when they leave the shop then a receipt will be emailed to them.

The Justification of the Advances in Artificial Intelligence (AI) With Intelligent Supply Chain Management Paradigms
Artificial Intelligence has already improved some fields or tasks. According to Toorajipour et al. (2021), the advances in AI have impacted literature, i.e. the marketing field. In marketing, the Artificial Neutral Networks (ANNs) have had a strong outcome when used on their own are when combined with other networks. When combined with other neutral networks like the self-organization and radial basis, the “Enhanced Cluster and Forecast Model.” The advantage of this model is that it is very easy to build, very accurate, and most suitable for forecasting in the real world. ANNs are also known for marketing the Decision Support System(DSS) by making it more accurate. The Marketing support frameworks, which are ANN-based, provide the automated classification of the unknown cases (Toorajipour et al., 2021). The marketing support frameworks do not achieve this by coming up with the data analysis rather, but by generalizing knowledge acquired from the illustrations that have been already analyzed. In addition, the ANNs normally are also used to create customer segmentation. Customer segmentation was defined by Toorajipour et al. (2021) as an approach that is very important in marketing campaigns.
The next model is the Fuzzy model, ranked the second most powerful technique in literature. Fuzzy models have in most cases been used to make or come up with the innovative segmentation method whereby cluster analysis plus the fuzzy learning techs will be combined to produce or come up with the highest accuracy. Pricing, which is part of the most vital parts of the marketing, is therefore achieved or improved by the multi-level model, which is ANN-based. The multi-level model which is ANN-based then processes the errors from the pricing. It also has excellent precision of pricing, which is picked from a sample making it extrapolate best from the environments related to pricing that are more volatile compared to the hedonic models (Toorajipour et al., 2021). The hedonic models are known for utilizing dummy variables which are in very large numbers.
Another advance in AI is developing a pricing system that encompasses the combination of the GA with simulated annealing, Swarm intelligence, and hill-climbing techniques. The pricing systems have been developed mainly to optimize the price and production policies for products and services. With such a support system in place, there will be a reduction of the implementation and, more importantly, the management model, which has increased in its expressiveness.
According to Wei and Zhang (2018, as cited in Toorajipour et al. 2021), AI has influenced consumer behavior. Several techniques have been combined in developing the current AI approaches. They go further to state that the Target strategies for marketing do coming both the rule of association with tree-based models that exist in an integrated model. The combination is aimed at predicting or forecasting whether the customer will buy a product. The results have been confirmed to be better when compared to other models. Therefore, the development of technology has had a positive outcome whereby effective sales management has been registered. The digitization shift will eventually cause many implications directed to the person selling the management functions of sales (Syam and Sharm, 2018, as cited in Toorajipour et al., 2021).
In the case of sales, Google Analytics(GA) has been applied to develop the online system whereby the bullwhip effect is minimized on the Supply chain. The supply chain is whereby the ordering policy for every individual supply chain member is determined. There will be an eventual reduction in prices and the minimization of the bullwhip effect. In addition, the Account Bases Selling (ABSs) have been developed to develop the sales management model that can predict or forecast the forthcoming economic conditions that make decisions related to sales. Therefore, the models which are AI-based outshine the traditional methods of predicting or forecasting. The idea of concept marketing has since gained popularity overages. Therefore, producers can freely customize their products to suit the customers uniquely and profoundly (Ladyzynski et al., 2019, as cited in Toorajipour et al., 2021). By doing so, the campaign is made efficient, and the costs are reduced.

Recent Role of Artificial Intelligence (AI) Technologies for Supply Chain Management
Many techniques are used in SCM organizations. According to Toorajipour et al. (2021), some techniques are used more than others. An excellent example of the technologies used for Supply Chain management is the Artificial Neutral Networks (ANNs). The ANNs, according to Toorajipour et al. (2021), are the information processing technology normally applied to find patterns and models from massive amounts of data and knowledge. The ANNs normally are based on mathematical form regressions that relate the output and input streams resulting from and to the units. The models mentioned in this case largely depend on the massive amount of experimental data. Therefore, the ANNs are considered the main technology used in computational intelligence since it is impressively versatile (Kasabov, 2019, as cited in Toorajipour et al., 2021). ANNs have become very popular in the current business since they can solve problems associated with intensive data, whereby rules or algorithms for coming up with the solutions to these problems are unknown.
Secondly, another technique used in SCM organizations is known as FL modeling. The technique shows the border existing between and Artificial Intelligence and techniques which are non-artificial. The FL theory was introduced a very long time ago, but it has become one of the prominent techniques used to develop complicated systems and models. Its growing popularity is because it has an approach address system that conveys qualitative information just the way humans behave and make decisions (Toorajipour et al., 2021).
Another technique that has been used recently for supply chain management is the Agent-Based Model(ABSs). According to Toorajipour et al. (2021), The ABS, in this case, is a computation model type that simulates processes and interactions of the agents either individually or collectively. The simulation is done while assessing their influences on the system. Therefore, they also say that the technique does merge the elements which are from the very complex systems, computational sociology, evolutional programming, and game theory. In simpler terms, the agents involved are but entities that can perceive the surrounding environment then act autonomously to provide solutions to problems. When the agents come together, they form Multi-Model Agent-Based Models (MASs). Therefore, it forms a network of agents that function as a single piece of the software that contains the code and data. The MASs can model, design, and implement very complex systems. Since the early and mid-90s, the agents have been employed in Supply Chain Management organizations to solve specific problems (Toorajipour et al., 2021).
Toorajipour et al. (2021) say that the most influential Artificial Intelligence technology in SCM Literature is the Genetic Algorithms (GAs). They describe GAs as the search engine which mimics the natural selection whereby the algorithms are notably seen evolving to a point that has solved the problem. GAs was first introduced in the 1970s and can be described as a group of computational models which has since been inspired by evolution. The algorithms mentioned in this case are responsible for potential encoding solutions to a specific problem using a particular structure of data with a close resemblance to chromosomes. They there employ the operators to the structures in such a way to maintain the important information. GAs is therefore regarded termed as function optimizers since many problems that GAs has been involved in solving are quite many and extensive.
Another very important technique used in the Artificial Intelligence- Supply and Chain management organizations is the Support Vector Machines (SVMs). The SVM is the approach that uses a linear classifier that aims at classifying data (Peter et al., 2019, as cited in Toorajipour et al., 2021). The SVMs were first introduced on the market in the 1990s, and since then, they have been widespread and used in many applications. Some of the studies that have used the SVMs approach include Supply chain demand forecasting, time series classification in SCM, supplier selection, and last but not least, the design process of the system for SCM networks.
The Impact of Supplies Shortage On the Supply Chain Process During The COVID-19 Pandemic
During the surge in COVID-19 infections, many businesses collapsed dramatically. Many businesses such as hotels, recreational areas, and restaurants experienced shortages in supplies and have been trying to reopen since the situation has worsened. In the US, the businesses that faced a collapse have been struggling to keep pace with the need for workers, rising daily (Helper and Soltas, 2021). The situation has not been the same, especially when it dawned on them that their product has been put at risk because of the shortages of inputs from the businesses.
The shortage of inputs from these businesses there disrupted the entire process of supply chain management. Helper and Soltas (2021) say that the economy and the retail sector recorded meager results in March 2021. There were low ratios that measure or show the number of days the sales that the retailers and the businesses could comfortably support. During the COVID, businesses completely stuck with goods or products that cost money. The unsold goods worth billions of money caused the inventory to sales ratio to rise for some time until the businesses decided to liquidate the inventories. When the economy recovered from the hit of the pandemic, these businesses could not bounce back fully to the levels they were at before the pandemic (Helper and Soltas, 2021). Failure to bounce back to these levels leads to a decrease in the inventory to sales ratio.
According to Helper and Soltas (2021), it was noted that while the retailers only had around 43 days of inventory by 2020 in February, they had only 33 days in 2021. Other inventories that recorded lows were the cars and homes, whereby the inventories for cars sales were just one month while that of the home sales were 4.4 months. Compared to the pre-pandemic time, the inventories were two months for cars, and homes sales were 5.5 months. Therefore, low inventories cascaded issues in the industrial SCM in the US and generally the rest of the world. It was reported that between May 31 and June 6, approximately 36 percent of the small business recorded had reported delays in the domestic suppliers, whereby many delays were reported in the manufacturing, trade sectors, and construction.
Another negative result of the shortages that were reported included the abrupt rise in prices. It was recorded that between May 2020 and May 2021, the prices of many products which were tracked within the producer price Index were recorded to have risen 19 percent. The increase was the largest since 1974; the effects were undesirable. Supply chain disruptions have been noted too in the consumer prices, specifically in the motor vehicle sector. Helper and Soltas (2021) stated that the automakers underestimated the demand when the pandemic kicked off. Since they expected low demand, they had to cancel the orders from their semi-conductors.

How Artificial Intelligence (AI) Support supplies shortage on the Supply Chain Process
There are many ways through which Artificial Intelligence can help make sure that the supply shortage or the supply shortage disruption has been supported. First of all, AI can support through Predictive analytics. With the concept of Predictive analytics, no company will be blind to the changing market trends. The AI-enabled predictive analytics have been of great help in ensuring that there is forecasting which is done by critical analysis of data (Melendez, 2020).
Secondly, AI can also help in the supply shortage through deep learning, providing visibility into a remote storage location. According to Melendez (2020), Video surveillance can be combined with deep learning-based models to ensure that the managers can comfortably determine whether the safety protocols are being followed correctly. The video surveillance will also be of great help in determining whether the empty shelves have been filled with required products.
AI can also support the shortages in the supply chain through warehouse robotics, whereby items can be picked and packed substantially, thus increasing the speed of production and the efficiency in the SCM organizations. In case workers are reportedly sick, they can decide to remain at home as robots can now take up their responsibilities to ensure that the process is run uninterruptedly (Melendez, 2020). Robotic process automation can also aid any future shortages in the supply chain. Therefore, some tasks are repetitive and can be programmed on their own to avoid supply chain disruption. According to Melendez (2020), Robotic process automation can be applied to make these tasks automatic, thus freeing many workers from handling or doing simple tasks. Therefore, the employees can concentrate on other strategic tasks to avoid shortages in supply.
Last but not least, Artificial Intelligence can also support the shortages through computer vision. The AI-based image detection can be applied in the transportation and logistics system aimed at helping the high traffic areas and help detect and help get the best trucking routes (Melendez, 2020). In addition, Computer Vision algorithms play a crucial role in analyzing the digital images and video from the satellite imagery aimed at spot and count. For instance, buses and cars in some areas can help truckers use an alternative route if there is a damaged train that can hinder the smooth and quick supply of the products.

Key Findings and Discussion
The key findings were well developed and recorded in detail under the literature review. Among the detailed findings, there is a key of them that can be summarized in this section. Firstly, the research findings stated that the most important section in the SCM where is applied is value creation. Under value creation, it was noted that Artificial Intelligence aids in carrying out the functions such as forecasting demand and optimization, production, promotion, and pricing and delivery. Many businesses have decided to use Artificial Intelligence to modernize their operations to increase speed, ensure high accuracy of the results and security for their businesses.
Secondly, Artificial Intelligence has helped in Supply Chain Management. Many technologies have been confirmed to be working towards making sure that the operations in the SCM are carried out perfectly well. In that case, therefore, there are Networks like Artificial Neutral Networks which have had an impact in the digital world of the SCM. The ANNs market the Decision Support Systems by making sure that it is more accurate. Furthermore, the ANN-based marketing support has provided an automated classification of the unknown cases. Other Models include the Fuzzy model which is very powerful in literature being used in the pricing of the products in the SCM.
Thirdly, many technics have been employed recently in SCM organizations to carry out certain tasks. An example is the ANNs which have been discussed above and the FL modeling which is used to develop complicated systems and models. The FL modeling has been proved effective since it produces information that is qualitative and therefore does exactly what humans could do. There is also the Agent-Based model (ABSs) which is a computation model that simulates the processes and interactions of the agents. The ABSs can link up to form the Multi-Model Agent-Based Models (MASs) forming a network of the agents that function as a single piece of the software containing the code and the data.
Fourthly, it was discovered that COVID 19 has had adverse effects specifically the Supplies Shortage on the Supply Chain Process. A case of the USA was studied whereby many businesses had to pace up with the need for workers. Their products have been put at risk since they experienced shortages in the inputs. For example, the economy experienced low ratios that measured the number of daily sales that the businesses and retailers could record. Compared to the pre-pandemic times, the prices of items rose during the pandemic that made many businesses cancel the orders with an assumption that the consumers may not buy. They experienced supply shortages when the situation drew close to normal in the year 2021.
Lastly, it was noted that Artificial Intelligence can support Supplies shortage in SCM organizations. Firstly, AI can aid the shortages through Predictive analytics whereby the future market situation is forecasted. The forecasting will help avoid the tendencies of having empty shelves. Secondly, the AI could also aid through computer vision whereby cameras are placed strategically to monitor the buying and selling. In case of the items that are running out of stock, the remote storage devices will be of help to realize the shortage and be in a position to solve it. Thirdly, robotics can help future shortages by fastening the production process of items to be brought on the market. Robotic process automation can be applied in such cases to enable workers to concentrate on other tasks.

Conclusion and Recommendations
In conclusion, there has been a significant advancement in technology which has aided the growth and the complexity of the Artificial Intelligence application. This research aimed to clarify and find out how AI can be used in SCM organizations and the roles it can play in achieving its set goals. Nine sources have been examined, with which there are other sources cited to clarify the knowledge of the AI techniques in solving the SCM organizations’ problems. From the information gathered, the ANNs become the most powerful technique, which is also the most prevalent used to find the complex ways that mere human beings cannot get. ANNs and other techniques can be used in many problems categorized as per the organization’s needs. Other techniques, including GAs, have been of great help in mimicking the natural human knowledge to tackle many problems in SCM organizations. SCM organizations should therefore apply Artificial Intelligence to fasten the process of production and automate its operations to avoid the shortages as it had been experienced during the COVID 19 Pandemic.

Bughin, J., Hazan, E., Ramaswamy, S., Chui, M., Allas, T., Dahlstrom, P., … & Trench, M. (2017). Artificial intelligence: The next digital frontier?
Dale, M. (2018). Automating grocery shopping. Imaging and Machine Vision Europe, (85), 16-20.
Dash, R., McMurtrey, M., Rebman, C., & Kar, U. K. (2019). Application of artificial intelligence in automation of supply chain management. Journal of Strategic Innovation and Sustainability, 14(3), 43-53.
Hamet, P., & Tremblay, J. (2017). Artificial intelligence in medicine. Metabolism, 69, S36-S40.
Helper, S and Soltas, E. (2021, June 17). Why the Pandemic Has Disrupted Supply Chains. Whitehouse.GOV.
Kohtamäki, M., Parida, V., Oghazi, P., Gebauer, H., & Baines, T. (2019). Digital servitization business models in ecosystems: A theory of the firm. Journal of Business Research, 104, 380-392.
Melendez, C. (2020, October 28). Five Ways AI Can Solve Supply-Chain Disruption. Supply Chain Brain.
Seligman, J. (2018). Artificial intelligence and machine learning and marketing management. Lulu. com.,+J.+(2018).+Artificial+intelligence+and+machine+learning+and+marketing+management.&ots=-sCkL41g-u&sig=fr2GwIDuNmMBUqoyBjHo7P5wTQI&redir_esc=y#v=onepage&q=Seligman%2C%20J.%20(2018).%20Artificial%20intelligence%20and%20machine%20learning%20and%20marketing%20management.&f=false
Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P., & Fischl, M. (2021). Artificial intelligence in supply chain management: A systematic literature review. Journal of Business Research, 122, 502-517.

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