Supply chain management is defined as managing the steady movement of commodities and services, which also includes converting raw materials to finished goods. Most, if not all, businesses use technology to assist with their supply chain management processes. This is because technology has proven to be faster and more efficient in delivering goods. One such technology is AI, which stands for Artificial Intelligence, and it is when a computer or a robot do tasks that humans usually do. This kind of technology has its origins in the mid-twentieth century. A knowledgeable person named Alan Turing analyzed how artificial intelligence would work from a mathematical perspective ( Anyoha, 2020). However, Marvin Minsky and John McCarthy hosted the first artificial intelligence project in 1956. This project aimed to assemble the most outstanding researchers from different fields to discuss the technology.
Unfortunately, the meeting was a failure because everyone disagreed on standard methods of that field. From the late fifties to the mid-seventies, AI seemed to be doing well while still battling a few setbacks, such as funding. In the eighties, it was given a massive boost of millions of dollars in funding by the Japanese government, which lasted until the early nineties. Despite support from the government, most of the goals were still not achieved, which led to the dwindling of AI technology. It turns out that without government support, this technology started flourishing. This is backed up by the defeat of world chess champion Gary Kasparov, who lost to a match to a computer. This has proven to be a significant step towards AI improvement.
On the other hand, Supply chain analytics is when a company analyses data derived from applications bound by its supply chain, for example, inventory management (McCue, 2020).
The use of analytics goes as far as the nineteenth century when Frederick Taylor started performing exercises to improve time management. In addition, Henry Ford was known to estimate the speed of assembly lines. In the late sixties, significant attention on analytics started because computers began making decisions. The creation of big data and cloud computing made data analytics evolve. In the previous years, supply chain analytics were restricted to statistical analysis, which predicted demands for organizations and estimated the success of companies. All that changed at the beginning of the early 21st century due to ERP (Enterprise Resource Planning) implementation that made it easy for businesses to comprehend their supply chain performance. Currently, several major companies have benefitted from AI and analytics. One such company is LLamasoft, a significant business that deals with supply chain management. It was founded in 2004 with headquarters in Ann Arbor, Michigan, and it focuses on designing software that assists organizations in improving their supply chain management (Forman, 2015). The firm is known for including AI in its activities. Some of its expertise consists of optimization, simulation, and machine learning. In addition, LLamasoft was named as a quickly growing private corporation worldwide. It has various subsidiaries and a robust network of distribution. LLamasoft also provides contemporary solutions that encourage the use of AI.
There are certain features of AI that LLamasoft enjoys during operations. An example is machine learning, which trains computers to do what is naturally done by humans (TechVidan). The use of chatbots is also applied in LLamasoft. This is done by creating supply chain algorithms that are purposely made to provide new ways to answer questions that a customer might have. The bots are programmed to be intelligent and powered by AI, which dramatically advances them.
Data ingestion is another feature of AI that LLamasoft uses. It transfers knowledge from mixed sources to a storage medium, in which the data can be accessed and examined by the company. This improves its supply chain analytics, which is powered by artificial intelligence. An additional AI platform used by LLamasoft is known as Digital Twin. This digital portrayal of the whole supply chain enables its customers to look into options and assess risks across business purposes (SupplyChainBrain, 2020). This technology also consists of App Studio, in which exclusive apps are created for business users to take advantage of improved algorithms and examine specific business questions. Since LLamasoft designs supply chain networks using neural networks, it applies quantum computing to resolve challenging physics problems. This is done with the aid of supercomputers and the networks mentioned above.
On the other hand, analytics consists of features such as stream processing, which generates insights from numerous data streams (Lawton & Essex, 2020). Data resolution, also another characteristic, is its ability to divide data into smaller parts for easier understanding. It also employs Digital Twin, which arranges data into an understandable model. This model is then shared amongst different users to enhance predictive analysis, a concept of supply chain analytics. Also inclusive of characteristics is its ability to track insights from data, which is used to comprehend and enhance supply. Analytics has shown that it is capable of structuring information connected to elements that make it simpler to locate links, recognize patterns and enhance traceability of goods and facilities.
In recent times, AI has become an emerging technology in supply chain businesses. This is proven that corporations reduce the difficulty in the supply chain and speed up responsiveness using artificial intelligence tools (Hanifans & Timmermans, 2018). Companies, through artificial intelligence, are increasing in areas where intense knowledge is required. Examples of such sites are supply chain management and inventory tracking. With the help of improved analytics, AI will make it easy for supply chain planners to create more intentional decisions and spend little time on susceptible problem-solving. Because of its slowness, the planners will take the lead in transitioning from a typical supply chain operating model to a new, more potent model.
LLamasoft, for example, has AI technology that major companies like Boeing and Nestle use, and it has helped these businesses make clever supply chain decisions (Harrison, 2020). Supply chain leaders are also preparing their workers for this change, even committing themselves to transfer workers to sectors that add more value. This proves that artificial intelligence is rapidly emerging in supply chain management.
Analytics is also a trending technology because it is known to plan while also displaying precise forecasts, allowing businesses to meet the required volume of data. Studies show that most companies have excessive or minimal inventory, which is not suitable. This is where the use of supply chain analytics comes in, when they make it easy to determine the correct amount of inventory to reduce costs.
Artificial intelligence has several functions in supply chain management. It enables an organization to streamline procedures and operations in transportation and inventory (Kelley, 2021). The AI engineers also ensure that it is efficient and productive for the supply chain management of a business. This technology also uses predictive analysis, which demands anticipation of factors like volatility and varying demand in supply chain management. It can also be of great use in inventory management by increasing inventory levels in particulars such as regions.
AI has altered the traditional methods of supply chain management in various ways. It has proven to be faster and more efficient than conventional models. Artificial intelligence is also cost-efficient and increases business revenue (Hunt, 2021). AI has also taken over some of the roles performed by typical workers in a company.
Analytics ensure that businesses assemble, analyze and use data provided by their supply chains. They permit companies to make quick alterations and durable strategic corrections that will be advantageous to their rivals. They are also instrumental in acting upon historical information to foretell what consumers will order, ensuring that the business has an idea of what to expect in the future. Analytics are also crucial for strategizing sales and operations. They do this by producing and buying what the company needs to cater to predicted demand. They are also used to control inventory by finding the percentage of a good sold to customers and which SKU (Store Keeping Unit) it requires to refresh.
Since it has been established that companies use AI in supply chain operations, it is definite that this type of technology has some advantages (Helo & Yao, 2021). It ensures error-free inventory management by monitoring the correct flow of products in and out of the area of production (Jacobs, 2020). Safety is also another merit that AI has brought. Clever planning and successful warehouse management, which are also perks of AI, contribute to the safety of workers and materials. Artificial intelligence also analyzes work data and alerts manufacturers about any potential risks.
Another very significant benefit is the reduction of operation costs, which is crucial for the company’s finances. An example is when the manufacturers employ warehouse robots that work at high speed while producing massive productivity. This reduces the time that human workers spend doing a specific task, which means more money would have been used to pay regular workers. A concept of AI known as cloud computing makes it easy for a company to acquire the technology as needed, either renting or buying it. In addition, AI is always available, which suggests that it operates uninterrupted is free of outage. Since it has market potential, AI is versatile in that it can be applied on any industry apart from supply chain.
When companies can get their hands on instantaneous analytics, they better grip their gainfulness, minimize stock outs and get used to varying consumer needs. Such knowledge assists companies in boosting their distribution of resources, which enables cost savings. Supply chain analytics help company leaders to make good choices with the aid of deep supply chain data. Many firms focus on digitally doing things, making it simple for supply chain analytics to achieve this goal. It has been proven that analytics are vital in locating patterns and displaying essential insights. Their ability to show current supply chain risks and predict the future is a significant benefit. Analytics assures exclusive services to customers, which bring about customer satisfaction.
Artificial intelligence does have a few factors that block its adoption in the supply chain of businesses. For companies to use AI, they need to set ambitious goals on innovative technology, for example, digital conversion. A Deloitte survey indicated that this particular goal was not prioritized in many companies, hence the failure of artificial intelligence. The lack of a strategic plan related to AI is another challenge. This is true in cases where there are no leaders to showcase the concept of artificial intelligence (McKinsey, 2018).
Since AI systems are based on cloud computing, a vast bandwidth is needed to power them. This can sometimes lead to system difficulties. AI operators require specific hardware to gain AI proficiency, which can be expensive for the business. Training workers on how to use artificial machines costs both time and money. This is likely to affect the company’s efficiency. There is also the cost of operating the AI machines. This is backed by the fact that replacing and maintaining the parts of the machine can be very costly to service.
Given that most AI systems are expandable, another challenge presents itself. This is because commencing start-up systems are required to display effectiveness. Considering the distinctiveness of all AI systems, supply chain associates will spend time discussing scalability with their system providers.
Supply chain analytics is expensive, especially for those organizations that do not have access to the systems that assemble these insights. An additional issue is finding skilled personnel to handle and interpret some analytics. This is true if the company does not have employees who have studied data science, regardless of the software that shows analytics to be simple. Supply chain analytics works in a firm capable of producing forces to gather the needful data. This data needs to have a smooth flow from all relevant systems so that the analytics can display the current status of the company’s supply chain. Tactical challenges of analytics may also occur. Companies may fail to adopt long-term plans that support supply chain analytics (Khan, 2019).
Implementing AI in a business can be either a success or a failure. There are a few reasons why it has failed. Companies lack enormous and pure data, which is needed to instruct algorithms and produce predictive models (Duckworth, 2019). Firms need to advance the standards of their data through effective management. Corporations that apply AI in bits tend to experience negative results. This is because companies do not strive to include important data, hence missing the chances of planning and expansion. It is also crucial that businesses understand what they are dealing with when applying AI to their supply chain management. Semantic networks are mysterious and challenging to understand, which is a reason for the failure of AI. The system used by the company should be easy to understand, and essential knowledge needs to be possessed. Lacking skills is also another factor to consider. With artificial intelligence spreading rapidly, some companies do not have the modern skills to utilize the technology properly.
However, success in the application of AI has also been observed. Companies with access to new data tend to enjoy AI implementation since it relies on new data to improve conventional enterprise systems (Brady, 2017). Businesses are also prioritizing customer service levels at the lowest cost. In addition, companies are making decisions concerning AI are very considerate about factors such as the expense of change. This is because constant change will lead to more costs than savings and reduce the capability to execute plans. Another reason for the success of AI execution among companies is making sure that artificial intelligence engines are expandable. For optimizing a whole networked group of customers to suppliers, businesses ensure that their systems deal with enormous volumes of data faster.
Many reasons are contributing to the success of analytics in supply chains. Companies using analytics ensure consistent flexibility (Coon, 2019). This means finding a supply chain that is either nimble or responsive, be efficient, or low in expenses. The business can determine which one of the mentioned features is crucial for execution. Companies also ensure that there is data analysis. According to Steinberg, data is very significant as a fundamental element. Without accurate data, analytics are unlikely to succeed because it is related to decision-making.
Applying artificial intelligence in supply chain management for firms has its costs. There are several factors to consider when analyzing the costs of AI execution. The software that the company chooses to install is one factor. Examples of AI software include chatbots, voice assistants, and CT scan machines. The costs change based on the software’s difficulty, purpose, and performance (Sanyal, 2021). The achievement of AI algorithms is a vital factor to consider. Enough algorithm performance is crucial because correct predictions need a few rounds of adjustment, which increases the cost of applying AI. In other words, the more the accuracy of the AI predictions, the more expensive it is to implement AI technology. It is also wise to consider the amount of data used by the AI software. The performance of AI is dependent on the data loaded in the systems, and it is capable of absorbing orderly and disorderly data. Concerning costs, tidy data is more affordable to work with if there is sufficient data to advance the algorithm’s accuracy.
According to the usual misconception, AI is very costly. While that might have been the truth in the past, things have changed due to modern technology. Because of frameworks and some pocket-friendly tools, AI has become available to many small organizations, and the expense of its application has been reduced.
Analytics is critical and valuable, but any client would want to know the cost. Luckily there is a regular and free version for small and medium-sized businesses. Large corporations pay up to $25,000 per terabyte every year (Atscale, 2019).
Scientific steadiness between physical existence and the AI model has to be present. The programs have to be taught how to portray reality, and if successful, they need to be precise (Brown, 2021). AI teams have to regulate data or its algorithm to produce steadiness using correlating activities. There is also implementation consistency between the model and its solution. An AI model needs to attain desires and keep away from unintended outcomes.AI installation requires scrutinizing activities, which focuses on results and modifies the model’s restrictions and error if necessary. Also, when using a neural algorithm, some organizations disable ongoing learning for the neural network. This ensures that the outcome would remain the same and replicate.
Finally, stakeholder steadiness between the quick fit and stakeholder needs is essential. This is advantageous to supply chain managers, workers, shareholders, and consumers. Consistency occurs when AI software produces value comprehensible to stakeholders, who can also gain from it. Creating value focuses on expenses, advantages, risks, and problem fixing. It is also wise for the business to manage the strength that fashions its AI programs. This is a role meant for the supply chain leaders because they need to adopt new activities that assist AI solutions and create means of learning getting used to AI.
A company must analyze how changing tax codes will affect operations for analytics to succeed. A few factors to be considered include inventory, expenses of procedures, regular product recognizers, percentage of electronic data capture, and granular metrics. Concerning stockpile management, companies need to possess stockpile locating methods for perishable products, like the ones in food companies. This is because perishable goods can be a liability in the sense of wasted effort and money.
The use of granular data can be beneficial for mistakes of costly materials (Clark, 2021). An excellent example is when a company is locating supply costs for every good used for a specific order of a particular customer. Collecting automated data is very helpful because visibility can lead to implementing many enhancement strategies and supply chain. Metric managers are also required to improve their standards by including things like serial numbers, to strengthen tracking of distinctive product recognizers. Observing these as elements used in the production process is vital for analytics to succeed.
Another crucial factor that firms consider when trying to make analytics a successful adoption is finding workers who can interpret analytics, preferably those familiar with data science.
Artificial intelligence will and still is benefitting firms. An example of a company that is gaining from an AI-powered supply chain is Coupa, which acquired LLamasoft in November 2020. Coupa, globally renowned as a Business Spend Management company, bought LLamasoft at $1.5 billion to improve their supply chain with superior technology. This expansion has proven to strengthen its supply chain by increasing the value of Coupa. The pandemic had affected the operations of many supply chains; hence the takeover came at a very crucial time. LLamasoft’s latest creation, llama.ai, provides decision-making powered by AI across an endless number of users. This creation allows Coupa and other businesses to develop special-purpose applications supporting end-to-end models and using authorized supply chain algorithms. CEO of LLamasoft Rauzat Gurav was pleased that the takeover had happened because they will combine Coupa’s market and their AI-controlled supply chain analytics. This would form a chance to unite digital conversion solutions that improve decision-making and efficiency. The acquisition will also have merits for Coupa’s customers in that it will provide a supply chain for them, especially the software technology companies. Coupa aims to identify sectors where technology is not enough and provide AI programs, creating value for them.
Economies are also benefitting from this technology because international trade will be boosted by optimizing current supply chain models. Several concepts of AI can be applied in every part of international trade. Through global value chains, AI services help firms in trading. This is due to their ability to save expenses, time, and difficulty delivering on export chances. The power of artificial intelligence to inspect data and recognize patterns helps find out the validity of the trade. A group of professionals known as BACUDA (Band of Customs Data Analysts) has created a neural network known as DATE (Dual Attentive Tree-aware Embedding). It serves to uncover low-valued imports while approximating revenue generated from these imports.
Data analytics are helpful in firms in that it helps in predicting future orders and demand forecasts. They can also be on the lookout and foretell items running out of stock before the manufacturers find out. Analytics are also changing the rate at which shipments arrive late to their destination (Ittman, 2015). They do this by reducing the burden of late or incomplete shipments. Firms are also witnessing data analytics evaluating risks and problems that may arise. Data analytics have also provided firms with cheaper data storage, reducing expenses. The processing power of analytics is always increasing and, combined with great speed, is essential for firms in their operations. It also provides better tools, which make analysis easier for companies. Firms can easily estimate the pricing and assignment strategies in cases where no historical data is accessible. This indicates how effective analytics are to firms. Because of how much analytics are associated with predicting, they can foretell the optimal stockpile required for advertising and suggest the appropriate time to ship the inventory.
Studies have indicated that supply chain firms expect the amount of AI in their procedures to double in the next five years (Jacobs. 2020). While global chain supplies are becoming increasingly tricky, errors are decreasing due to the implementation of AI. Due to advancing competition in the digital sector, AI technology focuses more on enhancing productivity by minimizing uncertainties of any kind. Market fluctuation, which has been made worse by the pandemic, has raised the need for AI to be more flexible in the future (McKinsey, 2021). Years to come, AI will be foretelling demands around some product segments. It will also be able to recognize trade-offs with a vast amount of interlinked variables actively. In addition, supply chain managers will be adopting AI quick fixes such as optimization to control the broader value chain. For AI not to fail in businesses in the future, leaders will ensure that goals can be achieved and adapted to flexibility effects, such as production halting and transport disturbance. Coupa, for example, had the purpose of satisfying its customers by working with LLamasoft’s cutting-edge AI technology. Using LLamasoft’s AI, Coupa intends to help businesses make wise supply chain decisions at an advanced speed.
Supply chains powered by AI have been systematically strengthened, especially when COVID-19 has forced supply chains to be more effective (Modgil, Gupta, Stekelorum & Laguir, 2021). The presence of powered supply chains in active settings makes it easy to identify risks and failures. AI has demonstrated how it has improved the foretelling of demand and supply for businesses. This is a great time-saver, increasing competency (Zeisl, 2020). Transport and delivery among companies have been enhanced and well planned by AI technology because it helps predict peak hours and the fastest routes. This leads to perfect delivery of products, hence client satisfaction. Also, companies have been able to find dependable partners with good insights on AI-powered supply chains, like the case of Coupa and LLamasoft.
Companies have discovered that analytics effectively cuts expenses and enhances the client experience in recent periods. Because of this reason, analytics are likely to be implemented by many firms, as it is a crucial factor in realizing most corporations’ goals for supply chain visibility. In addition, the market for supply chain analytics is expected to surpass $10 billion three years from now, not ignoring the fact that it has a yearly compound growth of 16%. It is also said that analytics will be able to generate even bigger groups of data in the upcoming years. This is because many corporations are in the process of transforming digitally for the sake of their operations. Technology providers are also said to support various types of technologies under the same category as AI, analytics being one of them. Soon, it will be the only way for businesses to gain from large volumes of data coming from their supply chains. Cognitive and prescriptive analytics are unavailable to small-scale businesses because of both capital and human funds needed. However, that factor is changing, and they will become available to all companies, considering most supply chain businesses are already implementing AI into their systems, and enterprises are also enjoying the same benefit.
It is a known factor that various types of technology are used in supply chain systems. Most companies are now incorporating technology into their supply chain management for various reasons. Technology has proven to be very efficient, and it saves time. Technology can do what humans can do but in a simplified way. This has led businesses to use standard and constructive forms of technology, artificial intelligence (AI), and data analytics. The use of these two types of technologies goes back to the twentieth century when each had formed its foundation. Alan Turing first spoke of AI, who explained that computers had programs to read what they encountered and write more symbols. He claimed that computers could modify and improve their program. It was not until the late twentieth century that AI evolved more, demonstrated by the loss of chess champion Gary Kasparov against a computer match. The concept of data analytics was practiced in the late nineteenth century, but it was in the late sixties that it gained more attention. The development of cloud computing and big data contributed to the growth of analytics. AI and analytics are trending fast because they have made supply chain processes easy and accelerated responsiveness. It has advanced analytics aid companies in executing beneficial decisions. A company called LLamasoft is very famous for its utilization of AI in the supply chain, and its technology is beneficial to large businesses using it. This shows how useful AI is to companies because it helps businesses make wise supply chain decisions.
Both technologies have features that enable them to do their jobs effectively. AI consists of machine learning, which is responsible for training computers to perform human tasks. Data ingestion involves transferring knowledge from mixed sources to a storage medium. AI also applies chatbots, whose function is to answer questions. Another feature that both AI and analytics share is Digital Twin. It arranges data in such a way that it is comprehensible. Analytics on its consists of stream processing, which means it gets insights from many data streams. Data resolution allows it to divide data into small pieces for more straightforward comprehension. AI and analytics have different functions. AI allows a business to simplify operations and transport, and inventory. It also employs the use of predictive analysis for anticipating things like volatility. Analytics ensure that firms assemble and use data distributed by their supply chains. They also use historical data to predict what the business should anticipate later. Another crucial feature that analytics possess is perfect organizational skills. It does this by structuring data that is linked to hidden elements, in such a way that it is easy to recognize patterns. Capability of finding insights from position data, for the purpose of enhancing distribution.
Both AI and analytics have shown that they can be advantageous. Their work has no errors because they manage inventory by overseeing the correct flow of products. AI also ensures safety for the worker and equipment. Both technologies reduce operation expenses, an essential aspect in companies. Several factors make it a challenge to adopt them. Some businesses do not have clear and ambitious goals in terms of digitization. Because of this, they do not prioritize technology, which makes it hard for them to use these concepts. Training employees to adapt to AI and analytics is another reason some businesses find it tough to implement these technologies.
Some companies are finding it hard to keep up with the cost of operating the AI and the analytical machines because of servicing and maintaining them. It does cost money to install them, which is dependent on factors like the software that the business wants to be installed in their work areas. However, AI is now affordable to many. As far as the future is concerned, things are looking good for both concepts. AI will and still is being implemented by many corporations to improve supply chains ruined by the pandemic. Analytics is expected to exceed a value of $10 billion in a few years to come. This is mainly because many organizations are in the process of altering themselves digitally. Adopting these two technologies will be on the rise because most supply chains are technologically enhanced.
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