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Introduction
Background
Industry 4.0, which refers to the fourth industrial revolution and also known as 4ILR, is a digital transformation in the field of manufacturing, production, and processing that emphasizes the need for the use of artificial intelligence (AI) when making decisions and undertaking specific actions. Information has become a critical factor that defines the success or failure of a given business (Yuanita, 2019, p. 15). As such, firms are under pressure to find the best ways of processing large data within the shortest period possible and use it to make critical decisions.
Traditionally, humans would be expected to process the data using simple machines and then make relevant decisions. However, that is changing with the emergence of concepts of Big Data and Machine Learning (ML). It is becoming increasingly possible to rely on machines to collect and process data, and then use it to make critical decisions and predictions more accurately than a human would (Tirkolaee, Sadeghi, Mooseloo, Vandchali, & Aeini, 2021, p. 13). As such, AI has gained popularity in the field of supply chain management (SCM). In this study, the researcher focuses on the benefits, opportunities, and challenges of embracing ML technologies in logistics to help SMEs boost their performance.
Research Problem
Micro, Small & Medium Enterprises (MSMEs) play a critical role in emerging and developing economies in terms of the creation of employment and the growth of gross domestic products (GDP). However, they face unfair competition from large multinational corporations that have the financial power, experience, and the right connections to facilitate their growth and embrace emerging technologies. Adopting industry 4.0 has been a major challenge for these small and medium enterprises because of the financial implications and expertise needed. Despite the challenge, Singh, Wiktorsson, and Hauge (2021) explain that industry 4.0 offers a unique opportunity for these firms to redefine their operations and compete favorably with the more established rivals (p. 67). Embracing AI in SCM makes it possible to reduce expenses on human resources, improve efficiency in operations, and enhance the quality of products that they deliver.
Research Objectives
This study aims to establish a practical and clear of the benefits, opportunities, and challenges of adopting machine learning technologies in logistics to help SMEs boost their performance. To achieve this primary goal, the researcher will focus on realizing the following objectives:
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To identify ML techniques frequently used in logistics in SMEs;
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To identify the benefits of ML in Logistics in SMEs;
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To identify the opportunities of implementing ML techniques in logistics in SMEs;
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To identify the challenges of implementing ML techniques in logistics in SMEs;
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To identify the relationship between ML adoption in logistics and MSMEs performance.
Research Questions
Artificial intelligence is a field that has continued to attract the attention of many scholars over the years. It is essential to specifically define the specific issues that need to be investigated in a given study. In this research, the following are the specific questions that the researcher seeks to answer:
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What are ML techniques frequently used in logistics in SMEs and what are the benefits and opportunities of adopting them?
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What are the challenges of adopting ML techniques in logistics in SMEs?
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What is the relationship between ML adoption in logistics and MSMEs performance?
Overview of the Current State of Research and Methodology
Artificial intelligence and its relevance in supply chain management is a field of study that has attracted massive attention from scholars over the years. According to Singh et al. (2021), scholars have been keen to understand how AI can help MSMEs to acquire materials they need for production more efficiently and cost-effectively (p. 69). However, a preliminary review of existing studies shows that this is a relatively new field as AI continues to evolve. In this project, the researcher will rely on secondary data sources to help achieve the aim and objectives identified above.
Evolution of Artificial Intelligence
Artificial intelligence and machine learning have become increasingly relevant in the modern business environment. It is necessary to discuss the evolution of this technology and how it gained relevance in different fields (Rabayah, 2013, p. 8). The origin of AI can be traced back to Europe in the early 20th century, especially in the United Kingdom and Germany. Mathematicians, scientists, and philosophers have conceptualized the idea of having intelligent machines capable of solving human problems in the 1950s. Several scientists were able to develop a logical framework in 1950 referred to as Computing Machinery and Intelligence, which would later redefine the current use of AI in supply chain management (Pervaiz, 2020, p. 26). He explained how machines can be trained to be intelligent and ways in which their intelligence can be tested.
Stages of AI Growth
The pioneers in this industry faced numerous challenges that slowed the growth of this industry. One of the biggest challenges was the accessibility of the computers at this time. Sendler (2017) explains that leasing a computer in the 1early periods of its development was an expensive undertaking, which meant that only prestigious and financially empowered institutions could afford it (p. 87). He describes computers at that time as being primitive, only capable of executing commands but not storing them. Many institutions did not see the worth of investing in this technology, which means that these pioneers did not get the relevant support they needed to facilitate the growth of the industry. However, they were able to overcome these challenges and facilitated its gradual growth.
It became evident that the growth of AI needed experts to share ideas to help in improving its capabilities. The Dartmouth Summer Research Project on Artificial Intelligence (DSRPAI) of 1956, which was hosted by Marvin Minsky and John McCarthy was the first of its kind and catalyzed its growth. It brought together experts in AI and financiers from all over the world. The technology started developing as machines could not only execute but also store data and remember actions taken.
The Fifth Generation Computer Project (FGCP), which started in 1980 and lasted till 1990, saw major improvements in the technology as computer programs became enhanced. Personal computers had also become common, significantly reducing the cost of having computers (Matt, Modrák, & Zsifkovits, 2020, p. 310). By the late 1990s, machine learning had started gaining massive popularity around the world. The success of Deep Blue and Alpha Go in the gaming field demonstrated that AI had achieved a level of intelligence that matched or even surpassed that of humans. During the same period, the concept of big data also started gaining rapid popularity. Figure 2.1 below summarizes the evolution path of artificial intelligence.
The growth of AI in the late 20th century and the 21st century can be summarized in three stages, as shown in figure 2.2 below. The first and critical stage was machine learning. Large technology companies such as Apple Inc., Microsoft, and Amazon.com created Siri, Cortana, and Alexa respectively. Small companies may not have such sophisticated tools but they have a role to play in the general development of the industry (Madanchian, Hussein, Noordin, & Taherdoost, 2015, p. 78). These were tools capable of communicating with humans and helping people to execute specific commands such as conducting online research and storing specific information, helping eliminate many challenges in the supply chain (Linh, Kumar, & Ruan, 2019, para. 117). These tools were also designed to become supper-collectors of data to help these firms to understand the emerging needs and expectations of customers. They were designed to learn based on information that they gathered independently.
The success of machine learning led to the second stage of machine intelligence. These machines were becoming increasingly independent as they could collect data and execute specific actions without any assistance from human beings. Earley (2014) explains that the current technology is at the level of machine intelligence (p. 59). Once the AI is trained on basic tasks, they can facilitate their further training as long as they can access accurate and reliable data. These machines are also capable of taking specific actions without any human input. The predictions and decisions that they make are sometimes superior to that of humans. This is so because they wholly rely on data instead of personal biases and cultural misconceptions, which enhances their competitiveness (Jovanovski, Seykova, Boshnyaku, & Fischer, 2019, p. 250). In the supply chain sector, this technology is proving to be crucial in supporting the decisions that a firm makes, based on data.
The next stage of AI, known as machine consciousness, is still futuristic. Currently, AI primarily bases its decisions on data, focusing on lowering costs while maximizing profitability (Devang, Chintan, Gunjan, & Krupa, 2019, p. 31). However, humanity goes beyond amassing wealth as these machines do. Compassion and the need to help those in distress make humans unique in their decision-making process. The fact that these machines lack consciousness may sometimes force managers to ignore the predictions, decisions, and simulations of AI. A firm may decide to invest in corporate social responsibility (CSR) to help the needy even if the plan developed by AI was different. The current progress in this field of technology is to facilitate the development of machine consciousness (Giudice, Scuotto, Garcia-Perez, & Petruzzelli, 2019, p. 310). AI should have human feelings and act in the interest of humanity.
Application of AI
AI creators started spreading the application of AI from the gaming industry, where it had achieved massive success, to the business sector. It was evident that AI could help firms in making accurate decisions and predictions when it is fed with the right data (Chung, 2021, p. 55). One of the areas that it gained massive popularity was in the manufacturing sector. As market competition became stiff, firms were under pressure to find production strategies that would lower the cost while enhancing quality and productivity as a way of improving profitability. AI made it possible to develop simulations of the best manufacturing strategies. Robots also started taking active roles in the manufacturing plants as a way of lowering costs, reducing risks to humans at these plants, and increasing standardization and productivity. The success of AI in the manufacturing sector made business executives find ways of applying it in other sectors.
Marketing became another major area where AI technology gained popularity. Firms needed to accurately predict market demands, changing tastes and preferences, and ways of meeting clients needs in the best way possible. AI became a critical tool that facilitated predictions and decision-making based on data. Small and medium-sized enterprises are under immense pressure to find ways of improving their efficiency (Alfoqahaa, 2018, p. 12). AI offers them a perfect way of overcoming numerous operational challenges. It can monitor the money markets and help in making accurate investment decisions. It also became an essential financial planning tool for small, medium, and large corporations. Intelligent robots are also currently used in supply chain management, which is the primary focus of this paper. Other areas where AI is actively used include green manufacturing and other environmental-friendly initiatives. Figure 2.3 below identifies areas where AI has become a crucial tool that facilitates effective operations.
Technologies, Security, People/Workers and Society
The rapid growth of AI and its current application in various fields has been facilitated by the desire to achieve efficiency and lower costs. In AI, volume, velocity, and variety have been the primary factors that have enhanced growth. Data has become a critical factor that defines the ability of a firm to achieve success. However, firms find themselves in situations where they have to collect large amounts of data to help in the decision-making process. Irrespective of a firms size, it is essential to collect relevant data that can help inform decisions and predictions that a company makes. AI makes it possible to collect large amounts of data, process it based on its relevance, and use it to conduct simulations and predictions (Aarstad & Saidl, 2019, p. 39). The capacity of the AI to handle large volumes of data surpasses that of humans in a significant way.
The speed or velocity with which data is processed is another important factor that has made AI popular in the business world. When collecting data, it is common for one to have access to large amounts of information. Humans are limited in terms of the amount of data that they can process within a specific period. The problem with such limitations is that sometimes the information that is ignored is the most crucial for the firms growth (Kumar, 2019, para. 6). AI makes it possible to process such large amounts of data within a short period. It can speedily select the most relevant data, based on the issue that it has to address and then process the data to help solve the problem. Once processed, AI also makes it easy to share information with all the relevant stakeholders in real-time.
AI and ML enable a firm to have a variety of options when deciding on various issues. For instance, when a firm is interested in selecting an appropriate supplier, various factors such as price, quality, and reliability have to be considered. It is common to find a case where the decision is solely based on price or quality instead of the other equally important variables. Stakeholders may not feel comfortable when they are not aware of the decision-making process (Adixon, 2019, para. 9). AI takes into consideration all the other important factors through simulation. It then provides various alternatives, in terms of priority, that the firm can consider. It evaluates both pros and cons in the simulation before classifying the possible actions that a firm can take to achieve the intended goal.
This technology is also meant to make the work of people, especially employees, easier than it was in the past. Some of the physically and mentally demanding tasks can now be done using machines. Robots have become effective in handling physically demanding tasks. On the other hand, sophisticated computers can now handle complex data processing and help in making accurate decisions. According to Amblee (2018), AI is emerging as a tool that will help protect modern society from natural forces such as global warming and climate change (para. 11). Fed with the right data, they can facilitate smart manufacturing and transportation in ways that have negligible impact on the environment. It can be possible to reverse some of the damages that have been made to the environment. Scientists believe that AI is set to make society a better place.
Artificial Intelligence in Supply Chain Management
Artificial intelligence has become a critical tool in supply chain management. According to Anyoha (2017), news about having driverless trucks was welcome news to many trucking companies in the United States (para. 7). Although the idea is yet to be actualized, its development is in advanced stages and there is a consensus that most cars on the road will not need drivers. For major trucking companies around the world, such a technology will have an immense impact in cutting the cost of hiring numerous drivers, paying their medical allowance, and having to deal with workers unions. AI goes beyond the promised creation of driverless trucks (Kersten, Blecker, & Ringle, 2019, p. 18). Numerous benefits are already available for firms that have chosen to use the new technology. It is necessary to discuss how AI and ML have gained relevance in SCM and how they are applied to help cut operational costs, improve efficiency, and enhance profitability.
Data and Optimization across the Value Chain
The concept of data optimization has gained massive relevance in the recent past as firms embrace big data. Data optimization refers to a process that uses sophisticated data management tools to have access to, organize, and process data from various sources at high speeds and in a comprehensive manner (Wright & Recht, p. 45). The processed data is then used to make critical decisions within an organization. The primary goal of data optimization is to facilitate a performance that is capable of meeting customers expectations (Dash, McMurtrey, Rebman, & Kar, 2019, p. 43). It makes it possible to provide real-time service to clients by understanding their emerging needs.
In supply chain management, data optimization has become essential in ensuring that smart manufacturing is achieved irrespective of the size of a firm. Didonet and Díaz (2012, p. 101) observe that successful companies have come to appreciate the significance of big data and its application in making predictions and decisions. Smart manufacturing requires effective communication and coordination in the entire supply chain, from the stage of raw material acquisition to the stage when the product is delivered to clients. As shown in figure 3.1 below, there are five stages that are involved in smart manufacturing when AI and ML are applied. The initial stage involves collection of raw data. At this stage, the focus is to gather relevant information about different suppliers to understand those that offer superior value.
The second stage is the visualization and integration of data. A firm starts to develop a database that can help it in making critical decisions. The third stage of material-centric insight involves the systematic use of the collected data to assess the suppliers worth to the firm (Belhadi, Mani, Kamble, Khan, & Verma, 2021, p. 4). Continued reliance on data facilitates transformative analytics and continuous improvement. At this stage, a firm will be moving from traditional approaches of manufacturing to one that integrates the use of machines in making predictions and decisions. Once the data is available, a firm can trust computers to process it and use it to accurately address specific challenges that affect the operations of a company. When these four stages are completed successfully, the company can move to the final stage of having fully automated smart manufacturing. At this stage, computers will be responsible for providing simulations about the best ways of operation that can yield the best output for the company.
AI and ML have made it possible for firms to achieve fully automated smart manufacturing. Traditionally, firms had to make manufacturing decisions based on human predictions (Toorajipour, Sohrabpour, Nazarpour, Oghazi, Fischl, 2021, p. 506). The problem with such predictions was that in most of the cases it was based on outdated data and personal biases that led to consistent inaccuracies. It was always impossible to produce the exact number of products needed by clients. The products were either in excess or less than what the market needed. Smart manufacturing helps in eliminating such challenges. Real-time data makes it possible for a firm to understand the current demand in the market.
The information will then be used to facilitate the delivery of the exact amount of raw materials needed, which will then be fully utilized to develop products. The produce will then be delivered to the market within the right time. Smart manufacturing helps in eliminating wastes caused by overproduction and delays that result from underproduction. It also helps in ensuring that the right quality of products is made available to customers at the right time. AI and ML make it possible for smart manufacturing to be realized in an organization.
ML Techniques Frequently Used in Logistics in MSMEs
Machine learning has achieved popularity in various business settings because of its unique capabilities. For small and medium enterprises, this technology offers a wide range of services that can be used to enhance activities in the logistics sector. Figure 3.2 below identifies specific ML techniques that can be used in logistics. Face recognition has become a powerful tool that firms use to facilitate the identification of persons. In the logistics sector, one of the challenges that SMEs face is cases where unauthorized individuals have access to goods on transit for the primary purpose of stealing (Chin, Hamid, Rasli, & Baharun, 2012, p. 615). ML has introduced a new technology that is meant to enhance the security of products while they are in transit. Face recognition is a technology that requires drivers and authorized individuals to show their faces before special in-built cameras on the trucks before one can access the cargo section. It means that unauthorized individuals cannot have access to the cargo section of these trucks. When one tries to make a forced entry without using face-recognition technology, information is sent directly to the security agencies.
The technology can also be used to ensure that drivers do not allow unauthorized passengers into the cabin because they can also be a security threat to the employee and goods on. The tool is also widely used in enhancing security at warehouses transit (Singh, Kumar, & Shankar, 2012, p. 174). Only those whose faces the machine can recognize are allowed into the warehouse. The technology not only helps in eliminating the possibility of unauthorized persons having access to goods and raw materials but also makes it possible to identify employees who are stealing from the firm. The technology records time an employee entered the warehouse or the cargo section of the truck, the time they took before leaving, and what they carried when leaving. The chances of stealing from a firm are significantly reduced in such instances, which means that the cost of operation will be dropped significantly.
Object detection is another ML technique that is gaining relevance in logistics. Using special sensors, the machine can detect when an object is approaching and take necessary action. The technology is currently used to automate various activities in warehouses (Lawson, 2021, para. 7). A common application is controlling the security lights. Instead of security lights being on at all the sections of the warehouse, the sensor can be trained to detect when people are in the store and it will automatically switch on the lights. When people are not within the warehouse, the sensor automatically switches off the lights. The technology helps in saving the cost of energy. Object detectors can also be used to enhance security.
When an object, which in most cases is a human, is detected moving into a warehouse, information will be relayed to the security unit immediately. If the object is considered suspicious, immediate action can be taken to inspect it and determine if indeed it is a security threat. Smart driving technology is making it possible for trucks to identify objects on their paths. The technology, although not fully developed, makes it possible for a vehicle to make emergency breaks when it is about to hit an object on its path. Such technologies will significantly increase safer working conditions (Jacobs, 2020, para. 13). It will significantly reduce losses of lives, trucks, and goods on transit caused by these accidents. The insurance premium that MSMEs have to pay on such smart trucks and goods on transit will be significantly low. Object detection technology is directly related to motion detection, both of which work together to enhance the capability of ML.
Advanced ML technology is currently capable of emotion recognition. When a driver, a captain, a pilot, or warehouse employees are in their place of work, AI is capable of detecting their emotions. The ML can recognize when one laughs a lot or specific words that they use in their conversation to express their emotions. Advanced AI has become critical in making important supply chain management decisions (Stinson, 2021, para. 3). They can tell when one is distressed or frustrated while they are on their official duties. Mental stress compromises the ability of one to reason.
Emotion recognition technology is meant to eliminate such cases by identifying employees who are emotionally distressed while at work. When they are captains, drivers, or pilots, measures can be taken to ensure that they are not allowed to be fully in control of the transport vessel. If possible, such an individual should be temporarily relieved of their duties to get the relevant assistance before they can resume work. When it is not possible or the threat is assessed to be negligible, they can be allowed to work alongside a colleague who can help them navigate the vessel and to talk about the issue of concern.
ML has made it possible to convert text to speech in a conversation. Texting while driving is a major cause of road accident in the United States. It is significantly more dangerous than making a call because one has to take their eyes off the road to write a text (Pournader, Ghaderi, Hassanzadegan, & Fahimnia, 2021, p. 2). Such an action makes it easy for a driver to lose focus on the road. ML has introduced a technology where a text can be converted easily into a sound and vice versa. It means that a driver can communicate with others easily without having to constantly take their eyes off the road. Their speech can then be converted to a text in case the person on the other end of the phone prefers chatting. Another benefit of this technology is that a driver, a captain, or a pilot can make their reports easily without having to type them. At every stage, they can verbally report what has taken place at a specific time. Their verbal report is then transformed into texts, which can then be documented for further references. It eliminates the need to develop a written report, which saves them time.
Automatic speech recognition is another aspect of ML that is proving to be crucial in the logistics sector. Like face recognition technology, speech recognition is also gaining relevance as a tool that can be used to identify employees within a firm (Hellingrath & Lechtenberg, 2019, p. 67). The AI has developed a unique capacity that enables it to identify a unique sound of an individual, just like a fingerprint. Unlike a signature that can be forged, one cannot defeat a well-trained machine by faking another persons voice. It is also not possible to steal the voice as one would steal a password.
One would need to say their name or utter a specific statement when they want to have access to the warehouse or a transport vessel. The voice recognition tool will then determine if they are authorized to have access to the store or the vessel. If they have the permission, the tool will state their identity, time of entry, and activities conducted. If they lack the authority to do so, the machine will make a report of an attempted breach of security (Isensee, Teuteberg, Griese, & Topi, 2020, p. 2). Relevant authorities can then investigate the issue immediately. This technology is meant to eliminate or significantly reduce cases of theft of goods on transit or those that are already in the warehouse.
Artificial Intelligence Opportunities and Benefits for MSMEs in the Logistics Sector
The concepts of AI and ML are rapidly gaining relevance and acceptance in the field of business. According to Belhadi et al. (2021, p. 10), it has become evident that AI has a unique capacity that is beyond that of humans when it comes to making accurate data-based predictions. The machine can process Big Data within a relatively short period, and then use it to address a specific problem that is affecting a given business entity. Kersten et al. (2019) state that AI has immense opportunities and benefits for micro, small, and medium business entities (p. 49). They only need to understand how to correctly apply it in specific operations of their businesses. It is necessary to discuss the specific opportunities and benefits that AI presents to these firms.
AI Opportunities for MSMEs in the Logistics Sector in Logistics Sector
MSMEs face some unique challenges, which make it necessary for them to find ways of competing favorably against some of the larger competitors. For a firm to succeed in the integration of AI into its operations, it should understand the opportunities that the new technology presents and how it can fully take advantage of them. The following are some of the specific opportunities that using this technology presents to a firm.
New strategies of reaching out to new suppliers. One of the unique opportunities that AI presents to MSMEs in the SCM is the ability to identify, select, and reach out to new suppliers. According to Singh et al. (2012), one of the most important factors that a firm has to consider is effective ways of identifying suppliers and communicating with them effectively (p. 171). Changes in the market make it necessary for a firm to regularly evaluate suppliers that can offer the best deals in the market. Using AI, a small or medium company can process large volumes of data within a short period and select suppliers than offering the best deals in the market. It can easily conduct a comparative analysis to determine how it can get the best value for every purchase that it makes.
Improved processing of clients orders. According to Klumpp and Ruiner (2018), large organizations handle numerous transactions every one hour as they struggles to serve its customers in the most efficient way possible (p. 7). Such a large volume of a transaction within that short period cannot be possible if technology is not applied. Small and medium enterprises must understand the significance of AI in making such large volumes of transactions possible. Using AI, the logistics department can easily interact with the sales department, process clients orders, and facilitate their delivery within a short time.
The technology creates
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