Integration of robotics and automation in supply chain: a comprehensive review

1. Introduction

An essential part of the global economy, the supply chain sector is in charge of shipping, distributing, and delivering commodities to customers all over the world. Increasing demand, growing expenses, and the need for increased efficiency and speed are just a few of the issues the sector must overcome. By enhancing the precision, speed, and dependability of supply chain processes, robotics, and automation technologies have the potential to address many of these issues.. New and creative business models are developed as e-business expands. According to Da Silveira and Cagliano, modern supply chain specialists deal with "Omni channel retailing," "complex global supply chains," and "consignment inventory." As a result, e-commerce enterprises are investing heavily in the adoption of supply chain technology based on robotics and automation. The autonomous robot is capable of perception, decision-making, and action in response to changes in the surrounding environment. Mechatronics and automation is a versatile innovation that can be quickly used to computerize use cases across various industry/SCM segments because it is adaptable, painless, and versatile [1].

Obtaining, acquiring, changing, assembling, and operation-related movements of all kinds are planned out and made to happen in SCM. For this engagement, channel partners including providers, go-betweens, suppliers for outside operations, and clients must coordinate and work together. A strategy for dealing with the board that integrates market interest both inside and outside of organizations is to inventory network the executives. When SCM manages various tasks, it benefits the provider, retailer, and client. Organizations for invention, obtaining, item improvement, and calculated arrangements might use this dynamic cycle to effectively prepare workouts. The cycle can also be used to improve a variety of options. The most typical approach of thinking about all points of view when exploring options at various levels leads to beneficial arrangements.

SCM is a crucial component of modern corporate operations. According to research, the most significant and influential companies or sectors have claimed that SCM is quite probably the most competitive industry, underscoring the connection with other sectors, advertising, creation, and coordinated operations [2]. Purchasing raw materials from the manufacturer is the second phase in SCM after sourcing the raw materials, which is the first step. The manufacturing unit purchases the raw materials from the supplier and transforms them into the finished product in accordance with the desired product type. Then, based on demand, the wholesaler distributes these goods from the stock to the retailer, who then sells it to the client. Every participant in the supply chain network, from suppliers to retailers, must communicate with one another effectively. A strong supply chain network should be present in the flow that is shown in the Fig. 1 because it is essential for businesses and industries.

Fig. 1Supply chain management

2. Evolution of supply chain management

This advancement is portrayed by the joining of discrete undertakings which were expanding from 1960, in the 1960s the framework was profoundly divided so it was a basic region for efficiency upgrades. Operations assignments even though have stayed comparative, they first solidified into two particular capabilities for Example materials on the board and actual circulation during the years the 1970s and 1980s. In the year1990s the interaction moved further as globalization induced a useful mix and rise of strategies in an obvious sense [3]. Every part of the creation network ended up being fundamental from a lone organization perspective. The total incorporation became conceivable with the assistance of data and correspondence advancements. This aids in the coordinated administration and data control, finance, and merchandise stream, and another scope of creation and dissemination framework was conceivable. Complex grouping of exercises building esteem catch and intensity made inventory network the board complex. The developing degree of mechanization as of late in-store network has been a prevailing component of the development of the two materials the executives and circulation frameworks. This digitalization is remarkable, especially inside the circulation habitats which have encountered a noteworthy street towards the away, material dealing with and bundling. Enhancements in data and Correspondence innovations have made it conceivable that two closures of the sequential construction system became incorporated into strategies and production networks. The improvements of high rack amassing ended up not being set in stone, or the inside advancement of packs by AGVs robots were early verbalizations of vital planning. Earlier tasks were a development isolated around the giving, warehousing, creation, and movement capacities, an enormous piece of them being truly free [3]. With the new affiliation and the board principles, firms followed a more planned approach, as such noting the impending interest in versatility without raising costs. As creation ended up being logically isolated, practices associated with its organization were combined. Spatial irregularity transformed into a symptom of economies of scale in movement. Fig. 2 tells about the how supply chain evolved from 1960-2010.

Fig. 2The Evolution of supply chain management

3. Use of robotics and automation in the supply chain in recent times

Fig. 3Recent use of robotics and automation in SCM from (2016-2020) [4]

Robots and atomization exercises utilized in the acquirement and operations chain over the most recent four years have had an expanded speed as far as their utilization in the production network of the executives. 2017 had a development pace of the utilization of this innovation by 5.71 % contrasted with 2016. In 2018 the utilization of this innovation diminished by 8.11 % contrasted with 2017. In 2019 contrasted with the earlier year (2018) in which it diminished by 5.88 %. In 2020, contrasted with 2019 the ease of use pace of this innovation expanded by a level of 21.88 %. Looking at a long time from 2016 to 2020 the convenience pace of mechanization and mechanical technology innovation is expanding with a level of 11.43 % [4]. The graph below tells about the recent use of robotics and automation in supply chain management.

4. Robotics and automation in the supply chain

That advanced as we undoubtedly already know, robots and automation are used in each phase of the production network’s flow, which runs from supplier to client, to ensure that everything moves smoothly and quickly. In the SCM industry, automation mostly congregates to estimate, request arrange, and advance. These areas increase customer satisfaction and enhance assessments of cycles and resources. Since they may function in many SCM environments, expert-based structures can be thought of as the most likely areas of automation in SCM. However, for the most part, automotive use affects practical levels like checking, creation, and stockroom exercises. Automobile use can also have a negative impact on strategical, vital, and utilitarian powerful levels. Companies invest significant sums of money in research and development when using current computer-based intelligence technology and determining the best programmed solutions for commercial activities due to increasing intensity, request vulnerability, and stockpile risk. Additionally, it improves quality and the customer experience. Organizations can reduce waste and become more supportable by using exact gauges. They can also increase purchasing and lower expenditures. Automated robots equipped with cameras can recognize objects and materials and expedite picking. Information that is essential to all gatherings can be updated gradually, doing away with the need for challenging, mistakeprone compromise processes. It can also be updated further to handle other tasks to be accomplished and changed correctly to make it on time. . In the past, there was only a sequential computerized construction system. However, there are currently a few important viewpoints in the production network, a variety of emotionally supportive networks, and specialized equipment that are available for automating the procedures of marketing and dealing, as well as the relationships with the providers. The main goal of these numerous initiatives is to computerise every step of the production network, from the acquisition of raw materials to delivery to the final consumer [5]. This will enable us to serve our customers more effectively, efficiently, and quickly. As a result, automation initiatives that first started on the factory floor have now reached the whole network of stores.

The stockpiles was helped by developing innovations to anchor in each area and control all of the activities. These developments support mechanized binding supply.

4.1. Automation and robotics in the management of warehouses

By ensuring that goods are stored, picked, and delivered in a timely and effective manner, warehouse management plays a crucial function in the supply chain. However, warehouse management, which includes many responsibilities including inventory control, material handling, and order fulfilment, maybe a challenging and labour-intensive procedure. Numerous difficulties in warehouse management might affect the facility's overall effectiveness and efficiency [6]. Among the principal difficulties are:

– Picking and filling orders ineffectively and slowly.

– Poor inventory control and management.

– Demand planning and forecasts that are inaccurate.

– Improper and dangerous material and product handling.

– Inadequate quality assurance and testing.

Fig. 4Market size for warehouse automation worldwide from 2012 to 2026 [6]

4.2. Robotics and automation applications in warehouse management

By automating repetitive and time-consuming processes, increasing accuracy and safety, and enabling real-time visibility and control across the warehouse, robotics and automation have the ability to alleviate many of these issues. The following are some of the major uses of robots and automation in warehouse management:

– Automated transportation and material handling.

– Mobile autonomous robots for order fulfilment and picking.

– Automated inventory control and management.

– Automated inspection and quality control.

– Virtual and augmented reality for training and simulation.

– Automated Storage and Retrieval Systems (AS/RS).

4.2.1. Automated storage and retrieval systems (AS/RS)

Automated AS/RS systems are used to store and retrieve goods from warehouses. It is a collection of computer-controlled systems that help computerise stock management and store/recover goods upon request. The help in creating these frameworks makes it easier to work with quick item recovery and placement [7]. They operate as cranes, move along set rails, can surely traverse across item pathways and vertical levels, remove, and store objects. It is used to speed up order fulfillment and project management of supplies in a stockroom environment. The picture figure illustrates the AS/RS system.

Fig. 5Automated storage capacity and recovery frameworks (AS/RS) [8]

4.2.2. Automated guided vehicles (AGVs)

AGVs are autonomous transportation systems that move goods inside of warehouses and manufacturing facilities. Typically, they are used to transport goods from one place to another, like from a storage area to a loading dock. AGVs replace physically demanding forklifts and pick trucks and help with material, supply, and stock movement. Some AGVs have independent paths that are marked out and separated from one another by wires, pretty strips, sensors, and tracks. In order to avoid impediments, another method makes use of a camera, LIDAR infrared technology, as well as other fundamental sensor technology, navigation technology, and system integration technology. AGVs in a warehouse are shown in the figure below.

Fig. 6Automated guided vehicles (AGVs) [9]

4.2.3. Collaborative robots (Cobots)

These versatile, semi-independent robots were created to aid human employees in carrying out various tasks in a warehouse environment. A portion of the cobots patrol the stockroom floor in search of human pickers and serve as adaptable storage containers for orders that have been picked [9]. These cobots control work operations by driving partners, shifting loads, and using the distribution centre. A perfect path through the office is ensured by the sensors’ coordination, which allows them to recognize obstacles like boxes and snags. By organizing or picking at stations, cobots can deliver picked requests to workers in various locations across the stockroom, speeding up the process of satisfying customer demands. Cobots are seen in the figure below.

Fig. 7Collaborative robots (cobots) [10]

4.2.4. Control and supervision

Prior to the PC era, basic regulators were used to completing the continuing control of specific pieces of equipment, such as the machine, processing machine, and robot. At this time in the PC era, programmable regulators have taken the place of basic control frameworks. Plant automation networks have emerged with the emergence of the neighbourhood (LANs); we now have cell regulators managing various machines and production line regulators regulating a few of these cells. By using assembling regulators and stockroom-the-board systems, it is possible to process the natural material into a phone and have the finished product stored in an automated stockpiling and recovery system (ASRS). The development of administrative control frameworks and continual control frameworks has been aided by programmed control. With so many projects underway, mechanization and improving the productivity of working units inside the confines of a manufacturing facility have taken centre stage. After finishing their specific tasks, such as manufacturing robots, all robots that are integrated with one another work on information transfer. The robot that transported the material also works side by side with the subsequent robot to continue supplying the material for work. The illustration below demonstrates how several technologies are utilized for control and supervision. This plant has been automated for control and supervision, as seen in Fig. 8.

Fig. 8Control and supervision [11]

4.2.5. Goods-to-Person technology (G2P)

The modern high-volume operation-based e-commerce model, where online purchases are made up of specific products, is ideally suited for this technology. As a result, G2P is particularly helpful for fulfilling split-case orders, which require that individual units be picked up from separate locations and put into a shipping tote or carton. Traditional picking involved people going to a good model where each picker goes to a certain product to pick it. This caused labour-intensive time wastage that Seduced the quantity of orders completed. In the G2P approach, as opposed to the P2G model, the good is brought to the person rather than the other way around. The individually stored objects are mechanically taken out of storage and delivered to a picker or pick station. The emphasis at pick stations and packing stations is on ergonomics and high productivity because the picker does not have to walk. Model increases the number of orders completed per person and reduces the amount of time wasted between picks (Kim, 2015). There are many various types of G2P technology, but they are all founded on the same fundamentals: SKUs are brought by an automated system to a fixed pick station, not the other way around. There are many G2P systems that are incredibly effective currently on the market. Numerous component-based technologies, such as pallet based or tote-/carton-based systems, high-density storage systems, robots, horizontal and vertical carousels, and vertical lift modules, can be incorporated into these G2P solutions. Additionally, these systems are adaptable enough to be scaled for changes in increased or decreased product production.

5. Artificial intelligence (AI) and machine learning (ML) in supply chain

Artificial intelligence (AI) and machine learning (ML) have become effective technologies for enhancing supply chain management. The way businesses organize, carry out, and improve their supply chain activities has been revolutionized by the integration of various technologies. Accurate demand forecasting is one of the most important advantages of ML and AI in supply chain management. ML algorithms can reliably estimate demand by examining historical sales data and other factors, allowing businesses to optimize their inventory levels and decrease stock outs. As a result, clients are more satisfied as they receive their products fully and on time. By analysing factors like weather, traffic, and road conditions, ML and AI can also aid in the optimization of transportation routes [12].

5.2. Forecasting demand is made easier by predictive skills

Demand forecasting is a branch of predictive analysis where businesses predict the demand for goods and shipments across the supply chain, even in the face of unforeseen circumstances. The multitude of parameters that affect demand in SC makes conventional methodologies, such as spreadsheet models, statistical models, moving averages, and exponential smoothing, which were previously addressed, limiting and overly simplistic, and erroneous. In this way, projections might only give a limited picture of how supply networks would experience variations in demand. Additionally, the non-linear nature of the unexplained demand variations could be attributed to their being nothing more than statistical noise. As a result, traditional or basic models are unable to represent crucial and non-linear aspects. Fortunately, machine learning offers algorithms that can map significant non-linear features and convert them into variables that can aid in understanding the past, accurately forecasting future events, helping them to enhance decision-making processes regarding cash flow, risk assessment, capacity planning, and workforce planning, as well as satisfy customer demands.AI Tools used in demand forecasting is shown in the Fig. 9.

Fig. 9AI Tools in demand forecasting

5.6. Classification of images

The classification of a picture is accomplished by image recognition. It is quite useful in the supply chain since it can quickly classify various products and divide them properly. Product and component faults, abnormalities, and quality problems can be found using fAI image classification. Photos of the objects on the assembly line can be taken using cameras and sensors, and AI algorithms can classify these photos to find any deviations from the acceptable standards. This helps to minimise the likelihood that customers will receive subpar goods by ensuring that only high-quality products move forward in the supply chain. Technology that uses visual recognition can help with efficient inventory management. Artificial intelligence (AI) can automatically ascertain stock levels, identify low-stock conditions, and even forecast when things need to be reordered by analysing photographs of products on shelves, in warehouses, or on pallets. This improves inventory management and lowers stock outs.

5.6.1. Detecting objects

You can instantly recognize various items with the aid of object detection. In supply chain management, numerous items are encountered simultaneously. These items require a lot of effort and money to separate manually. Without the involvement of a person, object detection can help you swiftly identify and classify things, improving the accuracy of image classification. Several warehouse operations, including truck loading and unloading, item sorting, and shelf placement, can be automated using object detection. Robots with cameras and object detection software can quickly recognise and handle objects, eliminating the need for physical labour and speeding up operations. Real-time tracking of the movement of commodities is aided by object detection. To identify goods, count numbers, and keep tabs on changes in inventory levels, cameras and sensors can keep an eye on shelves and storage locations. This precise and automated method optimises inventory management by minimising errors and stock outs. Systems for detecting objects can help with picking and packing procedures. They direct workers to the appropriate products, ensuring that the appropriate goods are chosen for each order. Because of the increased order accuracy and quicker fulfilment, customers are more satisfied. The supply chain can use object detection by deploying cameras, sensors, and AI algorithms that can precisely identify and track items. To fully reap the rewards of object detection technology, integration with current systems and procedures is essential. Supply chain management has the potential to become even more effective, accurate, and agile as technology develops.

5.6.2. Segmentation of images

Another approach that uses CNN to generate a pixel-wise mask around the item itself and determine its dimensions is image segmentation. The below figure shows the example of image classification object detection and image segmentation.

Fig. 10Image classification, object detection, and image segmentation [14]

Segmentation makes it possible to thoroughly evaluate products for flaws and anomalies. The ability to analyse each segment independently enables a more thorough evaluation of product quality. This helps find flaws and guarantee that only top-notch goods move forward in the supply chain. Monitoring product placement and stock levels is made easier by segmenting photos of store shelves. Retailers may determine which products are in short supply, which require restocking, and how various products are performing in terms of positioning and visibility by analysing shelf segments. Barcodes, QR codes, and other labels on goods or packages can be extracted and recognised with the use of segmentation. To accurately track and manage goods as they move through the supply chain, this is essential.

6. Machine learning algorithms

Due to their capacity to analyse vast volumes of data and optimize the supply chain process, machine learning (ML) algorithms have gained popularity in recent years [16]. A thorough analysis of the ML algorithms used in SCM and their applications is the goal of this study. Fig. 11 shows the different types of Machine Learning Algorithms.

SCM algorithms that leverage ML.

Fig. 11Machine learning algorithms

7. Examples of how AI and ML have been used in supply chain management

7.3. With AI, Rolls Royce reimagines freight transportation safety protocols

The venerable British automaker Rolls Royce collaborated with Intel to develop a clever AI system that may speed up and improve commercial shipping. They assert that this technology will be able to independently control communications, navigation, and obstacle detection, creating a new fleet of autonomous ships. This partnership can assist us in assisting ship owners with the automation of their navigation and operations, minimizing the possibility of human mistakes and allowing personnel to concentrate on more beneficial activities [25].

Fig. 12Use of AI globally in manufacturing and supply chains [26]

The supply chain AI adoption by worldwide businesses and industries is shown in the above graph. Grey indicates an infinite adoption of AI in businesses, orange indicates a plotting use case for AI, yellow indicates a widespread adoption of AI and ML, and blue with 11 % indicates that businesses view AI and ML are essential.

8. Result and discussion

The supply chain's efficiency, productivity, and cost savings have significantly increased with the usage of robotics and automation, AI, and ML. Worldwide e-commerce and online sales are increasing, and with that growth comes a need for products to flow through the supply chain more quickly. Customers’ rising expectations for quicker order fulfilment, fierce rivalry, and the emergence of newer business models have driven businesses, particularly ecommerce firms, to seek out cutting-edge technological solutions in order to serve their clientele. This causes a new generation of technological solutions to emerge, including mobile, intelligent, autonomous, and smart robots that can be valuable in supply chains and warehouses in particular. These solutions are enabling these businesses to quickly grow and meet the rising demand while ensuring lower operating costs, maintaining their competitiveness, and managing the labour shortages and efficiency further. Businesses can improve throughput, lower errors, and cut labour costs by automating repetitive and manual processes. Furthermore, the application of AI and ML has allowed businesses to forecast demand trends, optimize inventory levels, and identify potential supply chain disruptions in real-time.

Concerns over the displacement of human labour and potential job losses have also been highlighted by the use of robotics, automation, AI, and ML in the supply chain. However, proponents contend that these technologies can actually generate new employment prospects in fields like robot programming and maintenance. By aiding with physically taxing or dangerous jobs, collaborative robots, also known as cobots, can increase worker safety and morale generally. The up-front expenditures related to installing these technologies are another issue. However, many businesses find the investment to be justified due to the possible long-term cost savings and the boosted effectiveness and productivity. As the world becomes more interconnected and as the times change, there is more fierce competition and rivalry among organisations. Businesses are competing for growth and revenue generation while technology advances at an exponential rate. As we can observe, robotics are now being used in a variety of industries to complete challenging jobs. Automation is currently being used by businesses in every industry to combine human and machine labour.

However, AI will continue to advance and become more sophisticated than it already is. This development will intensify human and AI collaboration and possibly lead to ground-breaking innovations not only in the supply chain but also in other crucial industries. Furthermore, extensive data processing and analysis are needed in order to integrate AI and ML in supply chain management. Companies must therefore make sure they have the tools and knowledge needed to manage data properly. To ensure the successful application of these technologies, it could be necessary to spend more money on training or hiring data scientists. The application of robots, automation, AI, and ML in the supply chain is, all things considered, a positive development with great room for growth in the future. The supply chain industry is anticipated to improve in efficiency, cost-effectiveness, and safety over the next few years as businesses continue to invest in these technologies.

9. Conclusions

The supply chain has seen a considerable increase in efficiency, productivity, and cost savings through the use of robotics and automation, AI, and ML. The need for logistics robots is skyrocketing. According to a recent analysis by Tractica Research, warehousing and logistics robot sales will reach $22.4 billion globally by the end of 2021, with annual robot unit shipments expected to reach 620,000 units. More than 50 established and up-and-coming businesses are vying for clients in this market. The global warehouse robotics industry is anticipated to produce $10.34 billion in sales.

This demonstrates that robotics-based modern logistics will shape supply chains around the world in the coming years. The organisations may handle product movement considerably more effectively and efficiently while removing the chance of human error by automating the fundamental tasks of product movements in an assembly line, a manufacturing facility, or a warehouse. Mobile robotics solutions lessen the likelihood that a product would be erroneously received, stored, transferred, picked, packed, or shipped to the client. These robots reduce stress and make life and business operations safe.

At the end of the day, adding automation to the warehouses doesn't necessarily require a one-time upfront investment. It may be possible to upgrade the current warehouse system gradually with automation (Star ship, 2018) [27]. E-retailers can have a complete warehouse system that is fit for their business and can handle even complicated picking issues with the well-planned integration of the necessary equipment. Throughput has increased, mistakes have decreased, and labour expenses have been kept to a minimum as a result of automating repetitive and manual processes. Companies can now predict demand trends, optimize inventory levels, and spot possible supply chain problems in real-time thanks to the application of AI and ML.

Proponents contend that these technologies can generate new job opportunities and that long-term cost savings and improved efficiency make the investment worthwhile, despite concerns regarding the displacement of human labor and the upfront costs related to implementing them. The supply chain business is anticipated to get more productive, economical, and secure as technology advances. The supply chain's use of robotics, automation, AI, and ML has a bright future, with room for more technological improvements. In order to enhance supply chain operations and maintain market competitiveness, businesses need therefore continue to invest in these technologies.

Although there are many advantages to using robotics and automation, AI, and ML in the supply chain sector, there are also difficulties in putting them into practice. The expense of implementing these technologies, which can be substantial, is one of the biggest obstacles. The requirement for data infrastructure and knowledge to manage the huge amounts of data created by these technologies presents another difficulty. Concerns regarding job loss and the ethical ramifications of AI and ML have also been expressed.

10. Future technologies that can use in supply chain management

Supply chain management can benefit from the usage of block chain technology to improve transparency and traceability. Businesses may track the movement of items and make sure they are being moved and stored in line with industry standards and best practices by using a distributed ledger. Block chain can be used to monitor product authenticity and stop fraud.

The location and condition of commodities can be tracked throughout the supply chain using IoT devices like sensors and RFID tags. Companies may enhance inventory management, optimize logistics processes, and make sure that goods are being transported and stored in the proper conditions by gathering and analysing data from these devices. Workers in the warehouse or on the production floor can receive real-time information thanks to augmented reality technology. Workers can obtain instructions and suggestions on how to complete tasks by wearing AR headsets, decreasing errors and increasing efficiency.

Overall, a wide range of technologies is available for use in the supply chain sector, depending on the particular requirements and objectives of each company. Companies may enhance their supply chain operations, lower costs, and gain a competitive edge in the market by combining the proper technologies. In the field of SCM, AI's future looks bright. We recommend the following line of AI research topic areas that can improve SCM decision-making processes based on anticipated advances in AI research.

A new set of strategic SC issues, including SC integration and SC risk (disaster) management, can be addressed by multiple agent-based systems that are better able to manage complexity.

Reverse auctioning with SC partners and real-time pricing can both be done using intelligent agents.

Agent-based systems can include game theory to comprehend SC dynamics and create tactical SC alliances.

Using knowledge discovery approaches, profiles of ideal SC partners can be created, including suppliers and 3PL providers.

Rule-based expert systems can be created to aid with contract manufacturing or logistics outsourcing decisions.

It is possible to create expert systems that enhance airline revenue management.

Hybrid meta-heuristics can be created to handle combinatorial transportation network design challenges by combining the AI capabilities of GAs with those of ant colony optimisation.

The GA or ANN methodologies can be combined with the fuzzy logic approach to reduce overall logistical expenses.

The SC knowledge management framework can incorporate a rule-based expert system.

To handle the evaluation and selection concerns of overseas suppliers or 3PLs, rough set theory and/or machine learning can be further investigated.

Without impairing information flows between SC partners, AI may be integrated with the legacy systems already in place at multiple SC partners.