Abstract
Scientific research on emerging technologies underscored the advantages of their implementation within production systems, with a particular focus on artificial intelligence (AI). In particular, the integration of AI with other cutting-edge technologies is a relevant topic which can potentially lead to huge impacts in terms of business performance. Yet, literature on the subject, although rich, is still fragmented, limited to specific cases and applications, but lacking in a comprehensive classification framework. Therefore, using a systematic literature review, this study provides an overview of how the combination of AI and other cutting-edge technologies could potentially improve market and organisational performance in business functions and processes. By classifying the literature of case studies and real-world applications into specific taxonomies, the research considers an indicator, the co-occurrence ratio, highlighting the most significant and emerging combinations between AI and other cutting-edge technologies, also specifying the contexts in which they are used. The study shows that AI is strongly interconnected with other cutting-edge technologies, suggesting a research agenda in which the integration of AI with other emerging technologies is promising within specific production systems contexts, providing benefits and opportunities for companies.
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Introduction
Since the introduction of the Industry 4.0 paradigm, the scientific literature analysing AI has grown exponentially. Scholars ventured into several systematic literature reviews (SLRs) to outline, synthesize and have a global picture of the state of the art of AI in the business field, as well as to show challenges, opportunities, limitations and directions for the future (Dwivedi et al., 2021; Nti et al., 2022). In particular, several SRLs focused on the role of AI for supply chain management (SCM) to evaluate the effectiveness of this technology in improving the supply chain (SC) performance and dealing with unexpected events that could have a negative impact in any area of the SC (Koh et al., 2019; Pimenov et al., 2023; Rodríguez-Espíndola et al., 2020). Other researchers carried out bibliometric analysis on a huge number of scientific articles to evaluate the role of AI in manufacturing (Yuan et al., 2022; Zeba et al., 2021), confirming that the importance of AI within the modern business landscape is widely recognized.
Within the Industry 4.0 concept, AI is only one of the crucial technologies that is revolutionizing modern manufacturing and SC systems. Indeed, other cutting-edge technologies have a relevant role in implementing Industry 4.0, such as big data, Internet of Things (IoT), autonomous (driverless) vehicles (ADV), virtual reality, 3D printing and blockchain. Several bibliometric studies and literature reviews have investigated the impact of these technologies on various areas of production systems, operations management and supply networks (Rosati et al., 2023; Tliba et al., 2023). SLRs are mainly focused on only one technology at a time, with few cases of scientific papers reviewing more than one technology with the aim of comparing their different impact on production systems, SCs and business in general processes (Yingli Wang et al., 2021a, 2021b). Thus, SLRs on the value of each “standalone” technology have already depicted many aspects, from implementation issues to the expected advantages in terms of business performance. However, the Industry 4.0 paradigm underlines that manufacturing and SC activities must be viewed as a unique integrated system, where technologies jointly contribute to the achievement of business goals (Koh et al., 2019). In consideration of the predominant role of AI among new technologies, an interesting field of research is the integration between AI and other cutting-edge technologies. Indeed, literature is rich of papers presenting individual examples of the integration between cutting-edge technologies, in the form of simulation or case study. These works confirm that such an integration allows companies to discover new applications and possibilities to transform operations management paradigms and to reshape SC relationships (Chung, 2021; Ciano et al., 2021; Hopkins, 2021; Ivanov et al., 2021). However, a SLR on the integration between AI and cutting-edge technologies is still lacking. Therefore, a general overview of the opportunities of combinations, showing the main contexts of applications and the resulting advantages, is necessary to synthetize and provide the state of the art of this field of research. Indeed, without a comprehensive SLR, researchers and practitioners must rely on a fragmented and voluminous range of scientific papers whose suggested applications, taken individually, are not sufficient to highlight the general value of the integration among technologies. The absence of a SLR on the relationships between AI and other cutting-edge technologies may perpetuate knowledge gaps about their integration and significantly cloud the benefits of their synergies. Filling this gap should provide researchers with insights and help practitioners formulate optimal strategies for implementing AI with other cutting-edge technologies in production systems.
The aim of this research is to propose a comprehensive SLR on the integration of AI with a wide range of cutting-edge technologies to provide an overview of the opportunities of implementation in operations. The work will clarify which is the impact on the business performance considering all business functions and operations within production systems and SCs. Starting with the classification and analysis of scientific papers, this research will collect emerging practices that combine AI with other cutting-edge technologies and are implemented in specific business functions and processes, with a clear impact on either the market or the organizational performance in production systems. From the analysis of the collected emerging practices, the paper provides the state of the art on the fragmented literature for developing a future research agenda. The SLR considers the integration of AI with 88 different types of cutting-edge technologies grouped in 12 macro-categories: 3D printing, big data and data analytics, blockchain, computing, digital applications, geospatial technologies, human–computer interaction, immersive environments, internet of things, open and crowd-based platforms, proximity technologies, and robotics. The technologies exhibiting high Co-Occurrence Ratios (CORs) with AI applications will be considered as the most promising ones, clarifying in which contexts they could be implemented, and which could be the expected impact on business performance.
The paper is structured as follows. Section "Background" presents an overview on AI and the other cutting-edge technologies. Section "Materials and methods" describes the SLR and the article selection criteria. Then, a first general study on the integration of AI with the 12 cutting-edge technologies is performed, followed by a detail of the most significant relationships between AI and 88 types of cutting-edge technologies. The discussion section will focus on the theoretical, practical, managerial implications, limitations and future research. Conclusions close the work.
Background
This section presents an overview of the state of the art and technological development of emerging technologies. The classification of each cutting-edge technology and the relative specific technologies is shown in Table 1. The taxonomy of technologies was constructed considering classifications already found in the scientific literature in the area of Industry 4.0 and digitalisation of production systems (Attaran, 2020; Bajic et al., 2021; Dong et al., 2021; Oztemel & Gursev, 2020; Perano et al., 2023; Raut et al., 2020; Winkelhaus & Grosse, 2020; Zheng et al., 2020).
Artificial intelligence
AI is a field of information technology focusing on the management of technologies that learn to make decisions independently and to carry out actions instead of human beings (Dwivedi et al., 2021). AI can replace an entire system, taking decisions, or it can be used to improve a specific process. For example, a warehouse management system can show the current levels of different products, while a smart warehouse can identify deficiencies, analyse their reasons and effect on the SC, and adopt methods to increase efficiency (Geunes & Su, 2020). AI is not a single technology: it is a generic term including any type of software or hardware component that supports machine learning, artificial neural network, deep learning, cognitive computing and natural language processing.
Recently, significant progress has been made in the development of more powerful AI techniques. In particular, machine learning models are increasingly useful for identifying hidden relationships within data. The word “learning” describes how algorithms will automatically become more accurate as they receive additional data (Rai et al., 2021). The study of machine learning techniques is constantly and rapidly evolving. Indeed, there have been many advances in research to make these models more interpretable and provide rapid solutions. It is observed a growing focus on automated machine learning since it supports the automation of specific steps such as selection, optimisation and training of the model, encouraging and spreading its use (Larsen & Becker, 2021). Another highly developed branch of machine learning is reinforcement learning in which learning is based on continuous simulations providing, for instance, improvements in robot control (Liu et al., 2023).
On the other hand, artificial neural networks use learning algorithms that can independently carry out changes when they receive new inputs (Joung & Kim, 2021). Different types of neural networks are developing such as recurrent neural networks or convolutional neural networks. They are mainly applied to process sounds, images and videos (Mahmoud et al., 2023). Within production systems, ANN and machine learning techniques can analyse historical data of production processes and identify optimal parameter combinations to improve efficiency, reduce production time or minimise material waste (Geunes & Su, 2020).
Deep learning is part of a broader family of machine learning methods based on artificial neural networks using representation learning. The adjective “deep” refers to the use of multiple layers in the network (Palombarini & Martínez, 2021). Huge progress has been made for this technology due to increased computational power and data availability. One of the biggest challenges in deep learning is the interpretability of the results. Recently, new techniques have been developed to understand and visualise how these algorithms work by studying the intermediate layers and making more informed decisions (Yao et al., 2023). In production systems, deep and machine learning can be used for predictive maintenance and demand forecasting. For instance, these techniques can impend failures or signal anomalies and reduce bottlenecks (Singh et al., 2023; Zhang et al., 2023).
Computer vision is a field of computer science that allows computers to observe, identify and process images in the same way as human vision. Significant advances have been made in this field, such as the detection of specific objects within images. For example, product tracking in complex scenarios has been improved to facilitate inventory tracking (Ardanza et al., 2019). A significantly evolving field of research for computer vision is the generation of realistic and three-dimensional images.
The purpose of cognitive computing is to solve complex problems featured by uncertainty and ambiguity that can have dynamically changing situations conflicting with each other. With the exponential increase of data, it is crucial to integrate different sources to make informed, ethical and responsible decisions. Therefore, cognitive computing is mainly used for problems of complex resolution. Finally, natural language processing is the ability of a computer program to understand spoken and written human language (Yue Wang et al., 2021a, 2021b). Several developments have been conducted on the generation of coherent and controlled text as ChatGPT that can support various tasks (Wu et al., 2023). It could be used to process supply contracts or order documents to reduce processing times. Finally, it is an excellent tool for analysing customer feedback to obtain valuable information on customer preferences and opportunities for performance improvements (Bauer et al., 2023).
3D printing
3D printing is the process of building a physical object from a CAD model or a digital 3D model (Khorram Niaki & Nonino, 2017). The process is based on spreading layers of material in liquid or powder form then fused together using specific techniques. 3D printing has improved several processes in manufacturing productivity. The technology is mainly used to reduce lead times in prototyping or to create auxiliary devices that enable the production of new products. This allows to reduce the time between the idea generation of a product and its availability for sale (Khorram Niaki & Nonino, 2017). In particular, rapid prototyping allows for greater innovation and experimentation while reducing costs (Chatzoglou & Michailidou, 2019). Compared to traditional manufacturing processes, 3D printing allows creating complex shapes using less material. However, the speed of 3D prints is not high, therefore this technology is less used in mass production (Rong et al., 2020).
There are different 3D printing techniques depending on the material layer and the equipment used. For instance, powder bed fusion uses the laser beam to create physical objects from powder layers (Duda & Raghavan, 2016). Stereolithography creates three-dimensional objects from a liquid polymer that hardens upon contact with laser light. Polyjet builds parts by throwing droplets of photopolymer onto a build platform and solidifying them with a UV light. Using these technologies, it is possible to create parts with honeycomb structures that are particularly helpful for the aerospace, automotive and medical sectors. However, they are mainly used for small series or prototype production (Rasheed et al., 2021). Bioprinting combines cells and biomaterials to create biomedical parts with the aim of simulating the natural features of tissues. It can easily create customised prostheses that are adapted to the patient’s specific needs (Li et al., 2023). Fused filament fabrication and fused deposition modelling are techniques that add layer after layer of molten plastic to create a model or product. It is mainly adopted to produce spare parts and the replacement of damaged or obsolete components (Mousapour et al., 2021). Directed energy deposition is a method that uses a focused energy source, such as laser, plasma arc or electron beam, to melt a material that is simultaneously deposited by a nozzle. It is highly used for the repair and maintenance of components to reduce machine downtime. In addition, this technology can be applied for the production of large parts that cannot be created with traditional methods (Pandey et al., 2022). Finally, binder jetting is a 3D printing process in which an industrial print head selectively deposits a liquid binding agent on a thin layer of dust particles to build high-value parts (Colton et al., 2021).
Big data and data analytics
Big data technology is a combination of unstructured, semi-structured or structured data that can be collected and extracted to gain particular insights using predictive modelling and other advanced analytics projects (Sahoo, 2021). The five main features of big data technology are based on 5 V: volume, velocity, variety, veracity and value (Hofmann, 2017). Volume refers to the basis of big data, as it represents the initial size and amount of data collected, while velocity refers to the data generation rate. Variety refers to data diversity: the challenge in variety concerns data standardization (Jagadish et al., 2014). Veracity refers to data quality and accuracy: data collected could have missing parts, be inaccurate or not be able to provide real information. Finally, the value refers to the activities that companies can perform with the collected data (Akter et al., 2016).
The value of big data technology increases significantly in relation to extractable information when well implemented within production systems. Having more data on potential customers can allow companies better customizing products and achieving a high level of satisfaction (Raguseo & Vitari, 2018). However, big data technology presents challenges such as data protection, relevant data identification, and cleaning techniques for the analysis of data that have different formats (Escobar et al., 2021; Sivarajah et al., 2017). Specifically, data mining is the process of analysing hidden patterns of data from different perspectives in order to obtain useful information. By applying this technique within production systems, it is possible to analyse production data and detect patterns that have an impact on process performance. These patterns can include cycle times, the occurrence of defects, or errors (Rosati et al., 2023). Sentiment analysis and user behaviour analytics are a type of data mining measuring the inclination of people's opinions through computational linguistics and text analysis, which are used to extract and analyse personal information from the web (Jin et al., 2021). They lead to monitor customer satisfaction and make informed decisions on appropriate product and service improvements (Raguseo & Vitari, 2018). Specifically, they support marketing strategies by identifying which messages or channels can improve company profits and monitor competitors. User behaviour analytics is a powerful methodology used to analyse and monitor the behaviour of operators in the workplace. It involves closely examining and interpreting data related to their actions, activities, and the time they dedicate to working with machines (Damiani et al., 2020).
Blockchain technology
Blockchain technology is a distributed ledger shared among the nodes of a network. It differs from traditional databases for recording data (Iansiti & Lakhani, 2017). In particular, blockchain records data in blocks linked together using cryptography. Specifically, when new data is generated, it is recorded in a block. Once the block is filled it is linked to the previous block with a specific hash code. In this way, the blocks are always stored linearly and chronologically (Vatankhah Barenji et al., 2020). It is known for its crucial role in cryptocurrencies (Fosso Wamba et al., 2020). However, several companies are using this technology as a reliable and secure way to record different transactions (Jabbar & Dani, 2020). The benefits of blockchain are various, including the elimination of intermediaries and the costs associated with them. Furthermore, transactions can be completed in a short time (Cammarano et al., 2022a, 2022b, 2022c). This is useful for cross-border transactions that take time due to issues such as payment confirmation (Chang et al., 2020). Moreover, it ensures transparency and visibility of data thanks to its decentralization feature (Bai & Sarkis, 2020; Vatankhah Barenji, 2022).
Blockchain is increasingly being used in production systems for its benefits such as product traceability, security and efficiency of operations. It can transparently track products along the SC, ensuring authenticity and origin of products. It improves inventory management by providing visible and real-time information to all actors in the network, improving supply planning. This facilitates collaboration and data sharing between stakeholders involved in production (Cozzio et al., 2023). Yet, there are several challenges, including cost and inefficiency. For example, the block size debate is one of the most pressing issues for blockchain scalability in the future. Finally, government regulation regarding cryptocurrencies is still unclear. A further tool connected to the blockchain is the smart contract. It is a code that can be integrated into the blockchain to facilitate and verify a contractual agreement. Smart contracts operate on a series of conditions agreed by users. When these conditions occur, the contract terms are automatically executed. These tools could facilitate various activities within the SCM (Pournader et al., 2020). They automate payments between the parties involved only upon fulfilment of predefined conditions such as the successful delivery of products. This reduces the need for intermediaries and speeds up the payment process because human intervention is not required (Grida & Mostafa, 2023).
Computing
Cloud-related technology is the processing way in which data is stored on multiple servers and can be accessed online from any device. Instead of saving data in the local hard drive of a single computer, users can store data on third-party online servers. Cloud computing services provide users with applications including email, archiving, data backup and retrieval, data analytics, audio and video streaming and software on-demand (Marston et al., 2011). Cloud-based software offers companies several benefits, such as using the software from any device via a browser (Yu et al., 2017). Furthermore, it enables significant cost savings in information management technologies and infrastructures. Within production systems, it improves the monitoring and maintenance of production equipment. It facilitates collaboration between teams through data sharing (Shahul & Arunkumar, 2022). However, there are risks involved with its applications. One of the biggest concerns data security. Cloud computing company servers can also be victims of natural disasters, internal bugs and power outages (Low et al., 2011).
To improve the inefficiencies of cloud computing, new tools and techniques have been developed such as edge computing, fog computing and cloudlet computing. Edge computing processes data as close as possible to its origin in order to reduce network latency by minimizing communication times between client and server (von Stietencron et al., 2021). Fog computing is an infrastructure interposed between edge and cloud computing to enable more efficient data processing, analysis and storage, thereby reducing the amount of data that has to be sent to the cloud (Wu et al., 2017). A cloudlet is a small-scale data centre designed to quickly deliver cloud computing services to mobile and wearable devices. The purpose of a cloudlet is to increase the response time of applications (Fang et al., 2018). Such technologies allow real-time data analysis to be performed directly in the proximities of the items. This reduces latency, enabling a timely response from data generated by sensors and production machines. These systems are very useful for the early detection of anomalies or machine malfunctions (Teoh et al., 2023; Yu et al., 2023). Quantum computing is an area of computing focused on the development of information technologies based on the principles of quantum theory. Quantum computers can handle operations at significantly faster speeds than traditional ones and with lower power consumption. It is used to optimise production processes to solve complex problems such as managing workflows or reducing production times (Acín et al., 2018).
Digital applications
Digital applications such as social media & network, mobile applications and web applications have become an indispensable tool for people and companies (Kietzmann et al., 2011). Social media & network are Internet-based technologies that enable users to share text, audio, video and photo contents and interact with other individuals. Companies use these technologies to find and interact with customers, increasing sales through advertising and promotions, assess consumer trends, and offer customer service or support (Gao et al., 2020).
Mobile applications are software designed to run on smartphones and tablets, providing users with functions comparable to PC applications. The demand in mobile devices has expanded in the last decades and consequently the capability to develop mobile applications in different areas has grown (Varriale et al., 2023). These apps are greatly employed because people are progressively willing to use their smartphones and tablets to quickly perform various complex tasks (Hwang et al., 2020). Within production systems, they can be used for monitoring machines and updating order statuses. They can keep track of material stocks and inventory, making efficient use of resources. Operators can easily communicate and collaborate with the apps because they provide instant sharing of reports and documents and enable real-time communication (Vasquez Ubaldo et al., 2022).
A web application is a software program that runs on a web server. Despite the traditional desktop applications, which are started by the operating system, web applications must be accessed via a web browser. Web apps have several benefits, if compared to desktop applications: they should not be developed for multiple platforms and software updates should not be distributed to users which have direct access to the updated version (Murugesan, 2007). From the user's point of view, the data recorded in a web app is processed and saved remotely. This allows accessing the same data from multiple devices, rather than transfer files among computer systems. They are useful tools for planning production, allocating resources, managing inventory and improving the coordination of activities. Moreover, they can facilitate supplier relationship management through shared order management systems (Makanda et al., 2022). The drawbacks are limited access to system resources and the dependence on browser performance (Sivarajah et al., 2015). Among the digital applications, robotic process automation is the practice of automating routine business practices with software agents (bots) that perform tasks automatically. A bot is an automated software program that digitally replicates a human activity. In particular, it can automate activities without direct human intervention (Hyun et al., 2021). Finally, in this area there are technologies necessary for digitization that allow dematerialization, i.e. the elimination of paper documents, such as: digital signatures, electronic invoices, digital contracts (Hagsten & Falk, 2020).
Geospatial technologies
Geospatial technologies refer to modern tools that contribute to the geographic mapping and analysis of the Earth. The advent of satellites allowed to detect images of the earth's surface and human activities. In addition, computers enabled the storage and transfer of images, maps and datasets on socioeconomic and environmental phenomena, named GIS (Bateman et al., 2002). In the last decade these technologies have been significantly used in industrial engineering, agricultural and environmental monitoring (Abdelhaleem et al., 2021). They map manufacturing resources such as equipment, warehouses, and facilities to identify and plan the most efficient routes for optimally managing resources. In this way, it is possible to keep track of the flow of goods and identify delays and congestion to be resolved promptly (Norwood et al., 2014). The data collected by these technologies can intercept areas where environmental factors are important for marketing and can influence the demand for products and services. In addition, they are increasingly being adopted to improve safety at work, identifying high-risk areas and preventing employee health and safety (Moro et al., 2023).
There are several geospatial technologies such as remote sensing for the data acquisition of an entity (DeFries et al., 2007), web mapping in which geospatial data is displayed (Veenendaal et al., 2017), GPS providing the position, speed and synchronization of a specific entity, and the global navigation satellite system providing global Earth’s coverage (Li et al., 2015). These technologies are used in logistics to track goods and ensure their quality (Swayne & Lowery, 2021). They can monitor outbreak areas or assess the status of crops. Finally, they can be used in marketing to target advertisements to specific users (Brink & van Rensburg, 2017).
Human–computer interaction
Human–computer interaction focuses on the identification of IT methods and techniques that support humans (Parasuraman & Riley, 1997). There are several technologies associated to this area, such as: chatbot, facial recognition, voice recognition, speech recognition and biometrics. Chatbots are programs that simulate interactive human conversation using pre-calculated key phrases and acoustic or text-based signals. They are often used for customer service and marketing systems (Go & Sundar, 2019). In addition, they are used to managing orders and shipments by providing updates on their status. They can be employed in sales support, answering questions from potential customers. This technology can also be adopted for training operators to receive feedback on their performance (Herbert & Kang, 2018).
Facial recognition software is an application automatically identifying or verifying people from video frames or digital images. It is primarily used as a protective security measure and to verify staff activities, such as presence, computer access for secure work environments (Garaus et al., 2021). Vocal recognition is a technique in which specialized software and systems are able to identify, distinguish and authenticate the voice of a single speaker (Azanha et al., 2016). Speech recognition is the ability of an electronic device to understand spoken words. A microphone records a person's voice and the hardware converts the signal from analogue audio to digital audio. Finally, biometrics is a biology-based authentication method which authenticates secure access through human biological information such as DNA or fingerprints (Toledano et al., 2006). These technologies can improve the security of production systems by eliminating the use of badges or passwords. Through fingerprint or retina control systems, it is possible to record the presence and working time of employees in an automated way. This can simplify attendance management and activity monitoring (Zhao et al., 2009).
Immersive environments
Immersive environments are virtual simulations giving the impression of physical presence, filling users’ visual field. Immersive environments have varying sensory immersion and user awareness degrees within the digital world (Parise et al., 2016). Among the technologies based on immersive environments there are augmented reality, virtual reality, mixed reality, extended reality, digital twin, holograms and gamification. Augmented reality is a version of the physical world obtained through digital visuals, sound or other elements provided by technology. One of its purposes is to highlight features of the physical world, increase understanding of those features, and extract information that can be applied to the real world. For example, companies can use augmented reality to promote products or services or launch new marketing campaigns (Wang et al., 2022). Moreover, it can provide guidance and instructions overlaid on objects when repairing or assembling components. Operators, wearing smart glasses, could be trained and instructed step by step. It can be adopted in the quality control phase of products to detect defects and improve accuracy during inspection (Maio et al., 2023).
Virtual reality immerses users in a completely different environment developed by computers. Users can immerse themselves in an animated scene or in a real place that has been photographed (Dalgarno & Lee, 2010). Engineers can create 3D models, allowing them to simulate product features. This promotes more accurate design as well as cost reduction. This technology can simulate production layouts, allowing inefficiencies and potential logistical problems to be identified (Wiendahl et al., 2003). Mixed reality is a hybrid system involving both physical and virtual elements, whereas extended reality refers to real and virtual environments generated by computer technology and wearable devices (Flavián et al., 2019). They can visualise machine KPIs and better understand the improvement strategies to be undertaken. In addition, immersive technologies enable remote collaboration, remote image and video sharing (Baroroh & Chu, 2022).
A digital twin is a virtual model of a process, product or service. This combination of virtual and physical world enables data analysis and systems monitoring to avoid problems before they occur, prevent downtime, develop new opportunities, and even plan future operations using simulations (Fuller et al., 2020). Holograms are images created by a photographic projection: the images appear as a three-dimensional representation of a two-dimensional object (Edwards, 2021). Finally, gamification is based on the use of game design principles to improve customer engagement in non-game related activities. Specifically, real-world activities are represented like a game to motivate people to achieve their scopes. For instance, companies use gamification to increase interest in a product or service, or simply to deepen their customers' relationship with the brand (Hsu & Chen, 2018).
Internet of things
IoT is a computing concept that describes the collection of network-enabled devices, excluding traditional computers such as PCs and servers. These devices are able to identify, send and receive data over the network (Chae & Olson, 2021). Types of network connections can include Wi-Fi, LoRa (Long Range) and 5G connections (Clements et al., 2021). These technologies are increasingly being adopted in production systems for process monitoring, product traceability, predictive maintenance, workplace safety and process optimisation. They enable faster communication between machines with high reliability and low latency, which reduces the time to operations control. This type of connection supports real-time collaboration between several operators working remotely (Molka-Danielsen et al., 2018; Xu et al., 2020).
The IoT includes smart devices that can connect to the network. There are several “things” that can interact on the network via smart sensors, for instance, refrigerators and thermostats, home security systems and wearables. One of the purposes of the IoT is to have devices that communicate in real time, improving efficiency and ensuring more information without human intervention (Gottge et al., 2020). The use of smart devices is bringing competitive advantage to companies. For example, by monitoring data on energy use and inventory levels, a company can significantly reduce its overall costs (Li et al., 2021). With the support of industrial wearables (e.g. smart helmet, smart belt, smart glass, smart glove and smart shoe) and mobile gateway, a system can provide an engaging and interactive environment to connect the physical and the digital world (Guo et al., 2020). Through smart sensors and mobile sensing, potential hazardous conditions such as high temperature or the presence of toxic substances can be detected, ensuring a safe working environment for employees (Osunmakinde, 2013). However, there are privacy issues that still need to be addressed. Additionally, technology has advanced much faster than regulation, so there are potential regulatory risks (Ben-Daya et al., 2019).
Open and crowd-based platforms
Over the past decade, the term “open” has been widely used in the literature on business, management and innovation (Cammarano et al., 2022a). By expanding the original paradigm of open innovation (Chesbrough, 2003), several technologies and platforms have been developed allowing disparate individuals to cooperate, interact and collaborate with companies, enabling knowledge exchange and executing transactions (Wolff & Schlagwein, 2021). These technologies include open innovation platforms, crowdsourcing platforms, open science platforms, open access platforms, crowdfunding platforms, open data platforms, open-source technologies.
Open innovation platforms support and facilitate open innovation initiatives by employing ideas and knowledge from partners, suppliers and customers (Cruz et al., 2021). Crowdsourcing platforms allow crowds to work collaboratively to solve specific problems (Feng et al., 2021). They can be used to solve technical or complex problems within production systems by stimulating innovation from external users (Vianna et al., 2020). Open science platforms enable the crowd, citizens and disparate individuals to devote time and effort to science and innovation purposes (Wildschut, 2017). They foster interdisciplinary collaboration between research and production systems. Open access platforms allow the dissemination of knowledge in an egalitarian way. They promote transparency, accessibility and open sharing of scientific knowledge (Trishchenko, 2019).
Crowdfunding platforms favour the raising of funds from disparate individuals to support companies and projects. They can be a way to finance new product launches or technological upgrades within production systems (Belleflamme et al., 2014). Open data platforms allow the dissemination of data in a transparent and shared way among users. They provide access to public data that can be used to analyse and plan production processes. Data on logistics can be used to evaluate product distribution options (Lourenço, 2015). Finally, open-source technology allows the use of free and modellable software (Bollinger, 2003). Most of these technologies improve communication among individuals and knowledge sharing, also reducing costs and time for R&D units or for the development of computer codes. Industrial automation systems based on open-source software enable efficient control and management of production activities by monitoring data in real time and providing detailed information on system performance (Cammarano, Michelino, et al. 2022b; Minchala et al., 2020).
Proximity technologies
Proximity technologies are based on proximity sensors capable of detecting the presence of close entities without any physical contact. They detect a target within a defined range and are used in collision warning and collision avoidance systems. It can use sound, light, infrared radiation, or electromagnetic fields to detect a target (Gandhi et al., 2014). In production systems, proximity sensors can be used to detect the arrival of components or materials during assembly. They can be also employed to detect the presence of items in specific areas, automatically activating or deactivating machines or equipment (Tomar et al., 2023).
Among the technologies that belong to this category there are: beacon, motion detectors, RFID, QR code and data matrix, Bluetooth and near field communication. Beacons are small devices detecting a precise location within a narrow range. They are often used for internal location as they provide more accurate information. They can improve the safety of working environments by warning operators that they are approaching a dangerous area (Kim et al., 2017). Motion detectors are electrical devices using a sensor to detect nearby motion. They are a crucial technology for safety, automated lighting control and energy efficiency (Yang & Hsu, 2009). RFIDs use wireless communication between an object and an interrogation device to automatically identify and track the physical location of each entity. They are used for the traceability of products along the SC. Each product can have an RFID tag that contains information such as the identification code, product specifications and production date (Cao et al., 2019).
QR and data matrix codes are designed to be read by smartphones. Since these technologies can carry a multitude of data, they can provide a large amount of information, including links, text or other elements. They can be employed for product traceability, efficient inventory management and quality control (Liébana-Cabanillas et al., 2015). Bluetooth is an open wireless technology standard for transmitting data from fixed and mobile electronic devices over short distances. For example, operators can use their mobile devices to monitor machines through Bluetooth systems. Finally, NFC is a wireless technology allowing a device to collect and interpret data from another device located nearby (Ondrus & Pigneur, 2009). For instance, an NFC device can be used to quickly transfer configuration parameters or setup files to a production machine. This eliminates the need to manually record settings, reducing set-up time and increasing efficiency (Silvestri et al., 2020).
Robotics
Robotics is the field of research on machines that can autonomously or semi-autonomously perform physical tasks to help and assist humans. Robots typically perform tasks that are too repetitive or dangerous to be safely performed by a human (Cherubini et al., 2016). There are different types of robots depending on their operations. Within production systems, an autonomous mobile robot can understand and move independently in the environment using a sophisticated set of sensors and computation to plan the path to take in order to move in space. They can be used to transport materials and products along production lines and perform handling tasks (Fragapane et al., 2022). Autonomous (driverless) vehicles are vehicles that can move without input from a human driver. Complex sensors and actuators are used to create a map of the environment and detect the presence of vehicles and pedestrians (Paddeu et al., 2020). Cobots are robots that work with humans, either as an assistant in an activity, or as a guide. They are programmed and designed to respond to human instructions and actions. They support collaborative tasks such as assembling components, processing materials or lifting heavy objects, reducing the workload of operators and improving productivity (Krüger et al., 2009).
An industrial robot is a robotic system used for manufacturing. They are automated, programmable and capable of moving on three or more axes. Typical robot applications include welding, palletizing, assembly, disassembly, painting, pick and place, packaging and labelling, product inspection and testing (Lee & Murray, 2019). A service robot is capable of performing useful tasks excluding industrial applications such as surgery to remove tumours or implant new prostheses to the patient (Randhawa et al., 2021).
Unmanned vehicles, such as drones, are used for various purposes where it can be uncomfortable or dangerous for a human operator to work. They can be employed to monitor facilities and perform security inspections (Dhote & Limbourg, 2020). A wearable robot is a wearable device used to improve an individual’s movement and physical capabilities. Sensors or devices may receive verbal, behavioural, or other inputs to facilitate specific types of movement (Shi et al., 2020). They can be used to assist human workers in performing physically demanding tasks, such as reducing physical effort (Gonsalves et al., 2023). Finally, AS/RS are computer-assisted systems capable of retrieving objects or storing them in specific locations. These systems help speed up production and shipping activities (Foumani et al., 2018).
The integration of AI with other cutting-edge technologies within production systems
The study of AI has grown exponentially in different research fields. Given the great attention on this topic, several authors have tried to systematize and summarize the literature on AI by providing different perspectives. Table 2 compares the most recent systematic literature reviews that have studied and provided theoretical implications on the use of AI within research fields such as production systems, SCM, and manufacturing. Specifically, the table presents two sets of studies: the former discussing how AI is used within these manufacturing scenarios without the integration of other cutting-edge technologies and the latter evaluating AI in combination with other technologies to improve specific industrial aspects. As is evident, research has mostly focused on the contribution of AI in production systems without considering the integration of other technologies. There are some studies highlighting the importance of integrating AI with other technologies but they are limited to combining one technology at a time. An exception is the study by Andronie et al. (2021), which discusses the integration of AI with two technologies—IoT and big data—to manage the complexity and flexibility of cyber-physical manufacturing systems. Only recently, research is moving into studying the integration of AI with other technologies and this article fits into this stream by focusing on 12 macro-categories in which 88 different emerging technologies are classified.
Materials and methods
The purpose of the article is to clarify how and in which contexts AI can be integrated with other cutting-edge technologies to improve market and organisational performance in production systems. To achieve this research objective, the classification framework involves the identification of emerging practices collected from the scientific literature, based on business case studies, pilot projects and simulation models. An ‘emerging practice’ refers to a non-standard way of carrying out an activity in a business process using new and innovative technologies to achieve a specific impact. Figure 1 is a conceptual representation of the classification procedure used to perform the SLR of an emerging practice through specific labels. Each practice was synthesised into technologies, business functions, business processes and impacts on business performance. Emerging practices where AI is combined with other technologies for specific business functions to achieve specific impacts were collected. Four functions are considered: distribution, marketing operations and purchasing. Impacts have been classified into market and organizational ones, the former referring to performance benefits achieved from relations with external actors such as customers, the latter referring to performance benefits achieved from internal issues.
Conceptual representation of an emerging practice through labels
The classification framework is based on a manual content analysis of scientific articles to intercept emerging practices and to classify them according to the fields considered. A team of industrial engineering experts conducted the classification to standardise in which contexts the technologies are employed, and for which impacts. An example of the manual content analysis and classification performed by the team of experts is presented below in Fig. 2. It represents an emerging practice that combines a machine learning algorithm and an industrial robot within a warehouse to reduce time for order fulfilment (Li et al., 2020). The experts read the document and associated the appropriate labels such as technology, business functions, business process and impact. However, in some cases, the emerging practice could describe the combination of several technologies, processes and impacts at once. Therefore, the same practice could be represented by the multiple combination of these variables. In this way, it was possible to consider the combination of the various elements as the statistical unit on which the analyses will be carried out.
Example of labelling of an “emerging practice”
In order to ensure rigour and robustness of the study, the research method used is SLR because it is a replicable and transparent method and provides comprehensive results, overcoming the disadvantages of single study analyses (Tranfield et al., 2003). The SLR is organised as follows: (1) criteria for identifying articles; (2) selection, evaluation and classification of articles; (3) co-occurrence assessment.
Criteria for identifying articles
The database used to identify articles is Scopus since it is a multidisciplinary database and ensures greater clarity, rigour and replicability of the review (Festa et al., 2018; Paré et al., 2015). The subject areas considered are based on articles published in international journals in the fields of accounting, economics, finance, management science and operations research in the Q1 and Q2 quartiles according to Scimago Journal Ranking and ISI Web of Science. To conduct the SLR, the single technologies in Table 1 were considered. For each technology, the query is a combination of [SUBJAREA] AND [PUBYEAR] AND [KEYWORDS]. Specifically, [SUBJAREA] corresponds to the subject areas considered for this search; [PUBYEAR] indicates the time horizon considered. The selected scientific contributions cover the period from January 2019 to December 2022. The reason for this choice is twofold. Firstly, the technologies under analysis are considered cutting-edge. Secondly, it is necessary to have a homogeneous sample for a specific period of analysis of AI complementary technologies to minimise over-correlation errors. An example of the search string adopted for AI is:
[SUBJAREA] AND [PUBYEAR] AND (TITLE (“Artificial intelligence “) OR KEY (“Artificial intelligence “) OR TITLE (“Artificial neural network “) OR KEY (“Artificial neural network “) OR TITLE (“cognitive computing “) OR KEY (“cognitive computing “) OR TITLE (“computer vision “) OR KEY (“computer vision “) OR TITLE (“deep learning “) OR KEY (“deep learning “) OR TITLE (“machine learning “) OR KEY (“machine learning “) OR TITLE (“natural language processing “) OR KEY (“natural language processing “)).
Selection, evaluation and classification of articles
The aim of this first phase was to identify and collect emerging practices from business case studies, pilot projects and simulations related to the integration of AI with cutting-edge technologies considered for specific contexts in order to achieve certain impacts. At this stage, industrial engineering experts were involved in the research and conducted the manual content analysis of the articles. The search identified 18,125 potential articles, 839 of which were not downloadable. The analysis involved reading the remaining 17,286 papers. The inclusion criteria for this research are based on the identification of emerging practices that use cutting-edge technologies, including AI, in specific business functions and processes, and expected positive impacts on business performance. Figure 3 summarises the overall functioning of the classification framework. Starting from the identification of articles, eligible articles are those for which emerging practices have been identified in specific business functions and processes (as marked in bold) to achieve specific impacts. Table 3 describes in detail the business functions and processes considered for production systems.
Process of selection, evaluation and labelling of scientific articles
Therefore, 571 documents were identified describing emerging business case studies and business applications using AI technology in production systems. For this research, book chapters, conference papers, magazines and newspapers were excluded. In addition, literature reviews, conceptual papers, articles referring to the public sector and papers that do not clarify in which specific business process the technology can be implemented, as well as do not empirically demonstrate the positive impact on performance, were not considered.
Co-occurrence assessment
Starting from the total number of combinations of AI with other technologies, business functions, business processes and impacts, it is possible to understand what percentage of AI emerging practices is combined with other technologies using the COR index. This is defined as the percentage of emerging practices in which a specific cutting-edge technology is used together with AI on the total number of emerging practices in which AI is employed. The following formula indicates the COR index:
where \({N}_{AI, Tech}\) is the total number of emerging practices in which AI is combined with another specific technology, \({N}_{AI,}\) is the total number of emerging practices where AI is employed.
The ratio ranges from 0 to 100%. The value is 0 when the analysed technology is never combined with AI, 100% when it is always used together with AI. The higher the COR, the greater the relevance of the technology combination with AI and the technical development of this combination. The COR can be calculated both to understand the degree of combination of AI between different technologies and to investigate the degree of combination of AI considering different technologies in the specific business function, business process and impact. For instance, by filtering emerging practices for specific impacts, business functions and processes, subsets of the main dataset can be analysed. This allows the calculation of the COR for each of these subsets.
Results
From the SLR, 1,843 practices are associated with AI: 810 of them are not linked to any other cutting-edge technology, the remaining 1,033 (56%) combine AI with at least another technology. While 806 practices disclose the relationship between AI and one cutting-edge technology, in 227 cases more than one technology is integrated with AI: to simplify the discussion, these combinations are split in order to study one technology at time, resulting in 592 couples AI-technology. In this study, the integration of AI with only one other technology at a time is analysed. Therefore, 1,398 couples are considered with this analysis.
Table 4 reports the CORs of AI with the other macro-technologies. The category big data and data analytics is strongly connected with AI with four out of ten practices requiring their conjunct implementation. On the one hand, technologies linked to data capture, management and transmission are fundamental for implementing AI algorithms, on the other hand data mining and analytics require AI algorithms to provide powerful results to users. The second macro-technology that is most frequently associated with AI is IoT, with a COR of about 10%, followed by digital applications and robotics.
In the next sections, the analysis is performed by grouping practices by impact (see Appendix, Tables 7 and 8), business function and process (see Appendix, Tables 9, 10, 11 and 12). The most interesting combinations will be those exhibiting higher values of COR, since this will underscore the need to combine AI with other cutting-edge technologies to achieve interesting results. On the other hand, this means that the relationship with AI is more developed and then less risky. The domain and journal coverage of the SLR was presented in Table 5.
Integration of AI with other cutting-edge technologies for achieving market and organizational impacts
Figure 4 displays the relationship of technologies with the impact on business performance. Considering market impacts, the combination of AI with big data and data analytics, digital applications, geospatial technologies, IoT and robotics supports competitive advantage. For instance, using sentiment analysis and machine learning algorithms it is possible to analyse the positive and negative consumers’ feedback to detect the most effective decision-making strategies for managers in reverse logistics. In this way, companies will be able to minimize returned products, waste, inventory and costs, while maximizing efficiency and profit. Moreover, deep learning can be used with smart sensors for fault diagnosis of a production machine. This technique improves maintenance performance by monitoring the products' health in real time and identifying the defects thanks to the data provided by smart sensors, improving the competitive advantage and saving on maintenance and operating costs. The integration of AI with big data and data analytics, blockchain, computing, digital applications, geospatial technologies, IoT, proximity technologies and robotics leads to greater customer satisfaction. For example, by applying fog computing and ANNs, it is possible to improve customer satisfaction by predicting customers energy consumption and providing targeted promotions. Big data and data analytics, computing, and IoT, in conjunction with AI, improve better product/service quality/value/differentiation. For instance, AI can be used to model a food inventory system that assesses the effect of cross-perishability due to the joint storage of chemically different foods. Using an IoT system it is possible to collect information including freshness, temperature, humidity, air and then predictive models can be generated to improve inventory conditions. The integration of AI with big data and data analytics, digital applications, geospatial and IoT can enable maximisation of revenues. For instance, GIS and machine learning techniques can be used in the operations planning of industrial infrastructures: by mathematically managing geospatial data, it is possible to identify the optimal position for the building of industrial plants to ensure maximum revenues and efficiency.
Distribution of AI impacts when integrated with other cutting-edge technologies
Regarding organizational impacts, AI is often linked with big data and data analytics to enable better cost reduction, information management and efficiency and productivity. For example, machine learning techniques and big data technologies are being used to improve the forecasting of energy supply from power plants. This can solve problems related to planning and renewable energy production, such as reducing costs, reducing overloads and improving efficiency. In addition, ANN techniques and predictive analytics can estimate the best lead times for each stage of production, reducing inventory costs. Big data and data analytics and IoT in combination with AI support energy efficiency. For instance, the use of genetic algorithms and big data technologies, based on the summer and winter production data from industrial equipment, can allow significant energy savings and a production increase, which leads to economic benefits such as the improvement of sales. Big data and data analytics, computing, digital applications, IoT, proximity tech and robotics, in conjunction with AI, lead to higher flexibility. For example, truck-drone hybrid delivery systems use machine learning algorithms to develop optimal delivery routes so that all deliveries are completed in a shorter time. Specifically, a drone launches off the truck for a specific delivery, instead the truck continues to serve other customers in different locations. Once the drone has completed service to a customer, it must return to the truck. In this way, several advantages are achieved because the strengths of single vehicles can be exploited synergistically.
The impact innovation, knowledge & technology management is supported by various technologies complementary to AI such as big data and data analytics, human computer interaction, immersive environments, IoT, proximity technologies and robotics. For example, deep learning and emotion sensing techniques can be employed to adapt responses for chatbots to avoid responding inappropriately. Combining AI with big data and data analytics, computing, geospatial technologies and IoT leads to a risk reduction. Collaborative robotics systems can be employed in production systems as an alternative handling solution to collect objects in a messy environment. This solution allows industries to reduce risk because cobots can handle a large assortment of parts. Technologies such as big data and data analytics, digital applications, geospatial technologies, IoT, proximity technologies and robotics support SC relationship management. For instance, there are several systems that integrate multiple technologies such as IoT, AI algorithms, big data technologies, RFID, smart sensors, GPS and mobile applications. Proximity technologies can collect logistical information and with big data can accelerate the information flow. After that, information such as order, inventory and distribution information are stored and managed through the AI decision algorithm application.
Then, the instructions are sent to the distribution operators via wireless networks and to the localized distribution vehicles via sensors, optimizing logistics management. In order to monitor the supplied goods, an automatic alert signal can be implemented that analyses the status of the transport and sends information such as transport speed and equipment status. This system can be combined with a mobile application to improve material procurement, plan sharing, arrival processing, acceptance and performance evaluation. Finally, big data technologies can provide a model for evaluating the players in the SC (technical level, quality system, sales service). Finally, AI integrated with big data and data analytics, digital applications, immersive environments, IoT, proximity technologies and robotics enables a time reduction. By using robotic arms equipped with sensors capable of collecting signals, it is possible to predict non-compliant items. In the initial phase, there is an inspection process routine in which the sensor signals are compared with the output variables; through a machine learning approach, it will be possible to provide accurate forecasts, therefore the inspection phase will no longer be required. Moreover, the digital twin can be used in production processes, for example in the welding process. It can be used to monitor, control and visualize the welding process to ensure a more efficient process and a high-quality welding. By integrating this process with deep learning, it is possible to define a weld penetration strategy that guarantees greater quality and a time reduction.
Integration of AI with specific technologies in specific business functions and processes
Figure 5 displays the relationships about the business functions and business processes where it is possible to jointly implement AI and other cutting-edge technologies. Apart a confirmed significant role of big data and data analytics, which is the predominant category that works with AI in marketing, operations and procurement functions, it emerges the richness of relationships within the distribution function, where digital applications, geospatial tech, IoT, proximity technologies and robotics are featured by interesting values of COR (see Appendix, Table 9).
Contexts in which AI is integrated with other cutting-edge technologies
Marketing functions display an important COR for digital applications. For example, by using an approach that combines AI and consumer feedback and reviews from social media platforms, it is possible to help managers to better understand customer perspectives in real time. A further remark regards the count of AI practices for each business function, signalling that operations cover about 80% of opportunities of implementation of AI in production systems. Predictive analytics with machine learning help to predict the demand for new products in dynamic markets and to guarantee a better combination of high appeal and optimal price.
Distribution is featured by AI practices that also employ big data technology, data analytics, mobile applications, GPS, IOT, wireless sensors, proximity sensors, RFID, autonomous driverless vehicles, cobots and autonomous mobile robots. In last-mile delivery, machine learning techniques could use information generated by geospatial technologies to determine whether certain customers can be served without violating hours of service regulations to reduce the need for reprogramming other deliveries. For inventory management, the use of smart sensors integrated with neural networks allows inventory monitoring and efficient supply systems. Specifically, through the smart shelf weight sensors it is possible to monitor the quantity of items, alert the warehouse staff to refill the inventory before the item is out of stock, and notify the supplier for immediate replenishment. Using machine learning algorithms and mobile applications, it is possible to analyse traffic data from fashion industry to predict online purchases with the aim of shipping products early and minimizing delivery times. This reduces the cost of returning products that arrive late at warehouses and for early picking and packing. Other advantages are the reduction of order movements among different warehouses and the guarantee of a better product reordering, thus preserving customer satisfaction. As to warehouse management, autonomous vehicle systems can be used in production systems to transfer pallets into several units within a production plant. Therefore, traffic control, which represents the greatest challenge, can be managed by a system based on machine learning techniques, with minimal human intervention. This approach leads to a significant reduction in the transfer and waiting time of products in the warehouse compared to the traditional method. In addition, proximity technologies are mainly used to monitor and track raw parts, semi-finished and finished products. In this way, inventory can be synchronized and optimized, data and information are recorded in an inventory management software to manage order switching and picking problems. By integrating this data collection with AI techniques, it is possible to suggest the most suitable method of order picking and to improve the efficiency of warehouse operations and increase workers' job satisfaction.
Operations and procurement are affected by the same technologies, i.e. big data technology, data analytics, data mining and smart sensors. In particular, using machine learning and data analytics techniques it is possible to predict lagging suppliers using data from the manufacturer's ERP system. The only difference is the usefulness of predictive analytics for operations activities. For example, integrating a neural network with genetic optimization algorithms and predictive analytics tools can help industries to improve decision-making regarding reducing operational energy costs. With the implementation of the digital twin, manufacturing assets can be used to perform manufacturing analysis and simulation services in virtual spaces based on the data generated in physical production processes. Finally, with machine learning algorithms, rapid allocation of production activity is achieved, and efficiency is improved. Production of goods is enhanced using smart sensors and industrial robots, whereas the provision of services is linked to social media & network, GPS, IoT, smart sensors, and wearable robots. For instance, for the production of goods, the use of industrial robots and heuristic algorithms allows a reduction in the high cycle times generated by the coordination movements of robots in an assembly line. Quality management is particularly supported by the conjunct use of AI and proximity sensors. Smart sensors are also useful for operations control and maintenance and diagnostics activities. IoT platforms can monitor the status of sensors on machines and periodically record them on the private cloud system. After collecting data from sensors and automatically performing data conversions for the data pre-processing process, a fault detection analysis is performed using AI algorithms and preventive operational actions can be carried out.
An example of integration with AI by impact and business process: IoT
Considering the wide range of results concerning potential integrations between AI and the various cutting-edge technologies, it is complex to conduct in-depth investigations for each of these combinations. However, in order to demonstrate the value of combining single key AI technologies with other cutting-edge technologies and the importance of the methodology applied, the relationship between AI and IoT technologies is conducted. This section presents the analysis of the integration between AI and IoT technologies considering their combined use in specific business processes to achieve certain impacts. This specific combination is chosen because it represents two crucial technologies in production systems that enable the principles of Industry 4.0. Figures 6 and 7 present market and organizational impacts on the rows, and business processes on the columns. Specifically, Fig. 6 shows the business processes associated with operations, while Fig. 7 shows the business processes associated with marketing, distribution, and procurement. At the intersection of the two figures is shown how and how much AI technologies are combined with IoT technologies. For each combination, the number of associations is shown. It indicates the number of combinations in which specific technologies are integrated together in that specific business process to achieve a certain impact.
The combination of AI and IoT within operations function for achieving market and organizational impacts
The combination of AI and IoT within distribution, marketing and procurement functions for achieving market and organizational impacts
At a first glance, it is evident how AI is combined with IoT in all business processes of operations (Fig. 6). This indicates how AI and IoT integration is much studied and important for improving various aspects within this core business function of production systems. Considering market impacts, machine learning techniques are mainly employed with smart sensors to support product/service/quality/value differentiation. In terms of organizational impact, a combination of key AI technologies and various IoT technologies are employed to enhance efficiency and productivity, promote energy efficiency, and improve information management. In particular, scholars focused deeply on the integration of AI and IoT technologies in operations control and production of goods. However, for some impacts such as flexibility and SC relationship management, no emerging practices where AI is integrated with IoT have been identified. The potential benefits of the combination of IoT and AI in specific application contexts are still largely unexplored in depth by research.
As to distribution, marketing and procurement functions, the integration between AI and IoT technologies is less explored (Fig. 7). Specifically, not all business processes are present for each business function considered. This indicates that the exploration between AI and IoT is less investigated in these business contexts. Combining machine learning techniques with IoT and wireless sensors in warehouses leads to significant improvements in cost reduction, efficiency and productivity, flexibility, information management, SC relationship management, and time reduction. There are specific cases where AI techniques are combined with smart sensors and IoT within distributors and wholesalers, market analysis, buyer–supplier relationships, and supplier payment. In these business functions, there is potential for even greater progress in exploring the connections between different technologies. Both managers and academics should make a concerted effort to investigate these missing links. Recognizing the importance of established associations in specific business sectors is essential, as they are key to achieving the aforementioned impacts. Consequently, conducting a thorough analysis of the integration of AI and IoT has revealed even more significant implications for utilizing this approach, while also highlighting key areas where further research is needed.
Discussion
The aim of this article is twofold: firstly, to review and classify scientific articles dealing with the integration of AI with other cutting-edge-technologies in production systems, with the aim of suggesting opportunities for improvements in business performance in specific contexts; and secondly, to define directions for future research. This SLR offers interesting insights for both practitioners and academics. From an academic perspective, it classifies relevant literature on practices that employ AI with other cutting-edge technologies to improve business performance in production systems, proposing directions for future research. From a managerial perspective, it presents a classification framework delineating for which impacts and contexts the combination of AI with other technologies may be appropriate to improve standard practices in production systems.
Implications for theory and practice
This study offers useful insights in the topic area of AI integrated with other cutting-edge technologies within the production systems. From the SLR implemented by classifying the practices, it was found that about 56% of AI practices use at least another technology, confirming the importance of considering AI not as a standalone but as a highly integrated technology. The classification framework aims to support managers and practitioners in their decision-making process when adopting and combining other technologies with AI in specific business areas to achieve specific impacts. Studying the implementation of AI integrated with other cutting-edge technologies in production systems helps to understand when and where it is appropriate to combine these technologies. According to the literature review, the results suggest that future research should be directed towards this complex integration. The results emphasise the different implementation opportunities, which depend not only on the combination of technologies, but also on the business functions and processes where integration can be applied and the expected impact on business performance. For instance, big data and data analytics technologies are widely combined with AI. In particular, the linkage with big data and data analytics is predominant for each type of impact, with COR ranging from 28.81% for SC relationships management to 58.18% for the support to revenues. However, interesting values of COR have also been discovered for the IoT, digital applications and robotics. Since at least one AI practice has been discovered for each combined technology, technical compatibility is confirmed, and future developments could be directed towards less common combinations to understand their potential.
To provide further theoretical and practical input, Fig. 8 provides a summary of the most recurring links between technologies. In some cases, AI is supported by the combined technology, which provides input to its algorithms or visualisation of its outputs, while in other cases, the combined technology exploits the power of AI to “be smarter” and improve its performance and automation. For instance, machine learning algorithms could receive geographical data from geospatial technologies, while AI could provide intelligence to robots. The paper therefore suggests that future AI developments should focus on the value of integration with other technologies and the implications in terms of reengineering, reorganisation and remodelling of production systems, operations management and supply networks.
Technologies supporting AI and technologies supported by AI
The main theoretical contribution of this study is a classification framework that conceptualises the benefits of integrating AI with other cutting-edge technologies in production systems. The classification framework improves understanding of how companies could potentially integrate AI with other technologies in order to achieve specific market and organisational impacts. The analysis reveals new applications and possibilities for transforming production systems and changing operations management paradigms. Such combinations allow technologies to 'compensate' for their limitations and enhance their potential by finding new processes and activities that can be affected. Indeed, this research emphasises that organisational and market performance can be achieved by implementing AI practices that employ other cutting-edge technologies.
Furthermore, the research highlights differences in terms of implementation opportunities within specific business functions and processes. This level of detail is important in order to better focus on those combinations that have already been implemented in case studies and research and have demonstrated their ability to improve business performance in production systems. Therefore, this study suggests that managers and practitioners wishing to strengthen production systems should focus on those combinations that occur most often or those with higher COR values, as they may be considered more technically developed and, consequently, less risky to implement. The article shows relevant application of the most present combinations of AI with other technologies in specific contexts to achieve specific impacts. In particular, it reveals which other cutting-edge technologies together with AI are most stably deployed in specific processes, suggesting the definition of appropriate adoption strategies for managers and practitioners in relation to desired impacts. It emerges that AI and big data technologies are closely linked and related, and the most promising emerging practices combining AI with other cutting-edge technologies are present in a limited range of areas and for specific purposes. However, some cutting-edge technologies are emerging in association with AI, e.g. immersive environments such as the digital twin and augmented and virtual reality that enable time reduction for operations management. The integration of various cutting-edge technologies with AI is a very important initiative for SC and operations management, and although the approaches are still exploratory, the SLR-based descriptive view offers practitioners and researchers the opportunity to support their analyses with an innovative classification framework.
Gap analysis and future research
This section proposes directions for future research based on the identified research gaps. It includes the identification of three main research gaps, which could be the basis for prescriptions and a broad agenda to guide future research and theory development.
Absence of comprehensive frameworks investigating the connection of AI with other cutting-edge technologies
The study highlights a clear gap in the literature on the integration of AI with other cutting-edge technologies in the production systems. The academic literature in this area, although rich, is rather fragmented, often focusing on single business cases, applications, processes, functions and technologies. Most scientific articles analyse AI alone or compare it with a limited number of technologies in specific fields. The absence of a comprehensive classification framework that includes a comprehensive view of the integration of AI with other technologies for the production systems risks prolonging disjointed research and increasing knowledge gaps regarding the integrated perspective of technologies that underpins Industry 4.0. Most papers focus on a limited number of emerging practices, usually considering few cutting-edge technologies and specific business processes. Therefore, the article aims to open and broaden future research scenarios as it is evident how AI is highly connected to other technologies. Researchers should further investigate this gap and help practitioners formulate optimal strategies to implement and integrate AI with other cutting-edge technologies to improve business performance. Specifically, it would be useful to investigate the value of AI for other technologies or its interoperability to integrate easily with other technologies. In this study, the scientific literature becomes the source for the development of a database of emerging AI practices combined with other cutting-edge technologies to improve the performance of business functions such as operations, procurement, marketing and distribution. By building such a database, it is possible to provide a comprehensive view of all combinations of AI with the other cutting-edge technologies underpinning the Industry 4.0 paradigm. Only by considering all comparisons it is possible to understand the state of the art of the integrated effect of AI with other cutting-edge technologies. Therefore, the most important gaps found as a result of this study are as follows:
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The theoretical basis is poor on the issue of technology adoption of AI with other cutting-edge technologies; the documents analysed have a very practical orientation because they are based on specific business case studies, pilot projects and simulation models and, therefore, the theoretical background used, from a management perspective, is limited to specific cases.
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Literature analyses AI alone or at most compares it with other technologies. Since AI is highly interconnected with other technologies, there is a need for a research strand that focuses on the value of AI combined with other technologies in an Industry 4.0 perspective.
Less investigated combinations
Other implications for gap analysis are based on the fact that the business case studies, pilot projects and simulations analysed in which AI is combined with other technologies are strongly focused on specific business functions such as operations and distribution. In particular, AI is often associated with big data technologies in different contexts to achieve different impacts. This appears relatively obvious since AI often uses large quantities of data, i.e. "big data", and advanced analytics, i.e. “data analytics”. The literature analyses how adoption for production systems generate more impacts on efficiency & productivity, information management and cost reduction. These impacts are mainly achieved either by using standalone AI technology or through the combination of big data technology and IoT. Indeed, IoT captures data from external sources by storing it in a database and AI conducts prediction and data analysis to improve operational inefficiencies while reducing time. From the results, some available solutions are still under-researched, and many combinations have not gained the attention of academicians. Firstly, many combinations between AI and some cutting-edge technologies do not report any practice. For instance, emerging AI practices are weakly linked to 3D printing and open and crowd-based platforms. This research gap could be further explored in academic literature. Indeed, the link between AI and these two technologies has yet to be explored. In addition, the integration of AI with other technologies is heavily researched in some business processes and less so in others. This is mainly due to the limited implementation in specific business processes such as advertising, communication & promotion, order management—purchasing, plant services and quality management. Similarly, considering impacts, research seems to be more interested in improving organisational performance by integrating AI with other cutting-edge technologies, while market performance is less explored.
Based on this literature review, some combinations of AI with other technologies can be considered emerging and offer excellent opportunities for further research. The link between AI and other technologies may have a lower COR, but still important performance benefits. Indeed, a relatively small COR may reflect an emerging combination. For instance, blockchain and immersive environments such as augmented reality, digital twin and virtual reality are new technologies that could potentially be integrated with AI efficiently. In particular, digital twins could perform analyses and simulations in virtual spaces and generate new data in physical production processes. Potentially, AI could use that information to generate predictive models to optimise production. Finally, blockchain, being a distributed ledger containing a certain amount of data, could support machine learning activities on certified, transparent and visible data to all actors involved and automate certain operations.
In terms of single technologies, the literature review showed significant scope for further analysis on which specific technologies can support the decision-making process for managers to adopt these combinations. For example, different technologies such as social media & network, IoT, smart sensor can be implemented in distribution and operations to optimise and support logistical processes such as warehouse management, operations planning, operations control and inventory. The comprehension of specific business processes, i.e. procurement and marketing, which are important areas of the production systems, is still less researched from both a theoretical and practical point of view regarding the combination of AI with other cutting-edge technologies. Therefore, the most important gaps found as a result of this study are as follows:
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The literature on the combination of AI with other cutting-edge technologies on procurement and marketing in production systems provides limited insight into further application potential;
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Researchers mainly focus on the linkage between AI and big data technology, while the focus on other cutting-edge technologies is very limited; for instance, the literature on the link of AI with 3D printing and open and crowd-based platforms is lacking. Research should explore how these technologies could combine to provide positive impacts in the production systems;
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Based on the results, the most studied business processes are production of goods, operations control, operations planning, maintenance and diagnostics and market analysis while the other business processes do not reach the number of 100 emerging practices.
As for future directions, research could explore missing combinations in order to find new emerging practices to improve performance in production systems by integrating AI with other technologies. The literature could monitor these less studied combinations, as they could be the best candidates to become future best practices. Considering the limited time horizon covered, it would be interesting to study how CORs evolve over time. Frameworks that can outline future forecasts in terms of new and established best practices based on the integration of different technologies in order to support companies in their adoption are needed. Finally, new opportunities for research and combinations could also arise from the new technologies that will be proposed and that can be added to this model.
Perspective technology—context—impact
The proposed results provide a three-dimensional perspective for investigating the interconnection of AI with other technologies in specific contexts to achieve certain impacts. The variables business functions, business processes and impacts showed their importance in defining the contexts and impacts of emerging practices in production systems. Considering other literature reviews, many studies on Industry 4.0 refer to single technologies and not to their conjoint use, and especially few studies specifically define the specific impacts and contexts in which these technologies are implemented. As the results show, different processes within the same business functions could benefit differently from the combined use of AI with other cutting-edge technologies. At the same time, few researchers have paid attention to the impact derived from the combined use of technologies. Therefore, the characterisation of the specific impact allows moving appropriately from theory to practice and provides managers with potential scenarios for the adoption of technologies the production systems. The relevance of context and business impact variables suggests that scholars should consider these multidimensional factors in order to evaluate the integration of AI with other cutting-edge technologies. Exploring the combination of AI with other technologies in specific contexts in order to achieve certain impacts in future studies can help extend the theory of interconnectedness proclaimed by Industry 4.0 principles. It is evident how the same technologies are classified in different categories, confirming the complexity of achieving a shared taxonomy among researchers. Therefore, the most important gaps found as a result of this study are the following:
-
Absence of taxonomies and comprehensive view of the combined use of AI with other technologies in specific contexts;
-
Absence of taxonomies and comprehensive view of the combined use of AI with other technologies to achieve specific impacts.
Future developments could include other models for analysing the integrated use of technologies in specific contexts and for specific impacts. It would be useful to investigate what contingencies and synergies could be related to the combined adoption of AI with other technologies.
Conclusion
This work presents the results on the complementarity of AI with other cutting-edge technologies for production systems. In particular, AI practices have been selected and their combination with other technologies has been studied, focusing on the differences in terms of impact on business performance, business functions and processes where they can be implemented. Calculating the COR of each combined technology, the most significant technologies that can be integrated with AI have been presented, showing real examples of main relationships. Considering that AI is highly connected with other technologies, the research provides an overview of the state of the art of emerging practices that can be implemented in production systems and SCs to provide business performance enhancement.
However, some limitations emerge. First, to simplify the analysis, only the combination between AI and one cutting-edge technology at one time is considered. Therefore, the synergic effect of multiple technologies has not been detected, even though it is confined to a limited number of cases. The research group plans to address this issue in future research, when additional practices will be collected and the number of such cases will grow. Second, practices were collected from scientific literature, hence it is impossible to affirm that all existing technology combinations have been considered: researchers may have not studied yet some existing opportunities of integration. As a consequence, future research will be addressed to track changes in combinations and relationships among variables over time and study the evolution of emerging technologies in production systems and SCs. Considering a broader research time horizon, the COR could identify emerging links of AI key technologies with other cutting-edge technologies that will be established in the future.
Data availability
The data that support the findings of this study are available from the corresponding author, upon reasonable request.
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Varriale, V., Cammarano, A., Michelino, F. et al. Critical analysis of the impact of artificial intelligence integration with cutting-edge technologies for production systems. J Intell Manuf (2023). https://doi.org/10.1007/s10845-023-02244-8
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DOI: https://doi.org/10.1007/s10845-023-02244-8