Development of a Smart Manufacturing Execution System Architecture for SMEs: A Czech Case Study

sustainability Article Development of a Smart Manufacturing Execution System Architecture for SMEs: A Czech Case Study Marcel Rolf Pfeifer   Citation: Pfeifer, M.R. Development of a Smart Manufacturing Execution System Architecture for SMEs: A Czech Case Study. Sustainability 2021, 13, 10181. https://doi.org/10.3390/ su131810181 Academic Editors: Daryl Powell, David Romero and Paolo Gaiardelli Received: 9 August 2021 Accepted: 7 September 2021 Published: 12 September 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2021 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Department of Management, Faculty of Business and Management, Brno University of Technology, 612 00 Brno, Czech Republic; [email protected] Abstract: This study investigates the application of a smart manufacturing execution system (SMES) based on the current controlling structure in a medium-sized company in the Czech Republic. Based on existing approaches on the architecture of SMESs, this paper develops a sample architecture grounded in the current controlling structure of small and medium-sized enterprises (SMEs). While only a few papers on approaches to the given topic exist, this approach makes use of operative production controlling data and uses a standardisation module to provide standardised data. The sample architecture was validated with a case study on a Czech SME. This case study was conducted on two different entities of one production company suggesting differences in the entities due to the nature of production. The research showed that simple tasks with intelligent welding equipment allow for a working SMES architecture, while complex assembly works with a high extent of human labour, and a high number of components still remain an obstacle. This research contributes to gathering more understanding of SMES architectures in SMEs by making use of a standardisation module. Keywords: production controlling; Industry 4.0; OPC 4.0; machine learning; computer-aided standardisation; smart manufacturing; smart manufacturing execution system; sustainability 1. Introduction Industry 4.0 and the Internet of things (IoT) have been popular and widely discussed topics in recent years. Expecting substantial changes to happen to companies of all kinds in the upcoming years [ 1 , 2 ], these trends will have a persistent effect on companies, the way of work, and society as a whole [ 3 ]. Since the introduction of the term in 2011 [ 4 ], various research has been conducted on Industry 4.0 [ 5 , 6 ], as its introduction will have an impact on management and operations in companies. Industry 4.0 is described as a new paradigm [ 7 ] of changes in organization and technical aspects throughout the value chain leading to new business models [ 8 ], whereas IoT describes technologies for network-connected machines and devices that are expected to enable companies to introduce Industry 4.0 principles [ 9 ]. IoT enables the horizontal and vertical integration of tasks, information systems and their data, and decision making, leading to higher requirements for the supporting systems [ 10 ]. The application of new, smart technologies based on the IoT [ 11 ] allows companies to make use of data in a far wider range than before [ 12 ]. To introduce Industry 4.0, further approaches, technologies, and methodologies are discussed, such as artificial intelligence (AI) [ 13 ], multi-agent systems (MAS) [ 14 , 15 ], cloud-based manufacturing [ 16 , 17 ], and blockchain technologies [18]. Using data through interconnected IoT services and devices provides companies with the opportunity to further automate and to facilitate communication within the IT networks [ 19 ]. These IT networks belong to the internal company network, as well as to supply chains where data have to be exchanged with suppliers and customers [ 20 ]. Assuming the potential of computers and devices in the future, in 2004, White already expected information systems to be able to provide and exchange data in almost real time (referred to as “right time”) [ 21 ]. Interconnected IT networks exchanging data form a Sustainability 2021,13, 10181. https://doi.org/10.3390/su131810181 https://www.mdpi.com/journal/sustainability Sustainability 2021,13, 10181 2 of 23 cyber-physical network (CPN) [ 22 , 23 ]. Based on the integration of heterogeneous multisource data and the integration of knowledge into production processes, CPNs allow for integrated and interoperable manufacturing processes [20]. Allowing for an interoperable manufacturing process, Industry 4.0 may also be understood as an intelligent production flow from machine to machine based on data [ 24 ]. Equipping manufacturing with IoT devices leads to data-driven smart manufacturing capable of adapting fast to changes and triggers [ 25 ]. According to Kusiak, the core of smart manufacturing is material handling, including logistics and supply chain management (SCM), being integrated into the operations of the company [ 26 ]. A further embedding of smart manufacturing into a whole company network working on smart principles results in the concept of a smart factory [ 27 ]. Smart factories are based on the use of the most recent information technologies in order to provide further integration of company processes [11,28]. Expecting a purely data-driven factory requires the collection, storing, and distribution of data [ 29 ] from sensors and devices paired with a boost in data analysis and the development of predictive engineering [ 26 ]. According to [ 30 ], this calls for smart production control, being able to monitor and assess flows and requirements. Furthermore, smart production control has to be able to make decisions [ 31 ]. The issue of requiring the most exact scheduling and controlling of smart production for decision-making processes persists up to today [ 32 ]. Smart production control and management is one of the central topics of smart factories, as realization is progressing slowly [ 33 ]. Treating the data as a digital twin of smart manufacturing devices, products, and components, the physical circumstances are digitally resembled to facilitate distribution, processing, and assessment of data [34]. While ideas have been produced in the last decades, the fundamental issues of smart production control have not yet been solved [ 33 , 35 ]. Moreover, small and medium-sized enterprises (SMEs) struggle with lacking technology, knowledge, and finances to support their transition towards Industry 4.0. Existing theoretical frameworks lack practical applications due to the missing technical means [ 32 ]. While smart devices are evolving and approaches are created [ 36 ], this paper looks at how SMEs might set up their production control and management for the transition that has begun towards Industry 4.0 [ 37 ] while making use of existing systems. While LEs are assumed to use technology, knowledge, and resources to invest in new technologies, such as IoT, SMEs are believed to require other downsized frameworks with regard to their resource situation and with regard to their capabilities [ 38 ]. It is the aim of this paper to propose a framework based on today’s existing components, taking into account the principles of Industry 4.0. This is done through qualitative research as a multiple case study on two production sites of one single Czech production company. 2. Materials and Methods 2.1. Literature Review 2.1.1. Smart Manufacturing Smart manufacturing assumes the principles of manufacturing under Industry 4.0. Developed as the manufacturing framework within a smart factory (also a digital factory, digital manufacturing, a smart factory, an interconnected factory, integrated industry, or Industry 4.0 [39] ), it represents an alternative framework to multi-agent systems (MAS) [14,15] and cloud-based manufacturing [16,17]. Smart factories and smart manufacturing rely on the combination of smart objects and big data analytics [ 15 ]. Big data includes technologies and analytical approaches for extracting value from information through a transformation being characterised by high volume, velocity, and variety [ 40 ]. The concept of big data provides the potential to collect, process, and distribute a vast amount of data. Industrial big data analysis makes use of these data for diagnostics, optimization, and reconfiguration of the whole system [ 41 ]. Data for big data technology may be collected from the CPN within the company or from Sustainability 2021,13, 10181 3 of 23 external online resources [ 42 ]. Big data is therefore seen as an important component for data-driven manufacturing approaches, such as smart factories, to achieve higher effectivity and productivity [43]. Smart manufacturing should provide cost-effective, sustainable, and safe manufacturing. In these measures, it is estimated to be far more capable than usual manufacturing processes [ 44 ]. Industry 4.0 gives a boost to computer-integrated manufacturing (CIM), allowing for a more decentralized architecture based on CPN [ 45 ]. IoT allows for integrating devices and equipment into the company’s information system infrastructure [ 46 ]. While Industry 4.0 is based on M2M communication [ 47 ], CIM was initially developed with a focus on human employees [ 48 ]. This includes self-organized diagnostics and repair requests communicated to machine and equipment suppliers and allowing for smart and intelligent predictive maintenance (SIPM) [ 49 ]. Components within the Industry 4.0-framework act as autonomous agents [ 44 ]. The transition from usual manufacturing towards smart manufacturing usually passes through the stages of connected (computerization and connectivity), transparent (visibility and transparency), and intelligent (predictive capacity and adaptability) [50]. Rising manufacturing complexity requires information-based technologies working in real time [ 51 ]. Concern not only focuses on one department but has to be raised with regard to the whole supply chain and all further processes in the company. Combined with smart logistics that focus on managing and controlling supply chains [ 52 ], this leads to smart manufacturing supply chains (SMSCs). SMSCs determine and coordinate production and transportation features, such as quantities and timing, based on real-time data [ 53 ]. To resemble the actual state of products and devices, the system applies digital twins [ 54 ]. Relying on M2M communication makes data quality and data quantity critical factors for the implementation of smart manufacturing [ 55 ]. Big data technologies act as a feeding technology for data-driven analytics in smart manufacturing [56]. The features of big data technologies can be characterized by the features volume, velocity, variety, veracity (data quality), and value [ 57 ]. While some sources name only volume, variety, and velocity [ 58 ], other sources use value, veracity, and visualization [ 59 ]. Anticipating a higher data quantity (volume) and a higher resolution of data (veracity) requires further development in big data technologies [ 60 ]. In order to be able to apply big data for manufacturing purposes, data have to be transmitted with a subject-related context for correct interpretation [ 55 ]. A study from Günther et al. showed that a continuous restructuring and realignment of processes, data handling, and big data is also required in smart environments [ 61 ]. However, today’s manufacturing still relies on independent systems connected through various physical and data interfaces [60]. Smart manufacturing reference architectures have been proposed by various authors. Papazoglou et al. developed a reference architecture for automotive industries with a specific automotive sector extension [ 62 ]. As manufacturing knowledge is processbound and product-related, today’s settings lack the interrelation of special manufacturing knowledge [ 63 ]. A pre-determined interface and query language should respect these interrelations during the retrieval, processing, and distribution of data, information, and knowledge [ 62 ]. Another approach is the smart manufacturing systems (SMS) architecture developed for service environments, where components, such as enterprise resource planning (ERP) and supply chain management, are understood as services. Interactions between smart manufacturing and other parts of the company or the supply chain are handled through a business intelligence (BI) tool [ 64 ]. For separated company-internal activities, another approach is the integrated CAS system based on standardised data [ 65 ]. Other approaches are represented by the smart manufacturing execution system (SMES), focusing on the elimination of machine-to-human and human-to-machine interfaces to retain the existing manufacturing execution system (MES) resources [ 66 ]. The MES acts as a centre point for data collection in order to keep the existing structures of the company intact (see Figure 1). A message broker element ensures both-sided communication between the components [ 67 ]. A further difference between MESs and SMESs is Sustainability 2021,13, 10181 4 of 23 that MESs focus mostly on management support, while SMESs work in a broader range in supply chains [ 68 ]. A service-based SMES approach has been proposed for SMEs using an Android-based interface in order to reduce the widely existing paperwork in these companies [69]. Figure 1. Sample SMES architecture (adapted from [67]). 2.1.2. Smart Production Control Handling a vast amount of data in smart factories and SMESs requires a control system. It is assumed that previously perceived issues, such as the dilemma of the job shop scheduling problem (JSP), may be solved with the help of new technologies and MAS [ 70 ]. Due to the various influences of factors, the JSP is impervious [ 71 ]. MAS approaches are able to trigger production and control in a decentralized way, where each agent acts autonomously [ 72 ]. Research has been conducted with MAS systems using MES and ERP systems as data hubs [ 73 ]. This implies that agents may be found on various levels, such as order fulfilment agents, product agents, machine agents, supervisor agents, coordinator agents, and AI agents [74]. The reference service-oriented architecture (SOA) proposed by Papazoglou et al. places the production scheduling into its centrepoint, as the production schedule contains information on all crucial variables for the company network [ 62 ]. All approaches have in common that they work based on real-time data gathered through sensors from the CPN [ 75 ]. Auto-identification (Auto-ID) and radio frequency identification (RFID) are expected to allow for complete in-process tracking [ 76 ]. The application of these sensors can be applied within the company, as well as in supply chains [50]. While data and prediction models are missing for many situations, AI is applied through machine learning. Research conducted with machine learning approaches for planning and control enabled companies to predict disruptions in supply chains [ 77 ]. Deep learning approaches have been found to learn 57 Atari games without prior knowledge of the games [ 78 ]. Even though the best results were achieved in model-free environments, real-time scheduling was tested with a reinforcement learning approach [ 79 ]. Research studies have been conducted in logistics [80] and in the chemical industry [81]. However, these approaches relied on full information with model-free learning with uncertainty [ 82 ]. Management decision support was explored in model-free case studies in investment applications [ 83 ]. Due to the complexity of its learning, the application is limited to laboratory case studies [ 84 ]. Case studies applying machine learning conducted on the smart production planning and control (smart PPC) model of Oluyisola et al. showed ERP systems to be the centre of smart manufacturing [ 50 ], matching with findings from previous research [85]. Sustainability 2021,13, 10181 5 of 23 Chinese research proposed a self-adaptive collaborative control (SCC) mechanism as part of a smart production logistics system [ 86 ]. This integrated approach of production and logistics assesses the acquired data with data from the knowledge base in order to retrieve deviations and learning triggers [ 87 ]. The control model is applied vertically through the three layers of the smart production, while monitoring is set horizontally. Manufacturing process information is processed by the collaborative control [86]. While large companies tend towards a smart process design, smaller companies may focus more on smart products [ 50 ]. An issue for the transition towards smart manufacturing is the existing implication of human resources, which will slowly progress through furtheradvanced companies [ 88 , 89 ]. Hence, it is questionable whether smart production control approaches are feasible for SMEs. 2.2. Small and Medium-Sized Enterprises (SMEs) According to the European Commission, small and medium-sized enterprises are defined as companies with a maximum of 250 employees and with a maximum revenue of EUR 50 million. Above these numbers, companies are considered to be large enterprises [ 90 ]. While small in number of employees, SMEs account for the vast majority of companies globally in various economies. In Germany, SMEs represent more than 90% of registered companies [ 91 ], which is in line with the percentage for the whole European Union. Furthermore, this matches with the structure of Asian economies [ 92 ]. Due to the number of enterprises, SMEs employ 60% of all employees in Germany [91]. SMEs are known to face several constraints in human, financial, and technical resources [ 93 ]. Due to financial limitations, a South Korean study comes to the conclusion that financial government incentives will help to defeat the lack of finances, as this hinders SMEs in their innovation and development of forces [ 94 ], and SMEs seem unable to generate the required financial funds themselves and struggle in acquiring funds from banks [ 95 ]. Besides the mentioned constraints, the Organisation for Economic Co-operation and Development (OECD) identified missing managerial capabilities and low productivity as reasons for lacking competitive ability [ 96 ]. SMEs therefore seem unable to leverage their smaller size and lower transaction cost to gain competitive advantages [94]. With regard to digital factories and smart manufacturing, research also identified constraints for SMEs in IT [ 97 ]. With the integration of various IT systems, IT security is also coming into focus [ 98 ], being added into supply chain risk management (SCRM) [ 99 ]. Even though LEs are usually well equipped in IT security, SMEs in supply chains represent a threat [ 100 ] by opening a backdoor for intruders and malware searching for the knowledge of LEs [ 101 ]. Although IT security is considered to have a deciding role for enterprises in the future [ 102 ], SMEs seem to hesitate to invest in IT security. This might also explain why SMEs in the logistics industry were found to lack skills in IT competence [ 52 ]. A German study found that while LEs are looking for long-term strategies to secure benefits when implementing production planning and control, SMEs are focusing on short-term benefits [103]. While SMEs are struggling with their constraints, research approaches have tried to develop downsized small-scale strategical frameworks, adapted to the reality of these companies [ 38 ]. While Mittal et al. found 15 articles on smart manufacturing paradigms in SMEs [ 93 ], these approaches lacked taking the reality of SMEs into account. As a result, SMEs do not feel fit for adopting smart manufacturing [ 104 ], and managers and owners do not see the benefit for their companies [ 105 ]. Hence, Mittal et al.’s own approach is the smart manufacturing adoption framework (Figure 2), based on five stages: (i) identifying already available manufacturing data in the SME, (ii) assessing readiness of the SME, (iii) winning over SME management and staff for smart manufacturing, (iv) developing an individual smart manufacturing vision, and (v) identifying tools and practices needed for realization [ 106 ]. SMEs need to develop their own individual tool kit in order to be able to conquer their individual challenges [ 93 ]. However, it seems that SMEs lack an understanding of the importance of data [107]. Sustainability 2021,13, 10181 6 of 23 Figure 2. Smart manufacturing adoption framework for SMEs (adapted from [106]). Publications listed different stages of maturity for smart manufacturing in SMEs. A five-stage model was proposed by Qin et al. (1. single-station automated cells, 2. automated assembly system, 3. flexible manufacturing system, 4. computer-integrated manufacturing (CIM) system, and 5. reconfigurable manufacturing system) [ 108 ] and Mittal et al. [ 109 ] to assess the development of SMEs towards smart manufacturing. Schumacher et al. instead proposed a maturity index [ 105 ]. Another approach suitable for SMEs proposed a three-stage model (1. initial, 2. managed, and 3. defined) [ 110 ]. Further approaches have applied up to nine stages of maturity but lack applicability with regard to SMEs [ 109 ]. In order to pay attention to SMEs’ realities, Weyer et al. suggest adopting a standardized and modular approach to implement only the required components tailor-made for the given company [22]. As SMEs will have to deal with Industry 4.0 and smart manufacturing in the future to stay competitive in the market [ 111 ], this paper attempts to develop a downsized and small-scale SMES framework for SME manufacturing companies. This SMES framework will be verified by a case study on a Czech production company. 2.3. Development of the Standardized SMES Framework According to previous research, the requirements of a particular SME in smart manufacturing may be identified by the smart manufacturing adoption framework for SMEs proposed by Mittal et al. This framework assesses the readiness and requirements of SMEs in five steps: (i) identifying already available manufacturing data in the SME, (ii) assessing readiness of the SME, (iii) winning over SME management and staff for smart manufacturing, (iv) developing an individual smart manufacturing vision, and (v) identifying tools and practices needed for realization [106,109]. Step 1: Identifying already available manufacturing data in the SME. Smart manufacturing attempts to gather data of a product from all phases of production in order to improve manufacturing processes and products [ 56 ]. While smart manufacturing works on an in-depth analysis of the acquired data [ 112 ], the results are used for company decision-making processes. Furthermore, data from manufacturing may be used not only within the company but also at the interface of the company with other entities in supply chains [ 25 ]. In order to build a system for the data provided by the particular SME, the individual reality, ability, and needs of the SME have to be taken into account. Small-sized companies were found to primarily store their data on local PCs rather than in systems, while in medium-sized companies, a trend towards centralization was observed [ 113 ]. These data may be related to the organizational dimensions of (a) finance, (b) people, (c) strategy, (d) process, and (e) product [106]. Step 2: Assessment of readiness of the SME. According to an Irish study, the format of the gathered data tends to be stable over time and does not depend on the age of the company [ 113 ]. With SMEs being known for having individual financial, human, and technical resource constraints [93,96] , the level of readiness of the company should be assessed before action [ 109 ]. The level of readiness in Industry 4.0 may be assessed by various maturity models and indices (Lin, Wang, and Sheng, 2019a). For SMEs, the assessment of maturity level may be done by the Singapore smart industry readiness index, initially assessing 16 dimensions in the 3 dimensions of process, technology, Sustainability 2021,13, 10181 7 of 23 and organisation [ 114 ]. Mittal et al. extended the application to the mentioned five dimensions [106]. Step 3: Winning over SME management and staff for smart manufacturing. Human resource constraints have been identified as a characteristics of SMEs [ 93 ]. This also includes the management skills of these enterprises [ 96 ] that have a crucial impact on their sustainability and long-term performance [ 115 ]. Future job profiles are believed to differ widely in their requirements from what workers have to provide today. It is on the managers to already be involved during the designstage of processes, which has shown to have a positive outcome on the long-term development of the SME, creating a clear job profile for human resources [ 116 ]. The transition towards smart manufacturing therefore requires the involvement of SME management and the adoption of a new corporate culture [ 117 ] striving for overcoming human resource constraints. Step 4: Developing an individual smart manufacturing vision. Due to the realities of SMEs, researchers have come to the conclusion that SMEs are working in a small-scale and downsized environment [ 38 ]. Due to the broad bandwidth of these companies and their specialization, the approaches in smart manufacturing should also be tailor-made around a standardized core [ 22 ]. Being closely related to the companies’ strategic setup, the aim of this step is to boost the level of data from a mere data acquisition to a data distribution, allowing for data-based decision making [ 106 ]. Industry 4.0 and smart factories as an approach are striving for making use of a broad base of data gathered, processed, and distributed in order to allow for fast decision making [118]. Step 5: Identifying tools and practices needed for realization. As with the previous steps, the identification of appropriate tools for the realization of a smart manufacturing and SMES approach is also tailor-made for SMEs according to the given company. A toolkit for smart manufacturing was developed by Kaartinen et al. in [ 119 ] and has been adapted for the reality of SMEs by applying a toolkit for transition (Table 1) [ 93 ]. The developed toolboxes in the toolkit provide an overview of maturity levels, usually using five maturity levels to characterize the transition status of the SME towards smart manufacturing. Some researchers propose level 0 as the starting point for companies towards smart manufacturing, representing a fully analogue company. The jump from level 0 to level 1 is considered the hardest to overcome for these SMEs [ 103 ]. Concerning the step of data processing in the process, the toolbox may be characterized according to Mittal et al., 2019 (Table 1). Table 1. Smart manufacturing toolboxes corresponding to the data hierarchy steps (adapted from [106]). Data Hierarchy Steps Smart Manufacturing Toolboxes Data Generation Data Transmission Data Storage Data Analysis Fabrication/Manufacturing toolbox (FMT) YES Design and simulation toolbox (DST) YES YES YES YES Robotics and automation toolbox (RAT) YES YES YES YES Sensors and connectivity toolbox (SCT) YES Cloud/Storage toolbox (CST) YES YES Data analytics toolbox (DAT) YES Business management tools (BMT) YES YES YES YES 3. Methodology The research methodology can be found in Figure 3. The research will be carried out in three steps according to Figure 3by (a) developing an SMES model architecture for SMEs, (b) carrying out a case study with the proposed model architecture, and (c) applying the results from the case study to adapt the proposed model. Sustainability 2021,13, 10181 8 of 23 Figure 3. Research methodology. 3.1. Development of SMES Model Framework SMES models try to make use of ERP systems and the existing components of the information system architecture. The information system architecture components are linked through M2M communication. Industry 4.0 and smart concepts are assumed to depend on the quality and quantity of data in the company [ 55 ]. While Industry 4.0 approaches suggest big data to deal with a higher amount of data in a higher resolution, data today are still retrieved from various interdependent systems [ 60 ]. As companies make use of various systems today, high-level data for management decisions may be found in the ERP and MES system [ 73 ], while operative data may be handled in lower-level information systems for operative controlling. In production companies, this refers to the production organization and planning and requires production indicators for input (resource usage) and output (productivity, quality) [120]. Operative controlling is a discipline of controlling that may go into the daily ongoing operations. It has the task of supporting the operative management decisions. These decisions are done quickly with a limited range [ 121 ]. The production controlling does not go beyond the production. It is part of the logistic controlling and has interfaces with the procurement controlling and the sales controlling [ 122 ]. According to Oluyisola et al., SMEs may be able to focus on smart products to monitor production [ 50 ]. Smart products may be equipped with auto-ID or RFID sensors [76]. The required technologies for an operative production controlling architecture were defined by Heimel and Müller [ 123 ]. The system requires big data technology, as there are data warehouses or BIs. These systems should be able to provide planned and required data on request within almost real time [ 124 ]. Another way to provide these data is the usage of standardisation technologies, such as computer-aided standardisation (CAS). In order to unify and in order to simplify processes on the operative level, the CAS may take over the role of a data warehouse for a specific line. In this case, all required information on standardized technological steps, times, consumptions, etc., is available for the controlling system [125]. Operative production controlling relies on actual values from production. To a certain extent, today, such processes also already exist in production companies. The difference in Industry 4.0 is the quantity of data that has to be retrieved and processed, while SMEs should be able to work with a downsized architecture and downsized data management. While LEs might think about decentralized approaches with immediate correction through agents, SMEs may struggle to make use of the generated data. The whole system works on an actual basis, providing all functions of operative production controlling. As these data are directly consumed for the operative production management, the circle is closed, providing data and feedback. Sustainability 2021,13, 10181 9 of 23 Making use of the controlling structure of a company, the operative production controlling will be able to process and assess the status of production. The information of planned parameter values may be fed from a standardisation module or system, such as the integrated CAS system [ 65 ]. It may contain standardized and thus planned data on any process, such as on time parameters and consumptions and further requirements, including drawings and tool information. By integrating the CAS system into the controlling structure (see Figure 4), the author derived a controlling architecture and named it Operative Production Controlling 4.0 (OPC 4.0). This controlling structure was assumed to work in a particular production line, being fed by big data or CAS and by the APS. The big data or CAS contains the database of existing standards and planned values with which production data are assessed in right time. Figure 4. General OPC 4.0 architecture (own processing). Integrating the OPC 4.0 architecture into SMES allows for the usage of standardized data within the whole framework. According to other SMES frameworks, this framework also makes use of the MES system in its centre. The major difference with regard to proposed SMES frameworks is the additional standardisation module with standardised data feeding the MES and indirectly feeding the advanced planning module. Instead of big data technologies, it makes use of standardised company internal data. The required components already exist. A major difference in this is the elimination of human–machine interfaces for data acquisition, processing, and distribution. The whole architecture consists of M2M interfaces only, allowing for faster retrieval, processing, and full control. OPC 4.0 works in a closed system within the production line. This system requires several interfaces with other systems and subsystems. These systems include BI, CAS, advanced planning systems (APS), and ERP systems, which are able to provide required data for processing. Depending on the concrete application, these systems may differ. However, the basic logic of the system remains the same. Such an architecture relying on CAS, APS, and ERP systems is shown in (Figure 5). As OPC 4.0 is only the operative part of the controlling structure, higher-level controlling functions have to be covered additionally for LEs. Heimel and Müller assume the big data technologies to be a vital part of the strategic controlling [ 123 ]. Big data has the ability to consider not only internal standardized information but also information from external sources. Technologies such as text mining retrieve their data from any text found providing information, e.g., for marketing purposes that are required to be timely [ 126 ]. However, for an SME, the downsized framework for SMEs will work with a CAS instead of a BI and further big data technologies due to its limited complexity. The CAS contains Sustainability 2021,13, 10181 16 of 23 Figure 6. Case study architecture, including OPC 4.0 module. At the moment, the analysed SME does not have a viable database and needs to focus on vaster data-acquisition activity for a further implementation of an SMES approach with an OPC 4.0 framework. While the underlying basics of the OPC 4.0 architecture were implemented, the downsizing of the architecture implemented CAS and OPC 4.0 as modules into the ERP system. The ERP system itself remained the core of the architecture. The basic thought of a direct feedback and controlling loop was only implemented for the welding equipment, as it was an internal default function of the equipment that could be activated. 3.4. Results and Discussion The architecture and components for OPC 4.0 already exist today. The investment into a fully autonomous production line that is self-learning and self-correcting is costintensive. SMEs, with their financial constraints, are usually not able to invest into such an environment. This is why ERP-based SMES approaches were invented. The presented approach is based on the existing controlling structure of the SME. According to Abée et al., Controlling 4.0 is a process of trial-and-error where immediate positive results should not be expected [140]. Results show that equipment able to provide viable data on a manufacturing process may boost quality and reduce manufacturing and logistic cost by an early detection of errors. With a working APS, the system can adapt quickly to the errors and to the new circumstances. While research on the applicability of smart manufacturing approaches in SMEs is rare, the authors realized the importance of separating approaches for LEs and SMEs, also requiring an individual toolkit [ 93 ]. SMES approaches have shown the potential to work in SME environments [ 106 ]. Other approaches in the literature have only come to a simulation stage or to a case study of a prototype manufacturing environment [67]. According to the findings of previous research, SMEs are focusing on fast benefits and returns on investment [ 103 ]. In addition, the managers of the herein-mentioned case study company seemed to have a different understanding of the needs than their experts in production. While the initial framework was developed based on managerial input, the second, improved framework allowed for expert input in order to eliminate the shortcoming of the first version. The ex-post evaluation found a certain management myopia by focusing mostly on finances and reporting. Long-term strategies, such as the transition of a company towards smart principles, can therefore only be met with downsized frameworks [ 38 ]. In order to stay on the market, SMEs will also have to deal with smart approaches and principles [ 111 ]. While the proposed approach does not meet all principles yet, it fit the actual reality of the company in the case study. Approaches on MAS are far-fetched for SMEs, and the JSP is an apparent conflict in SMEs. As in previous Sustainability 2021,13, 10181 17 of 23 research studies [ 50 , 85 ], the ERP with the included MES may be assumed to play a crucial role as the centre of the SMES architecture. The proposed approach was built on the company’s current controlling structure. While research was done on smart production control in the past, the human interaction was seen as a conflicting element that should be eliminated [ 88 , 89 ]. SMEs show a currently high extent of human participation in processes, such as in the assembly area in the case study. In the case study company, this could not be overcome easily by smart products, as Oluyisola et al. suggested [ 50 ], due to the high number of components contributing to one assembly step. The complexity of these steps combined with manual work provides obstacles for an introduction of smart principles. Less complex assembly steps, such as the welding process, show that the fundamentals may be implemented and work in SME assembly as well. 4. Conclusions This research paper attempts to propose a smart manufacturing architecture for SMEs. There are only a few papers dealing with this topic to date. Building on previous SMES approaches that put the MES system at the centre of the architecture, the proposed architecture showed its feasibility for SMEs with the ERP including the MES. However, it also showed shortcomings in cases of increased complexity, standing in conflict with the persistent financial constraints that SMEs have to deal with. As components such as the ERP, MES, and APS are available and also used in SMEs today, the financial burden may not be as high as a completely new investment. Making use of already existing or easily retrievable data within the company should be secured by a system or module that substitutes a BI. These data might be stored in a CAS or in a similar system. The case study suggests that obstacles may be found in activities with a high degree of manual work and human implication. While the provided OPC 4.0-based SMES architecture is the framework outcome in this case study done in one SME with two production entities, the framework should be further validated and researched in other SMEs. As there are only a few papers available on this topic, also usually in the form of case studies on production companies, e.g., [ 106 , 134 , 135 ], the proposed SMES frameworks have only been applied and scientifically monitored in a small number of cases. Qualitative research through case studies goes into detail more than striving for generalizability based on quantitative data [ 128 , 129 ]. However, the case study shows that SMEs have issues in the full monitoring and tracking of production in the assembly area, due to its increased complexity and human work. An area for future research is the applicability of the OPC 4.0-based SMES architecture to different production areas and to different industries. While the OPC 4.0-based SMES architecture is assumed to be suitable for the production area, it is a question of whether there will be further application in other areas and industries. Future research should further investigate whether the proposed approach in particular and SMES frameworks in general are suitable for all subcategories of SMEs. These three subcategories according to the European Commission (micro, small, medium-sized) [ 90 ] might show differences in their behaviour, in their organization, in their complexity, and in their way of approaching SMESs. While assuming the further development of cyberphysical network technology and the IoT in the coming years, the exact outlines of the architecture in SMEs and LEs have to be further monitored in practice. It is a question of whether the described OPC 4.0-based SMES architecture would also be able to suite LEs. It remains to be seen in future research and development whether SMEs will be able to survive with SMES architectures by avoiding cyber-physical networks and more sophisticated IoT technologies. While Kotler assumes SMEs to have a wider range of opportunities in Industry 4.0 by eliminating their disadvantages in comparison with LEs [ 141 ], current established SMEs are assumed to take small steps. Research suggests that companies will have to move to new technologies to stay competitive on the market. However, it remains unclear how SMEs will conquer this challenge. The presented approach in this paper Sustainability 2021,13, 10181 18 of 23 may provide an SME architecture for a transition towards smart manufacturing based on production controlling. However, it is questionable whether this approach will secure long-term competitive abilities and how SMEs will climb the next maturity levels towards smart manufacturing. Funding: This research received no external funding. Institutional Review Board Statement: Ethical review and approval were waived for this study due to the complete anonymity of the study. Informed Consent Statement: Patient consent was waived due to the complete anonymity of the presented data. Data Availability Statement: The data presented in this study are available in the Results section. Conflicts of Interest: The author declares no conflict of interest. Abbreviations The following abbreviations are used in this manuscript: AI Artificial intelligence APS Advanced planning and scheduling Auto-ID Auto-identification BI Business intelligence BMT Business management tools CAS Computer-aided standardisation CIM Computer-integrated manufacturing CNC Computerized numerical control CPN Cyber-physical network CST Cloud/Storage toolbox DAT Data analytics toolbox DST Design and simulation toolbox ERP Enterprise resource planning FMT Fabrication/Manufacturing toolbox IoT Internet of things IT Information technology JSP Job shop scheduling problem KPI Key performance indicator LE Large enterprises M2M Machine-to-machine MAS Multi-agent system MES Manufacturing execution system OECD Organisation for Economic Co-operation and Development OPC Operational production controlling RAT Robotics and automation toolbox RFID Radio frequency identification SCC Self-adaptive collaborative control SCM Supply chain management SCRM Supply chain risk management SCT Sensors and connectivity toolbox SIPM Smart and intelligent predictive maintenance smart PPC Smart production planning and control SME Small and medium-sized enterprises SMES Smart manufacturing execution system SMS Smart manufacturing systems SMSC Smart manufacturing supply chains SOA Service-oriented architecture Sustainability 2021,13, 10181 19 of 23 References 1. Feshina, S.S.; Konovalova, O.V.; Sinyavsky, N.G. Industry 4.0—Transition to New Economic Reality. In Industry 4.0: Industrial Revolution of the 21st Century; Springer International Publishing: Cham, Germany, 2018; pp. 111–120. [CrossRef] 2. Popkova, E.G.; Ragulina, Y.V.; Bogoviz, A.V. Fundamental Differences of Transition to Industry 4.0 from Previous Industrial Revolutions. In Industry 4.0: Industrial Revolution of the 21st Century; Springer International Publishing: Cham, Germany, 2018; pp. 21–29. [CrossRef] 3. Ghobakhloo, M. Industry 4.0, digitization, and opportunities for sustainability. J. Clean. Prod. 2020,252, 119869. [CrossRef] 4. Kagermann, H.; Lukas, W.D.; Wahlster, W. Industrie 4.0: Mit dem Internet der Dinge auf dem Weg zur 4. industriellen Revolution. VDI Nachrichten 2011,13, 3–4. 5. Ghobakhloo, M.; Fathi, M. Corporate survival in Industry 4.0 era: The enabling role of lean-digitized manufacturing. J. Manuf. Technol. Manag. 2019,31, 1–30. [CrossRef] 6. Nascimento, D.L.M.; Alencastro, V.; Quelhas, O.L.G.; Caiado, R.G.G.; Garza-Reyes, J.A.; Rocha-Lona, L.; Tortorella, G. Exploring Industry 4.0 technologies to enable circular economy practices in a manufacturing context. J. Manuf. Technol. Manag. 2019 , 30, 607–627. [CrossRef] 7. Culot, G.; Orzes, G.; Sartor, M.; Nassimbeni, G. The future of manufacturing: A Delphi-based scenario analysis on Industry 4.0. Technol. Forecast. Soc. Chang. 2020,157, 120092. [CrossRef] 8. Nosalska, K.; Pi ˛atek, Z.M.; Mazurek, G.; Rz ˛adca, R. Industry 4.0: Coherent definition framework with technological and organizational interdependencies. J. Manuf. Technol. Manag. 2019,31, 837–862. [CrossRef] 9. Talwana, J.C.; Hua, H.J. Smart World of Internet of Things (IoT) and Its Security Concerns. In Proceedings of the 2016 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), Chengdu, China, 15–18 December 2016. [CrossRef] 10. Bruns, R.; Dunkel, J.; Masbruch, H.; Stipkovic, S. Intelligent M2M: Complex event processing for machine-to-machine communication. Expert Syst. Appl. 2015,42, 1235–1246. [CrossRef] 11. Chen, B.; Wan, J.; Shu, L.; Li, P.; Mukherjee, M.; Yin, B. Smart Factory of Industry 4.0: Key Technologies, Application Case, and Challenges. IEEE Access 2018,6, 6505–6519. [CrossRef] 12. Shrouf, F.; Miragliotta, G. Energy management based on Internet of Things: Practices and framework for adoption in production management. J. Clean. Prod. 2015,100, 235–246. [CrossRef] 13. Lee, J.; Davari, H.; Singh, J.; Pandhare, V. Industrial Artificial Intelligence for industry 4.0-based manufacturing systems. Manuf. Lett. 2018,18, 20–23. [CrossRef] 14. Shpilevoy, V.; Shishov, A.; Skobelev, P.; Kolbova, E.; Kazanskaia, D.; Shepilov, Y.; Tsarev, A. Multi-agent system “Smart Factory” for real-time workshop management in aircraft jet engines production. IFAC Proc. Vol. 2013,46, 204–209. [CrossRef] 15. Wang, S.; Wan, J.; Zhang, D.; Li, D.; Zhang, C. Towards smart factory for industry 4.0: A self-organized multi-agent system with big data based feedback and coordination. Comput. Netw. 2016,101, 158–168. [CrossRef] 16. Liu, Y.Y.; Hung, M.H.; Lin, Y.C.; Chen, C.C.; Gao, W.L.; Cheng, F.T. A Cloud-based Pluggable Manufacturing Service Scheme for Smart Factory. In Proceedings of the 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE), Munich, Germany, 20–24 August 2018. [CrossRef] 17. Wang, S.; Wan, J.; Imran, M.; Li, D.; Zhang, C. Cloud-based smart manufacturing for personalized candy packing application. J. Supercomput. 2016,74, 4339–4357. [CrossRef] 18. Romanovs, A.; Pichkalov, I.; Sabanovic, E.; Skirelis, J. Industry 4.0: Methodologies, Tools and Applications. In Proceedings of the 2019 Open Conference of Electrical, Electronic and Information Sciences (eStream), Vilnius, Lithuania, 25–25 April 2019. [CrossRef] 19. Tongsuksai, S.; Mathrani, S. Integrating Cloud ERP Systems with New Technologies Based on Industry 4.0: A Systematic Literature Review. In Proceedings of the 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), Gold Coast, Australia, 16–18 December 2020. [CrossRef] 20. Alcácer, V.; Cruz-Machado, V. Scanning the Industry 4.0: A Literature Review on Technologies for Manufacturing Systems. Eng. Sci. Technol. Int. J. 2019,22, 899–919. [CrossRef] 21. White, C. Now is the right time for real-time BI. Inf. Manag. 2004,14, 47. 22. Weyer, S.; Schmitt, M.; Ohmer, M.; Gorecky, D. Towards Industry 4.0—Standardization as the crucial challenge for highly modular, multi-vendor production systems. IFAC-PapersOnLine 2015,48, 579–584. [CrossRef] 23. Motyl, B.; Baronio, G.; Uberti, S.; Speranza, D.; Filippi, S. How will Change the Future Engineers’ Skills in the Industry 4.0 Framework? A Questionnaire Survey. Procedia Manuf. 2017,11, 1501–1509. [CrossRef] 24. Leyh, C.; Martin, S.; Schäffer, T. Industry 4.0 and Lean Production—A Matching Relationship? An analysis of selected Industry 4.0 models. In Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, Prague, Czech Republic, 3–6 September 2017. [CrossRef] 25. Tao, F.; Qi, Q.; Liu, A.; Kusiak, A. Data-driven smart manufacturing. J. Manuf. Syst. 2018,48, 157–169. [CrossRef] 26. Kusiak, A. Smart manufacturing. Int. J. Prod. Res. 2017,56, 508–517. [CrossRef] 27. Radziwon, A.; Bilberg, A.; Bogers, M.; Madsen, E.S. The Smart Factory: Exploring Adaptive and Flexible Manufacturing Solutions. Procedia Eng. 2014,69, 1184–1190. [CrossRef] Sustainability 2021,13, 10181 20 of 23 28. Lin, B.; Wu, W.; Song, M. Industry 4.0: Driving factors and impacts on firm’s performance: An empirical study on China’s manufacturing industry. Ann. Oper. Res. 2019, 1–21. [CrossRef] 29. Helu, M.; Libes, D.; Lubell, J.; Lyons, K.; Morris, K.C. Enabling Smart Manufacturing Technologies for Decision-Making Support. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference; American Society of Mechanical Engineers: Charlotte, NC, USA, 2016; Volume 50084. [CrossRef] 30. ˙ Zywicki, K.; Zawadzki, P.; Hamrol, A. Preparation and Production Control in Smart Factory Model. In Advances in Intelligent Systems and Computing; Springer International Publishing: Cham, Germany, 2017; pp. 519–527. [CrossRef] 31. Zawadzki, P.; ˙ Zywicki, K. Smart Product Design and Production Control for Effective Mass Customization in the Industry 4.0 Concept. Manag. Prod. Eng. Rev. 2016,7, 105–112. [CrossRef] 32. Kemény, Z.; Beregi, R.J.; Erd˝os, G.; Nacsa, J. The MTA SZTAKI Smart Factory: Platform for Research and Project-oriented Skill Development in Higher Education. Procedia CIRP 2016,54, 53–58. [CrossRef] 33. Tao, F.; Zhang, M.; Cheng, J.; Qi, Q. Digital twin workshop: A new paradigm for future workshop. Comput. Integr. Manuf. Syst. 2017,23, 1–9. 34. Zhuang, C.; Liu, J.; Xiong, H. Digital twin-based smart production management and control framework for the complex product assembly shop-floor. Int. J. Adv. Manuf. Technol. 2018,96, 1149–1163. [CrossRef] 35. Vrba, P.; Kadera, P.; Jirkovský, V.; Obitko, M.; Maˇrík, V. New Trends of Visualization in Smart Production Control Systems. In Holonic and Multi-Agent Systems for Manufacturing; Springer: Berlin/Heidelberg, Germany, 2011; pp. 72–83. [CrossRef] 36. Zeid, A.; Sundaram, S.; Moghaddam, M.; Kamarthi, S.; Marion, T. Interoperability in Smart Manufacturing: Research Challenges. Machines 2019,7, 21. [CrossRef] 37. Karre, H.; Hammer, M.; Kleindienst, M.; Ramsauer, C. Transition towards an Industry 4.0 State of the LeanLab at Graz University of Technology. Procedia Manuf. 2017,9, 206–213. [CrossRef] 38. Brozzi, R.; D’Amico, R.D.; Pasetti Monizza, G.; Marcher, C.; Riedl, M.; Matt, D. Design of Self-assessment Tools to Measure Industry 4.0 Readiness. A Methodological Approach for Craftsmanship SMEs. In Product Lifecycle Management to Support Industry 4.0; Springer International Publishing: Cham, Germany, 2018; pp. 566–578. [CrossRef] 39. Büchi, G.; Cugno, M.; Castagnoli, R. Smart factory performance and Industry 4.0. Technol. Forecast. Soc. Chang. 2020 ,150, 119790. [CrossRef] 40. Mauro, A.D.; Greco, M.; Grimaldi, M. A formal definition of Big Data based on its essential features. Libr. Rev. 2016 ,65, 122–135. [CrossRef] 41. Xu, X.; Hua, Q. Industrial Big Data Analysis in Smart Factory: Current Status and Research Strategies. IEEE Access 2017 , 5, 17543–17551. [CrossRef] 42. Al-Sai, Z.A.; Abualigah, L.M. Big data and E-government: A review. In Proceedings of the 2017 8th International Conference on Information Technology (ICIT), Amman, Jordan, 17–18 May 2017. [CrossRef] 43. Illa, P.K.; Padhi, N. Practical Guide to Smart Factory Transition Using IoT, Big Data and Edge Analytics. IEEE Access 2018 , 6, 55162–55170. [CrossRef] 44. Tuptuk, N.; Hailes, S. Security of smart manufacturing systems. J. Manuf. Syst. 2018,47, 93–106. [CrossRef] 45. Wilkesmann, M.; Wilkesmann, U. Industry 4.0 – organizing routines or innovations? VINE J. Inf. Knowl. Manag. Syst. 2018 , 48, 238–254. [CrossRef] 46. Chen, G.; Wang, P.; Feng, B.; Li, Y.; Liu, D. The framework design of smart factory in discrete manufacturing industry based on cyber-physical system. Int. J. Comput. Integr. Manuf. 2019,33, 79–101. [CrossRef] 47. Weyrich, M.; Schmidt, J.P.; Ebert, C. Machine-to-Machine Communication. IEEE Softw. 2014,31, 19–23. [CrossRef] 48. Vaidya, S.; Ambad, P.; Bhosle, S. Industry 4.0—A Glimpse. Procedia Manuf. 2018,20, 233–238. [CrossRef] 49. Pech, M.; Vrchota, J.; Bednáˇr, J. Predictive Maintenance and Intelligent Sensors in Smart Factory: Review. Sensors 2021 ,21, 1470. [CrossRef] 50. Oluyisola, O.E.; Sgarbossa, F.; Strandhagen, J.O. Smart Production Planning and Control: Concept, Use-Cases and Sustainability Implications. Sustainability 2020,12, 3791. [CrossRef] 51. Davis, J.; Edgar, T.; Porter, J.; Bernaden, J.; Sarli, M. Smart manufacturing, manufacturing intelligence and demand-dynamic performance. Comput. Chem. Eng. 2012,47, 145–156. [CrossRef] 52. Kawa, A. SMART Logistics Chain. In Intelligent Information and Database Systems; Springer: Berlin/Heidelberg, Germany, 2012; pp. 432–438. [CrossRef] 53. Oh, J.; Jeong, B. Tactical supply planning in smart manufacturing supply chain. Robot. Comput.-Integr. Manuf. 2019 ,55, 217–233. [CrossRef] 54. Park, K.T.; Nam, Y.W.; Lee, H.S.; Im, S.J.; Noh, S.D.; Son, J.Y.; Kim, H. Design and implementation of a digital twin application for a connected micro smart factory. Int. J. Comput. Integr. Manuf. 2019,32, 596–614. [CrossRef] 55. Moyne, J.; Iskander, J. Big Data Analytics for Smart Manufacturing: Case Studies in Semiconductor Manufacturing. Processes 2017,5, 39. [CrossRef] 56. O’Donovan, P.; Leahy, K.; Bruton, K.; O’Sullivan, D.T.J. An industrial big data pipeline for data-driven analytics maintenance applications in large-scale smart manufacturing facilities. J. Big Data 2015,2. [CrossRef] Sustainability 2021,13, 10181 21 of 23 57. Moyne, J. International Technology Roadmap for Semiconductors (ITRS) Factory Integration, 2015: Summary of Updates and Deep Dive Into Big Data Enhancements. In Proceedings of the APC Conference XXVII, Austin, TX, USA, 12–15 October 2015; pp. 12–15. 58. Dong, X.L.; Srivastava, D. Big data integration. In Proceedings of the 2013 IEEE 29th International Conference on Data Engineering (ICDE), Brisbane, Australia, 8–12 April 2013. [CrossRef] 59. Manyika, J.; Chui, M.; Brown, B.; Bughin, J.; Dobbs, R.; Roxburgh, C.; Hung Byers, A. Big Data: The Next Frontier for Innovation, Competition, and Productivity; McKinsey Global Institute: Washington, DC, USA, 2011. 60. Maier, A.; Schriegel, S.; Niggemann, O. Big Data and Machine Learning for the Smart Factory—Solutions for Condition Monitoring, Diagnosis and Optimization. In Industrial Internet of Things; Springer International Publishing: Cham, Germany, 2016; pp. 473–485. [CrossRef] 61. Günther, W.A.; Mehrizi, M.H.R.; Huysman, M.; Feldberg, F. Debating big data: A literature review on realizing value from big data. J. Strateg. Inf. Syst. 2017,26, 191–209. [CrossRef] 62. Papazoglou, M.P.; van den Heuvel, W.J.; Mascolo, J.E. A Reference Architecture and Knowledge-Based Structures for Smart Manufacturing Networks. IEEE Softw. 2015,32, 61–69. [CrossRef] 63. Chungoora, N.; Young, R.I.; Gunendran, G.; Palmer, C.; Usman, Z.; Anjum, N.A.; Cutting-Decelle, A.F.; Harding, J.A.; Case, K. A model-driven ontology approach for manufacturing system interoperability and knowledge sharing. Comput. Ind. 2013 , 64, 392–401. [CrossRef] 64. Lu, Y.; Riddick, F.; Ivezic, N. The Paradigm Shift in Smart Manufacturing System Architecture. In IFIP Advances in Information and Communication Technology; Springer International Publishing: Cham, Germany, 2016; pp. 767–776. [CrossRef] 65. Pfeifer, M.R. Operative Production Controlling as Entrance into Controlling 4.0. Trends Econ. Manag. 2021,15, 37. [CrossRef] 66. Jeon, B.W.; Um, J.; Yoon, S.C.; Suk-Hwan, S. An architecture design for smart manufacturing execution system. Comput.-Aided Des. Appl. 2016,14, 472–485. [CrossRef] 67. Larreina, J.; Gontarz, A.; Giannoulis, C.; Nguyen, V.; Stavropoulos, P.; Sinceri, B. Smart Manufacturing Execution System (SMES): The Possibilities of Evaluating the Sustainability of a Production Process. In Proceedings of the 11th Global Conference on Sustainable Manufacturing, Berlin, Germany, 23–25 September 2013. [CrossRef] 68. Kim, T.H.; Jeong, J.; Kim, Y. A Conceptual Model of Smart Manufacturing Execution System for Rolling Stock Manufacturer. Procedia Comput. Sci. 2019,151, 600–606. [CrossRef] 69. Menezes, S.; Creado, S.; Zhong, R.Y. Smart Manufacturing Execution Systems for Small and Medium-sized Enterprises. Procedia CIRP 2018,72, 1009–1014. [CrossRef] 70. Asadzadeh, L. A local search genetic algorithm for the job shop scheduling problem with intelligent agents. Comput. Ind. Eng. 2015,85, 376–383. [CrossRef] 71. Jain, A.; Meeran, S. Deterministic job-shop scheduling: Past, present and future. Eur. J. Oper. Res. 1999 ,113, 390–434. [CrossRef] 72. Vogel-Heuser, B.; Lee, J.; Leitão, P. Agents enabling cyber-physical production systems. Automatisierungstechnik 2015 ,63, 10. [CrossRef] 73. Frazzon, E.M.; Kück, M.; Freitag, M. Data-driven production control for complex and dynamic manufacturing systems. CIRP Ann. 2018,67, 515–518. [CrossRef] 74. Leusin, M.E.; Kück, M.; Frazzon, E.M.; Maldonado, M.U.; Freitag, M. Potential of a Multi-Agent System Approach for Production Control in Smart Factories. IFAC-PapersOnLine 2018,51, 1459–1464. [CrossRef] 75. Anderl, R. Industrie 4.0-advanced engineering of smart products and smart production. In Proceedings of the International Seminar on High Technology, Tehran, Iran, 6–9 October 2014; Volume 19. 76. Liao, Y.; Deschamps, F.; de Freitas Rocha Loures, E.; Ramos, L.F.P. Past, present and future of Industry 4.0—A systematic literature review and research agenda proposal. Int. J. Prod. Res. 2017,55, 3609–3629. [CrossRef] 77. Brintrup, A.; Pak, J.; Ratiney, D.; Pearce, T.; Wichmann, P.; Woodall, P.; McFarlane, D. Supply chain data analytics for predicting supplier disruptions: A case study in complex asset manufacturing. Int. J. Prod. Res. 2019,58, 3330–3341. [CrossRef] 78. Schrittwieser, J.; Antonoglou, I.; Hubert, T.; Simonyan, K.; Sifre, L.; Schmitt, S.; Guez, A.; Lockhart, E.; Hassabis, D.; Graepel, T.; et al. Mastering Atari, Go, chess and shogi by planning with a learned model. Nature 2020,588, 604–609. [CrossRef] 79. Shiue, Y.R.; Lee, K.C.; Su, C.T. Real-time scheduling for a smart factory using a reinforcement learning approach. Comput. Ind. Eng. 2018,125, 604–614. [CrossRef] 80. Vlahavas, I.; Refanidis, I. Planning and Scheduling; Technical Report; EETN: Thessaloniki, Greece, 2013. 81. Segler, M.H.S.; Preuss, M.; Waller, M.P. Planning chemical syntheses with deep neural networks and symbolic AI. Nature 2018 , 555, 604–610. [CrossRef] 82. Fliess, M.; Join, C. Machine Learning and Control Engineering: The Model-Free Case. In Proceedings of the Future Technologies Conference (FTC), San Francisco, CA, USA, 5–6 November 2020; Springer International Publishing: Cham, Germany, 2020; Volume 1, pp. 258–278. [CrossRef] 83. Serrano, W. Genetic and deep learning clusters based on neural networks for management decision structures. Neural Comput. Appl. 2019,32, 1–25. [CrossRef] 84. Kratzwald, B.; Ili´c, S.; Kraus, M.; Feuerriegel, S.; Prendinger, H. Deep learning for affective computing: Text-based emotion recognition in decision support. Decis. Support Syst. 2018,115, 24–35. [CrossRef] Sustainability 2021,13, 10181 22 of 23 85. Haddara, M.; Elragal, A. The Readiness of ERP Systems for the Factory of the Future. Procedia Comput. Sci. 2015 ,64, 721–728. [CrossRef] 86. Guo, Z.; Zhang, Y.; Zhao, X.; Song, X. CPS-Based Self-Adaptive Collaborative Control for Smart Production-Logistics Systems. IEEE Trans. Cybern. 2021,51, 188–198. [CrossRef] [PubMed] 87. Zhang, Y.; Guo, Z.; Lv, J.; Liu, Y. A Framework for Smart Production-Logistics Systems Based on CPS and Industrial IoT. IEEE Trans. Ind. Inform. 2018,14, 4019–4032. [CrossRef] 88. Coetzer, A.; Inma, C.; Poisat, P.; Redmond, J.; Standing, C. Does job embeddedness predict turnover intentions in SMEs? Int. J. Product. Perform. Manag. 2019,68, 340–361. [CrossRef] 89. Veile, J.W.; Kiel, D.; Müller, J.M.; Voigt, K.I. Lessons learned from Industry 4.0 implementation in the German manufacturing industry. J. Manuf. Technol. Manag. 2019,31, 977–997. [CrossRef] 90. European Commission. Commission Recommendation of 6 May 2003 Concerning the Definition of Micro, Small and Medium-Sized Enterprises; Official Journal of the European Union: Brussels, Belgium, 2003; pp. 36–41. 91. Sommer, L. Industrial revolution—Industry 4.0: Are German manufacturing SMEs the first victims of this revolution? J. Ind. Eng. Manag. 2015,8. [CrossRef] 92. Auzzir, Z.; Haigh, R.; Amaratunga, D. Impacts of Disaster to SMEs in Malaysia. Procedia Eng. 2018,212, 1131–1138. [CrossRef] 93. Mittal, S.; Khan, M.A.; Romero, D.; Wuest, T. A critical review of smart manufacturing & Industry 4.0 maturity models: Implications for small and medium-sized enterprises (SMEs). J. Manuf. Syst. 2018,49, 194–214. 94. Doh, S.; Kim, B. Government support for SME innovations in the regional industries: The case of government financial support program in South Korea. Res. Policy 2014,43, 1557–1569. [CrossRef] 95. Madrid-Guijarro, A.; García-Pérez-de Lema, D.; Van Auken, H. Financing constraints and SME innovation during economic crises. Acad. Rev. Latinoam. Adm. 2016,29, 84–106. [CrossRef] 96. Organisation for Economic Co-Operation and Development (OECD). Small and Medium-Sized Enterprises: Local Strength, Global Reach; OECD: Paris, France, 2000. 97. Wang, X.L.; Wang, L.; Bi, Z.; Li, Y.Y.; Xu, Y. Cloud computing in human resource management (HRM) system for small and medium enterprises (SMEs). Int. J. Adv. Manuf. Technol. 2016,84, 485–496. [CrossRef] 98. Brettel, M.; Friederichsen, N.; Keller, M.; Rosenberg, M. How virtualization, decentralization and network building change the manufacturing landscape: An Industry 4.0 Perspective. Int. J. Mech. Ind. Sci. Eng. 2014,8, 37–44. 99. Bandyopadhyay, T.; Jacob, V.; Raghunathan, S. Information security in networked supply chains: Impact of network vulnerability and supply chain integration on incentives to invest. Inf. Technol. Manag. 2010,11, 7–23. [CrossRef] 100. Falkner, E.M.; Hiebl, M.R. Risk management in SMEs: A systematic review of available evidence. J. Risk Financ. 2015 ,16, 122–144. [CrossRef] 101. Heidt, M.; Gerlach, J.P.; Buxmann, P. Investigating the Security Divide between SME and Large Companies: How SME Characteristics Influence Organizational IT Security Investments. Inf. Syst. Front. 2019,21, 1285–1305. [CrossRef] 102. Chatterjee, D. Should executives go to jail over cybersecurity breaches? J. Organ. Comput. Electron. Commer. 2019 ,29, 1–3. [CrossRef] 103. Müller, J.M.; Kiel, D.; Voigt, K.I. What Drives the Implementation of Industry 4.0? The Role of Opportunities and Challenges in the Context of Sustainability. Sustainability 2018,10, 247. [CrossRef] 104. Moeuf, A.; Pellerin, R.; Lamouri, S.; Tamayo-Giraldo, S.; Barbaray, R. The industrial management of SMEs in the era of Industry 4.0. Int. J. Prod. Res. 2017,56, 1118–1136. [CrossRef] 105. Schumacher, A.; Erol, S.; Sihn, W. A Maturity Model for Assessing Industry 4.0 Readiness and Maturity of Manufacturing Enterprises. Procedia CIRP 2016,52, 161–166. [CrossRef] 106. Mittal, S.; Khan, M.A.; Purohit, J.K.; Menon, K.; Romero, D.; Wuest, T. A smart manufacturing adoption framework for SMEs. Int. J. Prod. Res. 2019,58, 1555–1573. [CrossRef] 107. Omri, N.; Masry, Z.A.; Mairot, N.; Giampiccolo, S.; Zerhouni, N. Industrial data management strategy towards an SME-oriented PHM. J. Manuf. Syst. 2020,56, 23–36. [CrossRef] 108. Qin, J.; Liu, Y.; Grosvenor, R. A Categorical Framework of Manufacturing for Industry 4.0 and Beyond. Procedia CIRP 2016 , 52, 173–178. [CrossRef] 109. Mittal, S.; Romero, D.; Wuest, T. Towards a Smart Manufacturing Maturity Model for SMEs (SM3E). In Advances in Production Management Systems. Smart Manufacturing for Industry 4.0; Springer International Publishing: Cham, Germany, 2018; pp. 155–163. [CrossRef] 110. Ganzarain, J.; Errasti, N. Three stage maturity model in SME’s toward industry 4.0. J. Ind. Eng. Manag. 2016 ,9, 1119. [CrossRef] 111. Rauch, E.; Dallasega, P.; Unterhofer, M. Requirements and Barriers for Introducing Smart Manufacturing in Small and MediumSized Enterprises. IEEE Eng. Manag. Rev. 2019,47, 87–94. [CrossRef] 112. Shao, G.; Shin, S.J.; Jain, S. Data analytics using simulation for smart manufacturing. In Proceedings of the Winter Simulation Conference, Savannah, GA, USA, 7–10 December 2014. [CrossRef] 113. Ivers, A.M.; Byrne, J.; Byrne, P. Analysis of SME data readiness: A simulation perspective. J. Small Bus. Enterp. Dev. 2016 , 23, 163–188. [CrossRef] 114. Singapore Economic Development Board The Singapore smart industry readiness index. In Catalysing the Transformation of Manufacturing; Singapore Economic Development Board: Singapore, 2018. Sustainability 2021,13, 10181 23 of 23 115. Popescu, L.; Iancu, A.; Avram, M.; Avram, D.; Popescu, V. The Role of Managerial Skills in the Sustainable Development of SMEs in Mehedinti County, Romania. Sustainability 2020,12, 1119. [CrossRef] 116. Cimini, C.; Boffelli, A.; Lagorio, A.; Kalchschmidt, M.; Pinto, R. How do industry 4.0 technologies influence organisational change? An empirical analysis of Italian SMEs. J. Manuf. Technol. Manag. 2020,32, 695–721. [CrossRef] 117. Casey, C. Corporate Transformations: Designer Culture, Designer Employees and ‘Post-Occupational’ Solidarity. Organization 1996,3, 317–339. [CrossRef] 118. Qu, Y.J.; Ming, X.G.; Liu, Z.W.; Zhang, X.Y.; Hou, Z.T. Smart manufacturing systems: State of the art and future trends. Int. J. Adv. Manuf. Technol. 2019,103, 3751–3768. [CrossRef] 119. Kaartinen, H.; Pieska, S.; Vahasoyrinki, J. Digital manufacturing toolbox for supporting the manufacturing SMEs. In Proceedings of the 2016 7th IEEE International Conference on Cognitive Infocommunications (CogInfoCom), Wroclaw, Poland, 16–18 October 2016. [CrossRef] 120. Schnell, H. Unternehmenssteuerung mit Kennzahlen. Auswahl, Ermittlung, Analyse, Kommunikation. In Kennzahlen zur Effizienzsicherung in der Produktion; Klein, A., Ed.; Haufe-Lexware: Freiburg, Germany, 2014; Volume 1499. 121. Wagner, T.; Herrmann, C.; Thiede, S. Industry 4.0 Impacts on Lean Production Systems. Procedia 2017,63, 125–131. [CrossRef] 122. Härtel, L.; Nyhuis, P. Systematic Data Analysis in Production Controlling Systems to Increase Logistics Performance. In Advances in Production Research; Springer International Publishing: Cham, Germany, 2018; pp. 3–13. [CrossRef] 123. Heimel, J.; Müller, M. Controlling 4.0. In Management 4.0–Unternehmensführung im digitalen Zeitalter; Springer: Berlin/Heidelberg, Germany, 2019; pp. 389–430. 124. Khan, N.; Yaqoob, I.; Hashem, I.A.T.; Inayat, Z.; Ali, W.K.M.; Alam, M.; Shiraz, M.; Gani, A. Big Data: Survey, Technologies, Opportunities, and Challenges. Sci. World J. 2014,2014, 1–18. [CrossRef] [PubMed] 125. Pfeifer, M.R. Computer-Aided Standardisation for Manufacturing and Maintenance Activities. Technol. Eng. 2019 ,16, 22–24. [CrossRef] 126. Sundsøy, P.; Bjelland, J.; Iqbal, A.M.; Pentland, A.; de Montjoye, Y.A. Big Data-Driven Marketing: How Machine Learning Outperforms Marketers’ Gut-Feeling. In Social Computing, Behavioral-Cultural Modeling and Prediction; Springer International Publishing: Cham, Germany, 2014; pp. 367–374. [CrossRef] 127. Simons, H. Interpret in context: Generalizing from the single case in evaluation. Evaluation 2015,21, 173–188. [CrossRef] 128. Dyer, W.G.; Wilkins, A.L. Better Stories, Not Better Constructs, To Generate Better Theory: A Rejoinder to Eisenhardt. Acad. Manag. Rev. 1991,16, 613–619. [CrossRef] 129. Štrach, P. Tvorba v` yukov` ych a v` yzkumn` ych pˇrípadov` ych studií. Acta Oeconomica Pragensia 2007,15, 22–36. [CrossRef] 130. Locke, R.; Thelen, K. Problems of equivalence in comparative politics: Apples and oranges, again. Newsl. APSA Organ. Sect. Comp. Politics 1998,9, 9–12. 131. Collier, D. The comparative method. In Political Science: The State of Discipline II; Ada, W.F., Ed.; American Political Science Association: Washington, DC, USA, 1993. 132. Miles, M.B.; Huberman, A.M. Qualitative Data Analysis: An Expanded Sourcebook; Sage: Thousand Oaks, CA, USA; London, UK; New Delhi, India, 1994. 133. Eisenhardt, K.M. Building Theories from Case Study Research. Acad. Manag. Rev. 1989,14, 532–550. [CrossRef] 134. Bartezzaghi, E.; Cagliano, R.; Canterino, F.; Longoni, A. Organizing for smart manufacturing. In Proceedings of the 24th International Annual EurOMA Conference, “Inspiring Operations Management”, Edinburgh, UK, 1 July 2018; p. 12. 135. Cagliano, R.; Canterino, F.; Longoni, A.; Bartezzaghi, E. The interplay between smart manufacturing technologies and work organization. Int. J. Oper. Prod. Manag. 2019,39, 913–934. [CrossRef] 136. Yin, R.K. Case Study Research: Design and Methods; Sage: Thousand Oaks, CA, USA; London, UK; New Delhi, India; Singapore, 2009; Volume 5. 137. Bass, J.M.; Beecham, S.; Noll, J. Experience of industry case studies. In Proceedings of the 6th International Workshop on Conducting Empirical Studies in Industry, Gothenburg, Sweden, 27 May–3 June 2018. [CrossRef] 138. Johansson, R. On Case Study Methodology. Open House Int. 2007,32, 48–54. [CrossRef] 139. Gunesoglu, S.; Meric, B. The analysis of personal and delay allowances using work sampling technique in the sewing room of a clothing manufacturer. Int. J. Cloth. Sci. Technol. 2007,19, 145–150. [CrossRef] 140. Abée, S.; Andrae, S.; Schlemminger, R.B. Strategisches Controlling 4.0: Wie der Digitale Wandel Gelingt; Springer: Wiesbaden, Germany, 2020. 141. Kotler, P.; Kartajaya, H.; Setiawan, I. Marketing 4.0: Moving from Traditional to Digital; Wiley: Hoboken, NJ, USA, 2016.