Smart Manufacturing and Production Flexibility under Market and Demand Fluctuations: Evidence from Electrical and Electronic Industrial Companies in Iraq

Ethical considerations

This study involved human participants through a structured questionnaire distributed to employees and managers in industrial companies.

Ethical approval was not formally required for this study according to the institutional guidelines of Northern Technical University, as the research did not involve clinical experiments or sensitive personal data. However, the study was conducted in accordance with general ethical research principles.

All participants were informed about the purpose of the study, and their participation was entirely voluntary. The confidentiality and anonymity of all participants were strictly maintained, and no personally identifiable information was collect.

Smart manufacturing

There have been a lot of “industrial revolutions,” the most recent of which was the Fourth Industrial Revolution, or Industry 4.0. All of these factors had a significant impact on the manufacturing sector. Several new technologies and ideas have emerged in the industrial era. These have been shown to make manufacturing easier, more efficient, and more competitive worldwide. These new ideas make smart manufacturing one of the most important ways to help the economic growth.

Linking machines through the Internet of Things (IoT) or other communication networks is not always used in smart manufacturing. This approach considers the entire industrial system. It connects different parts of an industrial ecosystem using data-based smart technologies and processes. This integration helps businesses get closer to being fully digital, which keeps them in touch with rapidly changing market conditions, shifting customer tastes, and growing environmental needs in real-time.

Smart manufacturing technologies help factories better use resources and make their operations more adaptable. This provides them with a long-term edge over their competitors. These tools help companies become accustomed to changes in the market more quickly, boost their ability to solve problems, and make the most out of new opportunities (Wang et al., 2024; Zhang & Lee, 2025). In addition, smart manufacturing is better for the environment because it uses less energy and produces less waste, thus protecting resources for future generations (Kumar et al., 2023; Li, Zhao, & Huang, 2025). Using IoT, AI-driven analytics, and cloud computing all at once makes it easier to make decisions immediately. This helps make the kind of production settings that can change and are strong, which is in line with the ideas of Industry 4.0 (Smith & Chen, 2024; Garcia, et al., 2025).

Internet of Things (IoT), cyber-physical systems, and cloud computing are advanced technologies that have made smart manufacturing much more popular in today’s production environment. All of these technologies change the way things are made, increase the value of goods, and help different people (Schlemitz & Mezhuyev, 2024, 135). According to Fan et al. (2025, 17), smart manufacturing environments have come into being to bring human and robotic systems closer together by combining different types of data and encouraging cooperation, which makes the system more productive, adaptable, and efficient. In addition, setting standards and making things work together are seen as very important for the success of smart manufacturing. They ensure that systems can work with each other, connect without a hitch, and run efficiently (Villigran, et al., 2019, 352).

Ren et al. (2024, 17), however, pointed out that some problems still need to be addressed. Before smart manufacturing can be realized. Their assertion is that this is a situation. This is what they claim is a situation. The modeling and simulation of manufacturing equipment, processes, and workshops, which are activities that require a significant amount of human expertise and effort, are particularly susceptible to these challenges. These challenges are prevalent in the manufacturing industry. To achieve higher levels of efficiency, precision, and specialization, it is necessary to employ unconventional methods of thinking, rapid iteration, and intelligent monitoring mechanisms. This is because of the challenges presented. Similarly, Qu et al. (2019) pointed out that smart manufacturing relies on advanced intelligent systems that make it easier for products to be made quickly, for systems to respond quickly to changes, and for products, processes, distribution centers, and supply networks to be part of an interconnected industrial ecosystem. However, smart manufacturing cannot operate without these systems. These systems are necessary for smart manufacturing to be successful. As a result of this interconnectedness, the development of intelligent products and processes that can adjust to customer preferences and market dynamics is greatly encouraged. Smart manufacturing is a system that is fully integrated and collaborative, and it is able to react in real time to changes in the conditions of the factory, the activities of the supply chain, and the requirements of the customers, as (Nimmala 2019, 4) emphasizes. In conclusion, smart manufacturing is a system that responds to changes in factory conditions.

Recent developments in smart manufacturing

Recent studies have emphasized that smart manufacturing is becoming a core strategic approach for improving industrial responsiveness, resilience, and operational efficiency in highly dynamic markets. Advanced technologies such as artificial intelligence, industrial Internet of Things (IoT), digital twins, cloud computing, and predictive analytics are increasingly integrated into manufacturing systems to support real-time decision-making and adaptive production processes.

According to Garcia et al. (2025), AI-driven analytics significantly improve production agility and operational adaptability by enabling manufacturers to predict disruptions and optimize workflows dynamically. Similarly, Li and Wang (2025) highlighted the growing role of digital twins in enhancing manufacturing flexibility through virtual simulation and real-time system monitoring. Chen et al. (2024) also emphasized that smart manufacturing contributes to sustainable industrial performance by reducing operational waste and improving energy efficiency.

Furthermore, recent Industry 4.0 research indicates that smart manufacturing systems enhance supply chain resilience and organizational responsiveness during periods of market uncertainty and fluctuating customer demand (Wang et al., 2024; Zhang & Lee, 2025). These developments demonstrate that smart manufacturing is evolving beyond automation toward intelligent and self-optimizing industrial ecosystems capable of supporting long-term competitive advantage.

Dimensions of smart manufacturing

Several researchers have examined the foundations of smart manufacturing and viewed them as essential dimensions or drivers for implementing smart manufacturing in industrial organizations. Notable studies in this area include those by Kusiak (2017, 158), Qu et al. (2019, 130) and Schlemitz and Mezhuyev (2024, 138).

Process and manufacturing technology

New technologies and methods in manufacturing will help us make new, smarter products that can adapt to new situations. These tools make it easier to quickly adapt to changes in technology and the market. They also ensure that all parts of the operational process work together as a value chain. They encourage new ideas and competition by making things cheaper and more efficient. This maintains the lead in the field.

The concept of single-batch production is an important part of this change. Thus, when conditions change, manufacturers can quickly change how they do things by combining different ways of making things, such as computer numerical control (CNC), additive manufacturing, and automation. In this way, computer-aided design (CAD) files tell manufacturing systems about product needs, and the systems come up with a range of options for making things by themselves (Lu et al., 2019, 39).

Resource sharing

Smart manufacturing uses the best methods to obtain high quality and flexibility at a low cost. In a factory, people and machines are connected to the outside world. A multilayer structure makes it easy for many people to work together at the horizontal level. In this setup, cyberspace has the same physical resources as factory floors. This allows groups to work together and share. This helps suppliers obtain things and services to the right place on time, either for free or at a low cost. Regardless of the location or location, smart manufacturing can connect customers and supply chains (Huang, 2021).

Data and networks

Enterprise resource planning (ERP) systems help to make vertically integrated manufacturing processes more common. This is an important way to create more opportunities for improvement and automation in the industry. This integration helps people share information, start relevant operational procedures, and control the process, which makes the production hierarchy more aligned with itself at all levels. Smart manufacturing based on data depends on big datasets that contain many different kinds of data. These datasets are characterized by volume, variety, velocity, and variability. Artificial intelligence (AI), the Internet of Things (IoT), cloud computing, and other technologies that make it possible are at the heart of its construction. Owing to these factors, the entire production life cycle requires settings that can be scaled and support large data storage, fast processing, and AI-powered advanced analytics. Here, the focus of smart manufacturing is on making products based on the customer so that the market and consumer tastes can be met. This method was improved by self-organizing and open resource management, data-driven process control, real-time self-monitoring, and adaptive self-learning. Using both historical and real-time data can help businesses adapt to changing conditions quickly and easily. This may result in better efficiency in manufacturing, product performance, and overall competitiveness (Tao et al., 2018, 190).

Predictive engineering

Smart manufacturing utilizes simulation models and virtual technologies to anticipate future changes, support intelligent reprogramming, predictive analytics, autonomous control, and re-engineering. This transforms organizations from merely reactive to proactive. Through analysis, monitoring, and autonomous control, digital models have been developed to study relevant phenomena, predict future outcomes, and incorporate both physical and virtual sensors within the industrial environment.

Sustainability

Intelligent manufacturing is a new way of making things that are more efficient, cheaper, and do ot use as much energy (Nimmala, 2019, 7). These methods require more resources and energy. They also make the environment healthier and safer at work, and the economy more competitive (Chen et al., 2024; Kumar & Verma, 2023). Smart manufacturing is a large, planned way of doing things that has many benefits. It uses new technologies, manages energy, and ensures that products can be traced, vertical integration, virtual simulations, automation, flexible production systems, and interconnected network strategies. Ideas from Industry 4.0 were used to make this method (Kannan et al., 2023; Li & Wang, 2025; Garcia et al., 2025). It aims to make things easier, get emissions close to zero, lower the risk of accidents in manufacturing, and make long-term management easier.

What is flexibility

It’s a fundamental dynamic capability that allows organaizations to respond to changing environments in a timely and efficient manner (Pivorienė, 2015, 436) (Kalogiannidis et al., 2022. 259) and It measures the ability to adapt to change and often has multiple dimensions that impact value jointly yet differently (Alolayyana et al., 2022,1338). It has many dimensions; in our research, we take five dimensions.

Dimensions of production flexibility

Volume flexibility

Volume flexibility is the ability of a production process to change the amount of product it produces when customer demand changes. With this ability, businesses can quickly and easily adapt to market (Tran et al. 2025, 3; Kavaliauskiene et al. 2022, 846). Ewis et al. (2025, 1618) say that volume flexibility is the ability of a business to produce more or less while still being efficient and making money. In real life, volume flexibility means being able to make more or less than the planned amount. This allows businesses to adjust to sudden changes in customer demand without hurting their operations or financing. It is often measured as the percentage of the total unit cost that increases after production planning changes. A high fraction indicates that the volume can change more easily. This is because firms are more likely to increase their production capacity (which makes it less likely that output will be limited) when the costs of having that capacity are low compared with the costs of making things. Additionally, when costs are still changing at the update stage, firms are motivated to change the amount they produce based on the new demand forecast. Volume flexibility depends on many factors, such as the cost of the production process and the ease of changing supply contracts. Both affect how easily a company can increase or decrease production in response to market changes.

Mix flexibility

A flexibility mix allows a business to change how it makes things, so it can make many different kinds of products with many different types of specifications (Tran et al., 2025, 3; Kavaliauskiene et al., 2022, 846). It shows how good a factory is at making many different parts and products and quickly changing them to meet the needs of different products. Al-Obaidy et al. (2023, 4) also stated that if a manufacturing process can be easily changed to fit different products, it can produce different types of products in the same production run without any problems.

Mix flexibility, also known as resource or product flexibility, is the ability to change the amount of one type of product into a different type. People usually look at the cost of changing one unit of capacity made for a certain product into a different one to figure this out.

If you can show that you can be more flexible with your mix, you might be able to obtain a better deal. In real life, many factors affect how easy it is to change a mix. For example, it might be difficult or expensive to change the tools used to create new products, and it also takes time to train workers on how to use different machines.

Product flexibility

A manufacturing system with product flexibility can add new parts to systems that are already in place without having to make many changes (Zhang, et al., 2017, 4). An important example of product flexibility is component similarity, which stresses the importance of using the same parts in different types of products. Increasing the number of components that are similar to each other reduces inventory needs and resource use, and simplifies manufacturing. The following promotes two mix and size freedom (Salvador et al., 2007, 1173). Tran et al. (2025 , 5) stated that product flexibility should include both new product and modification flexibility. Product flexibility includes development and people that help businesses proactively grow and change their product lines, as well as products with structures and designs that can be changed. From these points of view, we can see how important it is for a company to be able to both make brand-new products and make small changes to things that they already sell. This ensures that they can adapt to market changes and new technologies.

Resource flexibility

Corporations tend to be more successful when they have further versus where they can obtain what they need. A supplier will be okay if they do not make enough of the things needed to make something or if the things they make are bad. The business has “supply flexibility” (Ewis et al., 2025, 1617), which means it can choose a different supplier. Being resource flexible also means that one can handle materials in different ways, such as having and using machines to make production faster. A flexible workforce can quickly and cheaply complete a range of manufacturing jobs. If companies are flexible with their workers, they can quickly deal with unexpected increases in work (Hatmanto & Yuniarinto, 2022, 3).

Scheduling flexibility

It is necessary to investigate a company’s ability to modify delivery routes, timelines, and schedules in response to customer requirements and unanticipated events when evaluating an organization’s capacity to deliver and adapt schedules. This is due to the fact that the evaluation is being conducted in relation to the organization’s capability of offering and adjusting schedules. Al-Obaidy et al. (2023, 4) defined scheduling or routing flexibility as “the capacity of a manufacturing system to generate products via diverse routes.” This definition applies to both scheduling and routing flexibilities. This description is applicable to two aspects of adaptability: scheduling and routing.

“Volume flexibility” comes from more general literature on operational flexibility. This means an organization’s ability to change its overall production or service levels to meet demand that is either going up or down while keeping the business running smoothly. With this kind of flexibility in operational management, you can quickly respond to changes in the market and customer needs. Along the same lines, mix flexibility shows how well an organization can change the types of products it delivers to the market, while still keeping costs low.

Importantly, mix flexibility depends on process and product flexibility. When customers need something, process flexibility refers to how quickly and easily a company can decide to do something, change their plans, or change an order that has already been placed. Product flexibility refers to how many companies can change products to meet customer needs. When demand changes significantly, product flexibility usually decreases. When products are strategic complements or substitutes, volume flexibility may increase or decrease, respectively. In addition, volume elasticity is better at making aggregate demand less uncertain, whereas product elasticity focuses on making individual product demand less uncertain.

Research gap

Despite the growing body of literature on smart manufacturing and Industry 4.0 technologies, several important gaps remain insufficiently addressed. First, most previous studies have focused primarily on developed economies and large-scale manufacturing environments, while limited attention has been given to developing countries and emerging industrial contexts such as Iraq. Second, existing research has often examined smart manufacturing technologies from a technological or operational perspective without comprehensively analyzing their direct role in enhancing production flexibility under unstable market and demand conditions.

Moreover, previous studies rarely integrated the dimensions of smart manufacturing, including process technology, predictive engineering, sustainability, data and networks, and resource sharing, with multiple dimensions of production flexibility within a unified analytical framework. In addition, limited empirical evidence exists regarding how industrial organizations can utilize smart manufacturing technologies to improve responsiveness, adaptability, and operational resilience during periods of uncertainty and fluctuating customer demand.

Therefore, this study seeks to fill these theoretical and empirical gaps by providing an integrated analytical model that examines the relationship between smart manufacturing dimensions and production flexibility in electrical and electronic industrial companies in Iraq.

Methodology of the investigation

This study employs a descriptive analytical framework. The theoretical component was elucidated through a descriptive method, whereas the analytical method was applied practically. This methodological design facilitated the testing of the study’s hypotheses by scrutinizing the interrelationships between the primary and secondary variables, drawing upon data acquired from the participating companies.

The subsequent sub-hypothesis emerges from this premise:

A strong correlation is observed between smart manufacturing and diverse dimensions of production flexibility, including Volume Flexibility, Mix Flexibility, Product Flexibility, Process Flexibility, and Scheduling Flexibility.

Hypothesis 2 suggests that smart manufacturing significantly affects the overall production flexibility within the specific organizational setting being studied. Consequently, the following sub hypotheses are proposed:

Smart manufacturing significantly influences individual aspects of production flexibility, namely Volume Flexibility, Mix Flexibility, Product Flexibility, Process Flexibility, and Scheduling Flexibility.

Informed consent

Informed consent was obtained from all participants prior to their participation in the study. Participants were clearly informed about the purpose of the research and their right to withdraw at any time without any consequences.

Consent was obtained verbally due to the nature of the data collection, as the questionnaire was distributed in field settings within industrial companies where written consent was not practical. Participation in the survey was considered as consent after explanation of the study objectives.

The first hypothesis generates the following sub-hypothesis

A notable correlation was observed between Smart Manufacturing and the individual dimensions of production flexibility, as evidenced by the following:

The smart manufacturing and production flexibility dimensions are related. The research results show that there is a strong link between smart manufacturing techniques and different types of production flexibility. As for volume adaptability, the results show a significantly positive association at a significance level of 0.05, with a correlation value of 0.837. This means that firms can better adjust the amount of production they do when demands changes when they use smart manufacturing systems. In the same way, the research shows that there is a strong link between smart manufacturing and mix flexibility. This can be seen through a correlation value of 0.864 at 0.05, which suggests that the ability to easily handle a range of products has been improved. There was a strong positive association (0.907, p < 0.05) in terms of product flexibility, which shows that using smart production technologies can help make products more adaptable and customizable. The link between smart manufacturing and process flexibility seems to be very strong, as shown by a correlation value of 0.995 at the 0.05 significance level. This shows that smart systems are very good at making it possible for quick changes to be made to processes. Finally, a correlation value of 0.998 (p < 0.05) shows a strong link between flexible scheduling and the use of smart manufacturing. This shows that production planning and task scheduling can be more sensitive, supporting hypothesis (A) of the first primary hypothesis, as Figure 3 shows a strong positive association between Smart Manufacturing and production flexibility.

Figure 3. Structural equation model (SEM) illustrating the direct effect of smart manufacturing dimensions on overall production flexibility at the organizational level.

The second hypothesis

Smart Manufacturing has a considerable impact on the overall production flexibility at the organizational level. A structural equation model was constructed to test this hypothesis (Figure 4). The test values of this model are presented, which indicate the acceptance or rejection of our hypothesis, as detailed in Table 4, as follows:

Figure 4. Structural equation model showing the effects of smart manufacturing dimensions on the individual dimensions of production flexibility, including volume, mix, product, process, and scheduling flexibility.

The data presented in Table 4 demonstrate a direct and significant effect of Smart Manufacturing on production flexibility, evidenced by a standard regression coefficient (SRW) of (0.979) and a non-standard regression coefficient (estimate) of (2.232). A P-value of 0.02, which is below the threshold of 0.05, indicates that the effect is significant. The data show the confidence range for nonstandard regression statistics. The confidence level was set to 95%. Zero was not in the range of 1.001–0.954. This clarifies how important it is to know how the explanatory variable changes; therefore, this study looks at the second main part, which is based on the dependent variable. We now examine the impact of smart manufacturing on the different types of production flexibility at the company level. To determine how this relationship works in real life, a theory was made that smart manufacturing has a statistically significant effect on all aspects of the organization’s ability to be flexible with production that is being studied. A method called Structural equation modeling (SEM) was used to determine whether this idea worked. Figure 5 shows the suggested model and Table 5 shows the data and model fit results. These results indicate that the suggested idea is correct or incorrect.

Figure 5. Path diagram presenting standardized regression coefficients and critical ratios for testing the research hypotheses related to smart manufacturing and production flexibility.

Structural equation modeling showed that the model created to test sub-hypothesis (A) of the second main hypothesis is important. This is clear from the positive signs in Table 5 and the high saturation values over 45%, as shown in Figure 5. By examining the typical error values, it becomes clear that the highest impact of the dimensions of Intelligent Manufacturing (Combined) was in the dimension (Product Flexibility), which indicates that the organization under study seeks to maintain the production of flexible products that meet the needs and desires of customers by adopting a series of operations through which waste is recycled and used in remanufacturing again, while the lowest impact of the dimensions of Smart Manufacturing (Combined) was in the dimension (Product Flexibility), as the organization lives in a turbulent, rapidly changing environment that is unable to predict what is coming as a result of the surrounding changes in an external environment, The results show that the critical ratio (C.R.) value of 4.71 is higher than the 1.96 and 2.66 threshold values at the 0.05 and 0.01 levels, respectively. These were the same as the t-values used in the standard regression analysis. This result proves that the expected effects were statistically significant. Thus, the second sub-hypothesis is proven to be true. This shows that intelligent manufacturing dimensions working together have a significant effect on the single dimensions of production freedom.

Challenges and implementation barriers

Despite the significant advantages of smart manufacturing in enhancing production flexibility, several implementation challenges remain critical for industrial organizations. One of the primary barriers is the high initial investment cost associated with adopting advanced technologies such as artificial intelligence, industrial Internet of Things (IoT), cloud computing, and digital twin systems. Many industrial organizations, particularly in developing economies, may face financial limitations that restrict large-scale digital transformation initiatives.

Another major challenge relates to workforce readiness and technical expertise. Successful implementation of smart manufacturing systems requires skilled employees capable of operating, analyzing, and maintaining advanced digital technologies. Therefore, continuous employee training and organizational learning are essential to ensure effective technology adoption and operational sustainability.

Cybersecurity risks also represent an important concern in smart manufacturing environments due to the increasing interconnectivity between machines, data systems, and industrial networks. Unauthorized access, data breaches, and system vulnerabilities may negatively affect operational reliability and organizational performance.

Furthermore, integrating smart manufacturing technologies into existing industrial infrastructures can be technically complex and may require gradual implementation strategies, organizational restructuring, and continuous technological adaptation. Addressing these challenges is essential for achieving long-term operational flexibility and sustainable competitive performance.

Limitations

This study was limited to electrical and electronic industrial companies in Iraq, which may affect the generalizability of the findings to other industrial sectors or countries with different technological and economic conditions. In addition, the study adopted a cross-sectional survey design, which limits the ability to examine long-term changes and dynamic developments in smart manufacturing implementation over time. Furthermore, some statistical assumptions related to multivariate normality and sample adequacy were not fully satisfied, which led to the adoption of the Balanced Free Least Squares estimation method.

Future research directions

Future studies are encouraged to investigate smart manufacturing and production flexibility across different industrial sectors and geographical contexts to improve the generalizability of findings. Longitudinal research designs are also recommended to evaluate the long-term sustainability and adaptability of smart manufacturing systems under rapidly changing market conditions. In addition, future research may explore the impact of cybersecurity readiness, organizational culture, digital transformation maturity, and employee technological capabilities on the successful implementation of smart manufacturing systems.