Digital Twin for Energy-Intelligent Bakery Operations: Concepts and Applications

Abstract

The bakery industry is undergoing a profound digital transformation driven by the increasing need for enhanced energy efficiency, operational resilience, and a commitment to environmental sustainability. Digital Twin (DT) technology, recognized as a fundamental component of Industry 4.0, provides advanced capabilities for intelligent energy management across bakery operations. This paper utilizes a narrative and integrative review approach, conceptually integrating emerging developments in using DT with respect toenergy management in the baking industry, including real-time energy monitoring, predictive maintenance, dynamic optimization of production processes, and the seamless integration of renewable energy sources. The study underscores the transformative benefits of adopting DT technologies, such as improvements in energy utilization, greater equipment reliability, increased operational transparency, and stronger alignment with global sustainability objectives. It also critically examines the technical, organizational, and financial barriers limiting broader adoption, particularly among small and medium-sized enterprises (SMEs). Future research directions are identified, emphasizing the potential of artificial intelligence-driven DTs, the adoption of edge computing, the development of scalable and modular platforms, and the necessity of supportive policy frameworks. By integrating DT technologies, bakeries can shift from traditional reactive energy practices to proactive, data-driven strategies, paving the way for greater competitiveness, operational excellence, and a sustainable future.

1. Introduction

The bakery industry holds a central position in the food manufacturing sector and is characterized by high energy consumption in varied production stages. Against the backdrop of rising global energy costs and increasing environmental requirements following regulatory policies promoting the use of more sustainable practices, this industry faces mounting pressure to enhance energy efficiency. Conventional energy management practices rely heavily on manual controls and reactive maintenance strategies, which may cause operational inefficiencies, higher costs, and a greater environmental footprint [1,2,3].

The operational systems of bakeries also undergo major overhauls nowadays. Increased consumer consciousness about the environmental impacts of food items and government campaigns promoting eco-friendly manufacturing practices, accentuate the need for bakery industry owners to go green from the energy side. Yet, the implementation of integrated energy management systems faces ongoing challenges, owing to the complex nature of the manufacturing processes, batch size variability, and strict demands placed on the product’s quality and homogeneity.

In resolving these challenges, Industry 4.0 technologies have played a significant role in the development of innovative solutions to accelerate industrial processes [4]. Of the numerous technologies, DT systems have been envisioned as revolutionary solutions. A DT refers to the digital replica of a physical object, system, or process that is continuously updated with real-time data received from the use of sensors and measuring instruments [5,6,7]. With the connection of cyber-physical systems to sophisticated data analytics, machine learning techniques, and simulation models, DTs enable virtual modelling, continuous monitoring, predictive analysis, and data-driven decision-making.

Within the context of bakery operations, the use of DT technology provides for all-encompassing energy consumption management, identification of inefficiencies, planning of production cycles, significantly reducing energy loss, increasing production adaptability, and enhancing the lifespan of machinery [8,9]. In addition, DTs can integrate renewable energy technologies like solar photovoltaic (PV) systems, wind turbines, and energy storage technologies, enabling bakeries to transition toward self-sufficient and carbon-neutral production systems [10,11]. The ability to simulate and optimize hybrid energy systems allows DTs to efficiently manage dynamic loads, participate in demand response programs, and increase the consumption of renewable energy sources. Various studies have highlighted the capability of DT technology to achieve high energy efficiencies in manufacturing environments [10,12,13], and hence it becomes especially interesting to bakeries for them to stay competitive while respecting high environmental standards. Additionally, DTs improve the reliability of assets using predictive maintenance processes, minimize the potential for unexpected disruptions, increase process transparency, and enable the transition to Industry 5.0, defined by human-centric, resilient, and sustainable manufacturing patterns [14,15,16]. Apart from the direct energy benefits, the use of DT technology ties into broad organizational objectives like ensuring the quality of products, compliance with food safety standards, and quick responses to market requirements [17,18]. By creating a DT of their operational processes, producers acquire better insight into the processes, allowing them to produce data-driven decisions and constantly optimize their processes.

This article discusses the transformative role of DT technologies in redefining energy management practices in the bakery industry. Specifically, DTs enable real-time energy monitoring, predictive control, and thermal process control, waste heat recovery (HWR), renewable integration, and dynamic energy use optimization, all fostering smarter and more efficient bakery operations. While DTs have become more known in manufacturing and energy-intensive industries, their direct application in the bakery industry, where thermal processes, energy variability, and sustainability goals intersect, has not been given due consideration. To the best of the authors’ knowledge, this is the first detailed review exclusively focused on the integration of Digital Twin systems for smart energy management in bakery processes.

To achieve the above objective, the present study employs a narrative and integrative review approach, which is highly appropriate to investigate frontier and interdisciplinary research areas such as digital twin-enabled energy management in food manufacturing industries. The reason why this particular methodology is used is based on the understanding of the dispersed and interdisciplinary nature found in literature across different fields, such as industrial engineering, energy systems, food processing technology, and digitalization. In this context, the use of a structured approach, in this case PRISMA, was not appropriate because the primary aim was not to provide quantitative outcomes, but to integrate conceptual models, experiential insights, and technological advancements. A thorough review of the literature was carried out using diverse databases, such as Scopus, Google Scholar, and IEEE Xplore, using specific search terms like “digital twin,” “bakery,” “energy efficiency,” “energy management,” “predictive maintenance,” “real-time monitoring,” and “Industry 4.0.” The primary aim of this review was to gather peer-reviewed studies that specifically discussed digital twin-based energy optimization, process control, and sustainable system design in the bakery industry and adjacent industries.

This research examines systematically the application of DT in the fields of real-time energy monitoring, predictive maintenance, waste heat recovery (WHR), and the integration of renewable energy sources. It also discusses the technological, organizational, and economic impediments hindering wider adoption, especially in the case of small and medium-sized bakeries. By carrying out a thorough synthesis of recent advances and challenges, this research hopes to provide useful insights for scholars, business professionals, and policymakers, thus enabling the development of strategic solutions for increasing the effective application of DT technology in the global baking business.

To guide this effort, the following research questions were developed:

• RQ1: How can digital twins optimize energy-intensive bakery operations?
• RQ2: What technological and organizational barriers limit DT adoption in bakeries?

2. Energy Consumption and Hurdles in the Bakery Industry

2.1. Energy Demand and Consumption in the Bakery Industry

The operational energy consumption in the bakery industry is significantly high due to the nature of the processes. The baking ovens are responsible for most of the energy expenditure, requiring precise and maintained high temperatures to obtain acceptable baking results [6,19,20]. Proofing chambers, which are essential to supply warm and humid conditions to facilitate dough fermentation, also drive energy consumption higher, thus demanding regulated temperature and humidity levels [21,22].

Refrigerative and cooling equipment are a substantial part of a bakery’s energy needs. It is used to maintain dough integrity, store goods, cool products after baking, and aid in steady operation, hence a constant need for energy. Heating, ventilation, and air conditioning (HVAC) equipment, required for controlling indoor air quality, temperature, and humidity, also forms a part of overall energy needs [23,24,25]. In addition to the main equipment, secondary equipment like mixers, conveyors, packagers, and lighting also make significant contributions to energy usage in bakeries. Individually, each piece of secondary equipment might represent a relatively small proportion, but when added together, the influence is significant. Baking ovens have the largest energy demands because they experience constant heat and heat loss at their operating temperatures. Refrigeration and HVAC add to energy consumption when they are necessary for ensuring product quality, but they lack load response and require maintenance. Lighting systems and mixers, while small, are major energy consumers and are easily left out of audits, thus lowering the efficiency of waste heat recovery. Traditional bakeries do not have real-time energy monitoring, which prevents them from being able to manage proactively. Legacy equipment is inefficient and not compatible with new sensors, particularly for SMEs.

2.2. Inefficiencies and Challenges

Despite advancements in equipment design, many bakeries continue to use outdated machinery and traditional energy management practices. Inefficiencies result from a range of factors. Baking ovens experience high energy losses through excessive thermal escape, frequently caused by poor insulation or frequent door openings [26,27]. Additionally, proofing and cooling systems tend to operate on fixed schedules, resulting in energy waste during periods of no use.

Refrigeration systems without load-responsive controls often fail to work efficiently; at the same time, HVAC systems often run based on pre-programmed settings regardless of current needs, and they use too much energy. Poor maintenance practices, such as clogged filters and inoperative dampers, also enhance the inefficiencies. Additionally, inefficient use of WHR opportunities presents another major hindrance. The exhaust gases of ovens and boilers harbor recoverable thermal energy that, in most bakeries, goes unrecovered due to the lack of suitable recovery systems [28,29,30].

The challenges are compounded by the absence of efficient, real-time energy tracking systems. With no consumption data, it becomes challenging for the bakeries to identify inefficiencies and implement targeted energy savings measures. The need for more efficient use of energy and curbing carbon discharge further emphasizes the drawbacks of traditional methods. Economic constraints, especially for small and medium-scale businesses, inhibit the use of very efficient technologies, despite the recognized benefits [4]. Therein lies a critical need to shift to integrated, data-driven, and smart energy management systems, as reflected in the backing of DT technologies.

The use of DT technology in the bakery industry is still in its early stages of development, marked by the unavailability of immediate empirical studies. However, the literature in related areas of food processing provides a valuable foundation for further study, as shown in

Table 1. Energy, technical issues, and digital twin relevance are key systems in the bakery.

System/Component	Energy Role	Technical Barriers	DT Relevance	Source
Baking Ovens	Highest energy consumer; requires constant high temperatures	Heat loss due to poor insulation and frequent door openings	DT can simulate thermal profiles, track energy leaks, and optimize baking cycles	[24]
Proofing Chambers	Maintain warm, humid conditions for fermentation	Inefficient temperature/humidity control; operate on fixed schedules	DT can enable predictive control of humidity/temperature based on the fermentation stage	[31]
Refrigeration & Cooling	Continuous operation for dough storage, product cooling	Lack of load-responsive control; high base load energy usage	DT enables real-time monitoring of cooling demand and energy loads	[32]
HVAC Systems	Air quality and climate regulation in production areas	Overuse due to static settings; poor maintenance (e.g., clogged filters)	DT can model airflow patterns and predict maintenance needs (e.g., dirty filters)	[33]
Auxiliary Equipment	Includes mixers, conveyors, packagers, and lighting	Often ignored in energy audits, but cumulatively significant	DT can include auxiliary loads in virtual audit and usage prediction	[34]
WHR	Potential energy recovery from the oven and boiler exhaust	Rarely implemented; absence of recovery systems and thermal reuse strategies	DT can simulate recovery potential and optimize reuse strategies	[35]
Energy Monitoring Tools	Track and analyze consumption data	Often missing in traditional bakeries, the lack of real-time data impedes efficiency.	The backbone of DT provides essential data for digital modeling and optimization.	[9,36]
Legacy Equipment	Used widely in SMEs	Obsolete, energy-inefficient machinery; difficult to retrofit with sensors	Hard to integrate directly into DT, but can be monitored indirectly through external sensors	[37,38]

. Eminent areas with development potential include optimizing bakery processes, increasing energy efficiency, and supply chain management made possible through the technologies of DTs. Future studies are needed to adapt the technological advances to the specific needs and challenges of the bakery industry.

3. Digital Twin for Energy Management in Bakery Industries

Figure 1 outlines a standard bakery industry production workflow, which includes key stages such as ingredient preparation, mixing, fermentation (proofing), shaping, baking, cooling, and final packaging. Each stage involves distinct thermal, mechanical, and energy-intensive processes. For instance, ovens require high and consistent temperatures during baking, while proofing chambers demand controlled humidity and warmth. Understanding these interconnected steps is essential to contextualize how DT systems can be designed to simulate, monitor, and optimize energy flows and operational efficiency across the entire production line.

3.1. Industrial Applications of DT Technology

The use of DT technology has found widespread use in all walks of life, revolutionizing the management of systems, processes, and assets at a fundamental level. For the manufacturing industry, DTs enable real-time process optimization, predictive maintenance, and resource allocation [39,40]. For smart factories, DTs optimize the production processes, reduce material loss, and enhance the flexibility of the operation. DTs give managers the ability to simulate different production scenarios, the option to adapt parameters dynamically, and ability to predict system behavior under different conditions.

In the aerospace industry, DTs take the responsibility of monitoring the operation of engines, airframes, and critical systems for missions, thus enhancing dependability and extending the life of the assets [41,42,43]. DT-based predictive maintenance practices have significantly reduced unexpected failures and related maintenance costs and thus improved operational efficiency. Similarly, the automotive sector widely utilizes DTs for car design, collision simulation, and predictive diagnosis [44,45]. Virtual prototyping hastens development timescales, optimizes vehicle performance, and encourages innovation.

DTs in the energy sector enhance the generation of power, enable the management of smart grids, manage renewable energy systems, and optimize the entire system’s efficiency [46,47]. Utilities use the technologies to simulate network operation under different loads, thus enhancing resilience strategies and enabling real-time in-field adjustments. The numerous applications in the industry demonstrate the versatility and transformative qualities of DT technologies. Effective applications in other industries provide insight into the possible adaptation and use of the technologies to optimize bakery industry processes.

3.2. Adaptation of Digital Twin in Bakery Operations

The implementation of DT technology in bakery industry operations requires the creation of models that can successfully capture the unique characteristics of bakery production processes. The environments in bakeries feature complex interactions between thermal, mechanical, and electrical systems, including baking ovens, proofing cabinets, HVAC systems, conveyor systems, and refrigeration systems.

To design effective DTs for bakeries, it is critical to build models that reflect the thermal behavior of ovens, the humidity levels within proofers, the cooling needs of refrigeration systems, and the airflow patterns of the manufacturing environment. The development of such sophisticated models requires the combination of extensive sensor networks, Internet of Things (IoT) devices, and past operational data [48,49]. Through virtual modeling, bakeries gain the capability to track energy consumption in real-time, detect irregularities during operations, and simulate changes without disrupting physical production processes. A DT, for instance, can predict the impact on energy usage and product quality arising from adjustments in oven temperature settings, changes in batch loading methods, or proofing time adjustments. These simulations lead to energy optimization, process efficiency improvements, and more product quality consistency. DTs also enable virtual commissioning, allowing verification of production processes digitally before any physical modifications are made. This significantly reduces the commissioning time, lowers risks, and guarantees more reliable production outcomes.

3.3. Real-Time Monitoring and Predictive Maintenance

Real-time monitoring forms the basis of DT applications in energy management in bakery industry environments. Sensors placed in the machinery in the bakery collect continuous streams of operational data, including temperature, humidity, pressure, vibration, and energy consumption. This data constantly streams into the DT and hence provides a real-time representation of the system’s operation, enabling continuous condition monitoring of important bakery equipment such as ovens, proofers, and refrigerators.

Such monitoring enables quick identification of deviations from optimal operational conditions. For example, the temperature profiles of ovens might indicate burner failures, insulation degeneration, or the blockage of exhaust routes. By comparing real-time operational parameters with the virtual replica, DTs can determine anomalies and propose prompt remedy measures. The use of predictive maintenance capabilities further maximizes bakery industry operations. With the evaluation of both historic and real-time operational data, DTs can foresee potential machinery failures [50,51]. Early knowledge of such anomalies enables prescheduling of maintenance, thus reducing unplanned downtime and related maintenance costs. Furthermore, predictive maintenance practices also help extend the life of critical assets and assist in the management of spare parts stock, hence ensuring that the bakery industry operates with greater reliability and proficiency.

The core value proposition of the adoption of DT technology lies in its ability to generate actionable insights that directly improve and optimize operations in the bakery industry. Expected outputs from a well-designed DT system include: (i) real-time visualizations of energy consumption in all main subsystems of the bakery sector; (ii) predictive alerts about maintenance needs and equipment anomalies; (iii) simulation-based recommendations for best the baking schedules aligned with renewable energy availability or variable energy tariffs; and (iv) analysis of process inefficiencies and key performance indicators (KPIs) such as energy consumption per batch and the thermal efficiency of ovens. By continuously updating these outputs with the incorporation of sensor information and artificial intelligence models, DTs not only improve decision-making processes but also facilitate automation, load scheduling, and energy balancing in line with production constraints and sustainability goals.

Figure 2 illustrates the general framework of a DT system designed for bakery energy and process management. The framework highlights the flow of data from physical sources, including equipment, the production line, and environmental conditions, into a centralized cloud and database layer, where sensing and data aggregation occur. This synchronized data is then fed into the simulation model, which serves as the analytical core of the digital twin. The model drives advanced analytics across key operational domains such as condition monitoring, predictive maintenance, dynamic scheduling, quality control, and energy management. The control feedback loop, represented by a dashed line, indicates how insights from analytics are utilized to optimize real-time operations in the physical bakery system. This figure serves as a conceptual foundation linking the bakery context to the digital twin functionalities elaborated in subsequent sections.

3.4. Renewable Integration and Energy Optimization

DTs play a significant role in enabling the accommodation of renewable energy sources in bakery industry operations. DTs enable modeling and optimization of the interactions between energy storage systems, traditional energy needs, and solar PV systems. By using actual production data and meteorological forecast data, DTs excel at predicting solar energy output and providing recommendations for production schedules to maximize the usage of renewable energy [52,53]. When renewable energy output is in excess, DTs optimize energy usage by adjusting energy storage systems or providing recommendations for energy-demanding activities to be scheduled to maximize energy usage. DTs also enhance the operating effectiveness of WHR systems. They enable effective heat capturing and redistribution of waste heat from ovens and HVAC systems using real-time simulations and redirecting the heat to applications like water preheating, heating spaces, or aiding other bakery industry processes [54,55]. DTs also continuously monitor energy consumption patterns and advise load-shifting measures, thus empowering demand response participation while dynamically varying overall energy expenditure. This innovative and advanced energy management mechanism substantially supports both operational sustainability and economic feasibility.

3.5. Power Converters and Their Role in Digital Twin-Based Energy Management Systems

The power converters are key elements for the implementation of DT systems, which facilitate better energy management for the bakery industry. Power electronic converters, consisting of inverters, DC-DC converters, and grid-connected controllers, are instrumental toward achieving the efficient distribution of energy for modern-day bakery industry operations that increasingly use renewable sources of energy as represented by solar PV systems [56]. Power converters are crucial for voltage level control, meeting the requirements for the grid, and opening the doors for dynamic energy reallocation between different sources, energy storage systems, and electrical loads.

Within a DT system, power converters undergo digital representation and real-time monitoring to improve energy efficiency, reduce losses, and apply predictive control strategies that align with price changes and production schedules [57]. DTs enable the simulation of converter behavior over a range of load conditions, thermal conditions, and grid conditions. Simulation plays a crucial role in the prediction of thermal stress, switching losses, and inefficiency under real-world operating conditions [58]. In establishments like bakeries, whose energy demand from ovens and HVAC systems is both significant and variable, the use of digital technologies to manage converters makes it easier to utilize stored renewable energy at peak demand times. Smart coordination between sources of power and their use through power converters optimizes high-level strategies such as load shifting, peak shaving, and grid-responsive demand response, each one intertwined to foster sustainable and efficient company operations [59]. Moreover, power converters are crucial for maintaining power quality and reliability, particularly for sensitive bakery equipment. Voltage sags, harmonic distortions, and frequency deviations can adversely affect product quality or damage equipment [60]. By incorporating converter dynamics into the DT environment, bakeries can ensure compliance with power quality standards. Additionally, the integration of fault detection algorithms within the DT allows the early diagnosis of converter anomalies, thereby reducing the risk of unplanned equipment downtime [61]. The use of power converters in distributed systems significantly enhances the energy efficiency, system reliability, and overall economic sustainability of bakery businesses [62].

3.6. Cross-Sector Insights

Insights from the food processing, brewing, and pharmaceutical industries contribute meaningfully to the implementation of DTs in bakery industry processes. The use of DTs in food manufacturing has been seen to optimize thermal and fluid characteristics, manage the packaging processes, and maintain compliance with food safety laws, all while achieving significant energy efficiencies [63,64].

Brewery operations have increasingly been adopting DTs to harmonize brewing processes with times of maximum PV production, thus reducing the usage of grid electricity and maximizing the use of renewable energy sources. DTs in the pharmaceutical industry enabled the strict control of environmental parameters in cleanrooms to guarantee compliance with regulations while maximizing the potential for energy efficiency. Such experiences emphasize the importance of accurate data acquisition methods, continuous model validation strategies, strict protection protocols, and interdepartmental collaboration. By adopting best practices in such areas, the bakery industry will accelerate the efficient operation of DTs and maximize the benefits of smart energy management.

4. Intelligent Energy Control in Bakeries with Digital Twins

4.1. Bakery Energy System Modeling

In bakery operations, energy system modeling enabled by DTs allows for the creation of complex and adaptable digital models of the main energy-consuming assets. The models mimic essential operating conditions, such as temperature patterns, airflow patterns, energy input levels, exhaust gas compositions, and baking times [19,65].

Comprehensive modeling permits bakeries to track energy flow across their operations, thus enabling the detection of activities that consume too much energy and areas where energy is wasted. For instance, direct analysis of temperature profiles across ovens quickly identifies high heat losses due to inefficient insulation or irregular combustion cycles. The combination of advanced physics-based modeling and machine learning programming successfully records steady-state and variable behavior characteristics of bakery systems and improves the insight into process dynamics [66,67,68]. By matching virtual models to historic data and actual sensor data, the DT is continuously refreshed to properly reflect the physical condition of the bakery plant, leading to improved predictive capabilities. The ongoing real-time synchronization ensures that energy conservation opportunities are based on sound simulations and not assumptions. Moreover, comprehensive mapping of energy flow helps determine areas of inefficiency in equipment, processes, and facilities, and hence identifies large opportunities for optimization.

4.2. Baking Process Optimization

Optimization of bakery processes using DTs requires the simulation and analysis of various production schedules, oven loading methods, and baking conditions to conserve energy while preserving the quality of products [64]. DTs enable bakeries to analyze many scenarios related to the baking process, such as changes to oven loads, variations in the duration and temperature of baking, and coordinating bakery processes with energy tariff patterns. The simulations analyze the effect of prolonged baking cycles, non-standard schedules of baking, and batch grouping on energy consumption and process efficiency.

In addition, the use of artificial intelligence algorithms makes the DT capable of autonomously recommending ideal bakery parameters that optimize energy use and product uniformity. Experimental studies have shown that smart control of the baking profile using DTs can lead to substantial energy savings without compromising the quality and consistency of the end products. DTs also adapt the protocols of baking to the ambient temperature and humidity and to variations in energy prices throughout the day, thus keeping operations both flexible and effective.

4.3. Predictive Fault Detection and Maintenance

Predictive fault detection is an important function enabled by energy management systems leveraging DT technology. By regularly comparing actual operating data to known performance norms, DTs are effective at detecting anomalies that can signal future malfunctions, such as unusual temperature fluctuations in ovens, inefficient compressor use in refrigeration systems, or air flow irregularities in HVAC systems [54,69].

On the detection of anomalies, the DT system sends predictive alerts regarding maintenance, thus enabling bakery staff to attend to issues before they develop into full-blown failures [70]. This approach essentially reduces unplanned downtime, extends the working life of the machinery, and reduces the cost of maintenance. Empirical evidence shows that DT-informed strategies of maintenance significantly reduce equipment downtime and the types of maintenance spending [71]. The integrated root-cause analysis capability within the DT also determines the root cause of faults and suggests appropriate corrective actions, thus enhancing the effectiveness and efficiency of the maintenance regime. Predictive modeling also improves the ability of bakeries to better regulate their spare parts inventory, ensuring that important parts are available when they are needed.

4.4. Waste Heat Recovery Management

WHR holds significant potential for energy saving across the bakery industry, particularly if one considers the high thermal loads associated with ovens and proofing systems. DT implementation makes it easier to optimize WHR systems by dynamically simulating exhaust gas behavior, heat exchanger efficiency, and thermal storage capacities [72].

By applying predictive modeling and ongoing monitoring, DTs fine-tune several parameters such as flow rate, heat exchange surface area, and recovery time to meet production needs. For instance, the DT predicts times of high availability of waste heat and coordinates its use for preheating air for ovens, proofing chambers, or the hot water system supply. In addition, DTs enable the pre-commissioning of WHR systems for bakeries to analyze several system configurations and control approaches without actually installing them. This substantially reduces the risks associated with investment while maximizing returns. Additionally, renewable thermal systems, such as solar thermal collectors, can also be modeled so that overall thermal energy recovery is increased.

4.5. Sustainable Smart Bakeries

The main aim of establishing DTs is to create sustainable and smart bakeries. DTs consolidate the end-to-end bakery environment by linking internal processes and external factors, for example, energy prices and renewable energy projections [73,74]. By conjoining energy models, production planning, and external factors, bakeries become intelligent and responsive entities. For example, whenever there is high solar energy production, the DT can schedule energy-consuming processes at times of high sunlight. Where there are high grid tariffs, it can plan production cycles that are energy-efficient or use stored thermal energy.

In addition to this, DTs make possible the strategic planning and optimization of carbon-neutral bakery industry operations by simulating large renewable energy systems, including solar PV systems, biomass systems, heat pumps, energy storage systems, and demand management techniques. This holistic approach makes it easier for bakeries to attain international sustainability standards like ISO 50001 certification [34] and corporate net-zero goals [75,76]. Empirical research suggests that energy management systems based on DT technology allow bakeries to attain significant carbon emissions savings, which highlights the revolutionary potential of this technology across the food industry [77]. Additionally, future smart bakeries can enhance their DTs to support interoperability across blockchain-based platforms, thus offering auditable evidence of sustainability claims for both customers and authorities.

Table 2. Overview of digital twin applications in the bakery industry.

Application Area	Key Objectives	Methodologies Used	Outcomes	Remarks	Sources
Bakery Industry Digitalization	Review of digital innovations in the bakery sector	Systematic literature review	Bakery Industry Digitalization	Emphasizes the need for more studies in bakery-specific DT applications	[4]
Baking Oven Optimization	Design, development, management, and optimization of baking ovens	3D CAD modeling, sensor integration, high-fidelity simulations	Enhanced control over baking parameters; improved product quality	Focused on industrial-scale ovens; emphasizes the importance of simulation in DT development	[78]
Baked Goods Manufacturing	Productivity enhancement through digitalization	Smart sensing technologies, data analytics	Improved manufacturing efficiency; reduced downtime	Highlights the role of smart technologies in bakery production	[79]
Food Processing Industry	Review of DT applications across food sectors	Literature review, case study analysis	Identified gaps in DT adoption in the bakery sector; proposed future research directions	Emphasizes the need for more studies in bakery-specific DT applications	[80,81]
Thermal Food Processing	Improve quality and safety through DT	COMSOL® Multiphysics modeling, microwave heating simulation	Accurate prediction of temperature and moisture loss; energy-efficient cooking	While not bakery-specific, it offers insights applicable to similar thermal processes in baking	[82]
Bakery Industry Digitalization	Review of digital innovations in the bakery sector	Systematic literature review	Bakery Industry Digitalization	Emphasizes the need for more studies in bakery-specific DT applications	[4]

summarizes key applications of DTs in the bakery industry, highlighting objectives, methods, outcomes, and research gaps.

5. Improvements, Challenges, and Future Perspectives

5.1. Digital Twin-Driven Improvements

The use of DT technology for the baking industry encompasses a host of benefits that are both tangible and intangible. Of particular prominence among these is the impact on energy consumption reduction. Research suggests that the use of DT models ensures lowered energy usage for production processes within factories through measures like live monitoring, energy optimization that adapts to conditions, predictive measures for maintaining facilities, and improved scheduling of processes [46]. Another important benefit provided by DTs is better reliability of assets and longer working lives. By predicting potential breakdowns ahead of time, bakeries could also move from reactive to predictive maintenance, thus decreasing costly downtime and increasing their machinery’s durability. Moreover, the application of DT technologies greatly improves the transparency of operations [5,83]. By gathering and presenting holistic operational data, bakeries gain essential insights regarding process inefficiencies, energy consumption patterns, and production capacity limitations. This increased transparency supports evidence-based decisions, facilitates performance assessment, and supports efforts toward continuous improvement.

In addition, DT technologies are vital to driving sustainability efforts. By optimizing energy consumption, reducing waste, and using renewable energy sources, bakeries can substantially reduce their carbon footprint while staying consistent with global sustainability goals, especially the United Nations SDGs [84]. Moreover, the operational flexibility provided by DTs makes bakeries responsive to changing markets, changing consumer needs, and changes to regulatory protocols, thus continuously enhancing their competitiveness and resilience.

In addition to the benefits of energy efficiency and operating optimization, DTs are important for improving product quality [85]. Through advanced simulation methods and predictive analytics, bakeries can simulate the effects of variations within ingredients, the conditions of baking, or environmental factors and thus ensure uniform product quality while maximizing the use of resources. DTs also enable compliance with food safety laws by monitoring vital control points and maintaining environmental parameters within regulatory bounds [63,86].

From an economic standpoint, investment in DT technology yields the potential for huge returns within a prolonged period. The cost savings derived from reduced energy consumption, reduced maintenance, increased lifespan of devices, and increased productivity during operations collectively offset the cost of installation [87,88]. Additionally, bakeries that invest in digital technologies stand to enhance their brand image, hence attracting eco-conscious consumers and strengthening partnerships with sustainable supply chain partners.

5.2. Adoption Constraints

5.2.1. Technical Barriers

The development and realization of accurate and effective DTs require a large amount of investment in digital infrastructure, including high-fidelity sensors, secure communications, cloud computing facilities, and cybersecurity [89,90,91,92]. For many bakeries, particularly small and medium-sized enterprises (SMEs), the upfront cost of these technologies is normally extremely high without the provision of external financing or incentives.

A further technical challenge lies in the creation of accurate digital replicas of complex bakery processes. DTs need to reflect physical equipment behavior under varying operating conditions, which requires extensive data collection, careful model calibration, and frequent validation processes. Poorly calibrated or flawed models would lead to unsuccessful optimization outcomes, thus compromising trust in the system. The protection of data and privacy remains a top priority. As DTs continuously transmit critical operating information across networks, they are particularly vulnerable to threats from the outside. To maintain operating integrity and keep business confidential, it is essential to implement tight security controls such as secure access protocols, sound encryption, and continuous threat monitoring [93,94,95].

5.2.2. Organizational and Operational Challenges

Organizational preparedness is another significant challenge. Leveraging DTs requires bakery workers to have digital literacy, and that includes the capacity to analyze operating data and use advanced analytics tools. Thus, corresponding staff training programs need to be provided along with the new technologies. Organizational resistance to new technologies is also commonly seen. Staff may fear for their job security or may not be willing to use new and unfamiliar tools. To overcome this, bakeries should establish an innovative culture that showcases how DTs augment human expertise rather than replacing it. The integration of DTs into existing legacy devices and deployed enterprise resource planning (ERP) systems can be complex [96]. Many older systems at bakeries do not have up-to-date data interfaces, thus necessitating adaptations or the use of middleware solutions. Moreover, the use of irregularly communicating devices and software applications from different vendors also complicates the process [97]. Scalability issues are also a cause for worry. Most existing DT solutions are designed for industrial-sized facilities. A need exists to create modular and scalable products for SMEs to bring DTs into broader usage in the bakery sector.

5.3. Future Research Directions

Future research activities need to overcome these challenges to unlock the full potential of DT technology within the application domain of energy management in bakeries. The development of AI-driven DTs is, first and foremost, a potentially revolutionary area of research. By incorporating machine learning into DTs, these systems can become adaptive and learn over time, enabling the dynamic adjustment of operational policies in response to changing production conditions and real-time data [98,99]. Second, advancements in the area of edge computing are expected. By processing data at the device or facility level rather than sending all data to remote cloud servers, this strategy may reduce latency, improve privacy, and increase the responsiveness of DT systems. Combined with upcoming 5G networks, edge computing will enable the ultra-reliable and low-latency communications essential to real-time control of energy-demanding bakery operations [100,101].

In addition, research efforts need to aim at the design of modular, open-source DT frameworks tailored to small and medium-sized bakeries. The modular system frameworks are also expected to be cost-effective, interoperable with existing operational systems, and scalable according to facility size and capacity variations [102]. The investigation of hybrid energy system simulation and optimization within the DT system is an essential area of study. Integration of solar PV technologies, WHR technologies, battery storage devices, and intelligent grid interaction models will help bakeries achieve improved energy independence and better adaptability to increasing energy prices and environmental volatility.

Incentive structures and regulatory contexts will be important for encouraging the use of DT technologies. Governments and industry bodies are advised to offer financial grants, tax credits, and technical assistance programs to support digitalization initiatives, especially for SMEs. In addition, multilateral collaboration on cybersecurity standards, data interoperability frameworks, and regulatory guidelines will increase confidence and facilitate the global spread of DT technologies.

Finally, a collaboration between engineers, computer scientists, energy specialists, and bakery technology professionals is essential. Interdisciplinary collaboration can potentially enhance innovation, enable knowledge transfer, and deliver holistic, pragmatic solutions that address the technological, operating, and organizational facets of the digital transition within bakeries.

6. Conclusions

The use of DT technology across the bakery industry marks a turning point toward smarter, more sustainable, and more robust production methods. Amidst increasing energy costs, heightened regulatory pressures, and the need for improved operating efficiency, DTs represent an all-encompassing solution that is well-suited to addressing these complex challenges. This analysis has demonstrated that DT systems create an integrated system for managing energy in real-time, supporting predictive maintenance, optimizing dynamic processes, and allowing the flexible use of renewable energy sources. By creating DTs of bakery industry operations, manufacturers can simulate different scenarios of operations, forecast energy usage, and identify inefficiencies ahead of time, thus enabling the prevention of potential decline to major issues. The tangible benefits in the form of significant energy savings, extended asset lifespan, increased process understanding, and improved sustainability returns validate DT technology as an indispensable enabler of high-performance bakery industry operations. Nevertheless, the full realization of the potential offered by DTs requires overcoming many technical, organizational, and financial hurdles. Significant capital is needed for investment in digital infrastructure, staff up-skilling, cybersecurity measures, and schema validation. Above all, modular, scalable, and SME-friendly frameworks must also be developed for DTs to allow bakeries of all capacities to participate in the digitalization process. Policymaking, industry-wide standardization processes, and interdisciplinary collaboration will play a crucial role in developing an enabling environment for large-scale deployment. Future research needs to focus on developing AI-driven DTs that are self-optimizing and utilize edge computing powered by 5G technologies to deliver real-time responses. Moreover, it is essential to come up with virtual energy management systems that mimic scenarios for integrating hybrid renewable energy resources. These developments will further intensify the use of DTs to enable greater energy efficiency, sustainability, and competitiveness in bakery industry processes.

For enabling efficient implementation, a priority agenda for upcoming research and implementation is suggested: (i) the development of AI-augmented DT systems designed to enable adaptive management and predictive analytics; (ii) the implementation of edge computing approaches to reduce latency and enable real-time decision-making; and (iii) the development of modular, low-cost DT platforms for specific use in SMEs. These approaches are technologically feasible with the recent evolution in the Internet of Things (IoT), AI, and 5G connectivity, and can be progressively achieved through pilot-scale demonstrations, public-private collaborations, and incremental digital enhancement of existing bakery infrastructures.