Revolutionizing industrial kitchen appliances: How data-driven supply chains enhance customer experience for U.S. food chains

 Corresponding author: Venkata Siva Prasad Maddala. Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0. Revolutionizing industrial kitchen appliances: How data-driven supply chains enhance customer experience for U.S. food chains Venkata Siva Prasad Maddala * Independent Researcher. Global Journal of Engineering and Technology Advances, 2025, 22(01), 116-128 Publication history: Received on 20 November 2024; revised on 28 January 2025; accepted on 31 January 2025 Article DOI: https://doi.org/10.30574/gjeta.2025.22.1.0254 Abstract In the U.S. food service industry, data driven supply chains are converging with advanced industrial kitchen appliances. Through technologies like Artificial Intelligence (AI), Internet of Things (IoT) and predictive analytics, businesses are improving operational efficiency and cutting waste while offering far superior customer experiences. However, traditional supply chains fail under the dynamic market demands, showing inefficiencies, delays and huge operational costs. These challenges are tackled by data driven approaches that deliver real time insights to predict maintenance, optimize inventory and build transparency. IoT and AI integrated smart appliances also improve the food quality and sustainability, personalization and speed in service delivery. Beyond customer preferences for quality and customization, this one address the industry’s transition towards the sustainable and ethical. Although high initial costs, data security and other challenges exist, these innovations are essential in a rapidly changing food service landscape as the potential benefits far outstrip any disadvantages. Keywords: Industrial kitchen appliances; Data-driven supply chains; Artificial Intelligence (AI); Internet of Things (IoT); Predictive analytics; Customer experience 1. Introduction 1.1. Background In today’s world, the U.S. food service industry depends on industrial kitchen appliances to fulfill the booming demand of production, quality, and scalability to feed the nation. They are the backbone of food preparation or food service — they guarantee it’s consistent throughout the organization and fast. Unfortunately, existing systems are usually inefficient, generally characterized by high energy consumption, lack of adaptability and frequent maintenance. However, with the progression of the industry, you get to see more and more need for advanced solutions which not just deal with these problems but fit into the needs of the modern customers of those days. Global Journal of Engineering and Technology Advances, 2025, 22(01), 116-128 117 Figure 1 Key technology elements related to Industry 4.0 for food processing Arrival of the Industry 4.0 technologies like Artificial Intelligence (AI), Internet of Things (IoT) and Big Data analytics has revolutionized the foodservice sector. These technologies allow industrial kitchen appliances to be made smarter by supplying real time operational insights and optimizing performance, as well as reducing waste. Moreover, data driven supply chains have become an important enabler of operational excellence. Using predictive analytics, real time monitoring and AI informed decision making, supply chains are now more able to react to dynamic market needs (Zatsu et al., 2024; Kang et al., 2024) providing that food chains consistently provide quality product and services. 1.2. Purpose and Scope In this article, we look at how the convergence of data driven supply chains and advanced industrial kitchen appliances are revolutionizing the U.S. food service industry. The importance of technology to solve the traditional inefficiencies, meet customer satisfaction and make the food production and delivery processes sustainable are highlighted. The scope of this discussion extends across three main dimensions: Industrial kitchen appliances evolve and adopt advanced technologies, data driven principles implement into supply chain management for higher efficiency and customer centricity, and benefits derived from these developments to the customer experience; reduced waiting time, higher product quality, and higher level of customization. The underlying intention is to offer insights on how these innovations ensure a foodservice ecosystem operating more agilibly, responsively and customer oriented (George, 2024; Guo, 2025). 1.3. Relevance of Data-Driven Approaches Modern supply chains are making data driven strategies indispensable, allowing businesses to use large data sets to make better decisions. In particular, this approach is relevant in the food service industry, where variability in customer preferences, supply chain disruptions or operational inefficiencies can degrade service delivery. This is one of the most transformative innovations this far using the IoT enabled kitchen appliances. Then, these devices can track and monitor your real time data like appliance usage, energy consumption, maintenance requirements, etc. An IoT connected oven, for example, can automatically set the cooking settings depending on what food is being cooked so as to achieve the best in quality and consistency. Predictive maintenance alerts also cut down to downtime help extend the lifespan of appliances (Khan et al., 2020) significantly decreasing operational costs. Besides improving appliances performance, data driven supply chain also improves the flow of goods and information. Predictive analytics is crucial to predicting customer preferences and demand patterns so as to preserve the optimal level of inventory. For instance, demand forecasting using AI could also ensure that ingredients are procured in the right quantities, preventing waste while still meeting customer demand. In this respect, this capability is especially useful in an industry where freshness and availability are important to the customers’ provision of services as evaluated by the Cherenkov et al. (2024). Global Journal of Engineering and Technology Advances, 2025, 22(01), 116-128 118 1.4. Industry Significance Use of data driven technologies and smart appliances is not a trend for U.S. food chains to control but its necessity to stay in the competition. Speed, personalization, sustainability – to many customers these are now things they expect when eating out. The demands are directly addressed by technological advancements in supply chains and kitchen appliances. For instance, with real time tracking of supply chain operations businesses are able to minimize the delivery time of product thus ensuring they have fresh ingredients at all times. Automated systems can also create unique dishes much more quickly than a human chef, which also creates a better service for guests in the kitchen. Additionally, these innovations also fit with an increasing movement toward sustainability by cutting food waste and energy used, which is appealing to the increasing number of environmentally conscious individuals (Zatsu et al., 2024). 1.5. Challenges and Opportunities Although data driven approaches and smart technologies are so potentially useful, they are not without their challenges. The initial investment costs and the need of skilled personnel to run complex systems can serve as a barrier for businesses to engage with these innovations fully. Additionally, problems of data security and privacy must be considered, to avoid the possibility of leaking the sensitive data. The opportunities however are far greater than the challenges, however. The adoption of these technologies however is expected to accelerate as more businesses recognize these long term benefits of efficiency, customer satisfaction and sustainability. To achieve the full potential of data driven supply chain and smart kitchen appliance, industry leaders must invest strategically on training, infrastructure and cybersecurity (George 2024, Ghodake et al. 2024). 2. The Evolution of Industrial Kitchen Appliances 2.1. Historical Context For over a century, industrial kitchen appliances have been an essential part of the food service industry by supplying the equipment for mass cooking of meals. During the rise of urbanization in the 20th century when the demand for efficiency in preparing food grew, early industrial appliances, like ovens and refrigerators, were developed. However, these appliances were limited in their capabilities, frequently using high energy consumption while needing manual maintenance frequently. As the industry of the 21st century repeated the model of global supply chains with ever increasing complexity, and customers shifted their expectations, the require for smarter, more flexible industrial kitchen technologies began to arise. Precision cooking, real time monitoring, and sustainability couldn’t be met by traditional systems. In an effort to integrate automation, connectivity, and a data driven decision making process, food chains initiated the transition towards Industry 4.0 technologies (George, 2024). 2.2. Integration of Smart Technologies Figure 2 IoT enabled Smart Kitchen Global Journal of Engineering and Technology Advances, 2025, 22(01), 116-128 119 Industrial kitchen appliances have fundamentally transformed using Industry 4.0 technologies, including Artificial Intelligence (AI), Internet of Things (IoT) and Big Data analytics. These smart appliances are capable of data collection and analysis and communicating with one another to perform real time optimization of both operations and performance. IoT-Enabled Appliances: As IoT technology has been developed applications have been created through which your appliances can become interconnected, thus forming a networked kitchen. For instance, smart ovens can now adjust cooking temperature automatically depending on the food being cooked at a time so that it's always cooked in the same way, every time. IoT enabled refrigerators sense temperature and humidity levels to minimize spoilage and energy consumption as well (Khan et al. 2020). They also allow operators to get real time updates about when maintenance needs to happen or any operational problems. The predictive maintenance that this provides allows for lower downtime and lower costly disruptions in food preparation. Extending the lifespan of appliances in businesses results in significant operational savings by reducing repair costs (Zatsu et al., 2024). 2.2.1. AI-Powered Automation Building on the above, industrial kitchen appliances have also been revolutionized by AI to make intelligent decisions. The dishwashers, whereby can powered by AI analyse the level of dirt of utensils and adjust the water usage according to it, saving resources whilst keeping the hygiene levels intact. Automated cooking systems can also create intricate menus with very little hand help from human chefs, so they can concentrate more on creativity and innovation (Ding et al, 2023). 2.2.2. Advanced Sensors and Monitoring However, modern appliances are outfitted with the latest sensors, in cooking, for instance it may monitor temperature, pressure and ingredient quality. Using these sensors, these appliances operate under conditions that are optimal and therefore reduce error and improve food quality. Then, for example, fryers with oil quality sensors can alert when oil should be replaced, in order that meals are at all times cooked to the required standard (Prakash et al., 2023). 2.3. Data-Driven Supply Chains and Their Role The effectiveness of industrial kitchen appliances is magnified when combined with data driven supply chains. These supply chains operate on the back of technologies such as AI and predictive analytics when it comes to optimizing operations such as inventory management and logistics. More and more, smart kitchen appliances are accompanying inventory systems to keep track of the usage of ingredients and stock levels. The amount of food that is being prepared is measurable using IoT enabled devices which update inventory records automatically. The integration facilitates the business in the avoidance of overstocking or running out of essential ingredients hence they prevent waste and have seamless operations (Cherenkov et al., 2024). Using the data collected in kitchen appliances, we can predict future trends in demand. For example, if a problem is noticed where a fryer is consistently peaking during certain hours, supply chain managers can then adjust their procurement schedules to meet peak demand times. In addition to inventory management, predictive analytics helps businesses uncover preferences patterns that customers have and make the best use of it by tailoring their menus per the needs (Kang et al., 2024). Food chains can improve on collaboration and transparency if they share data with suppliers. On the application level, for instance, a real time update from an IoT enabled refrigerator regarding low ingredient levels will trigger automated orders to suppliers. This system lowers lead times and makes sure that fresh ingredients reach on time which in return raises overall customer experience (Sallam et al., 2023). 2.4. Benefits of Smart Appliances in the Food Industry The addition of smart technologies into industrial kitchen appliances provides unrealized benefits to food chains by improving efficiency and sustainability as well as customer satisfaction. The smart appliances cut down the need for a manual intervention and employees can concentrate on other important tasks. Faster and more accurate food preparation translates to greater throughput of kitchens during busy hours. For large scale operations, i.e, quick service restaurants, i.e, QSR, speed is the key differentiator and this efficiency is ideally suited for those operations (George, 2024). IoT and AI technologies on Energy efficient appliances that supports in decreasing resource consumption. For instance, on energy management, you can have AI helping you identify on optimal power usage of appliances based on your operational needs. Predictive maintenance also minimizes waste to make sure appliances run with high energy efficiency (Ding et al., 2023). By combining sensors and AI algorithms, food can be prepared to the highest of standards every time. Because precise cooking conditions are maintained, along with Global Journal of Engineering and Technology Advances, 2025, 22(01), 116-128 120 ingredient quality control, smart appliances ensure that businesses deliver superior products which fulfill the expectations of the customers (Prakash et al., 2023). Data driven insights can help accomplish diverse customer preferences. For example, automated cooking systems can automatically adjust recipes such as preparing low sodium or gluten free food to cater to particular dietary requirements; such personalized dining experience can be created (Cherenkov et al., 2024). Figure 3 Benefits of Smart Supply Chain Management 2.5. Challenges in Adoption Although smart industrial kitchen appliances have many advantages, implementation challenges exist and must be solved in order to implement on a large scale. Getting and managing smart appliances can be a prohibitively expensive process, especially in the case of small and medium sized food chains. Although the use of these technologies may result in long term savings, businesses tend to avoid adopting these technologies since the upfront cost can be pricey (Kang et al., 2024). Operating smart areas also requires personnel capable of controlling complex technologies: a workforce trained for these appliances. Ineffective implementation of these systems resulting from the lack of skilled personnel. Ramachandran (2024) states that businesses will have to spend on training programs for employees to equip the workers with skills to address this challenge. For example, sensitive data generated from IoT enabled appliances and data driven supply chains is immense. Protecting this data from breach is critical to safeguard against breaches that could erode customer trust and threaten operational integrity. To protect the systems, they need to apply rigid cybersecurity measures (Khan et al., 2020). Employees used to traditional methods may be resistant to the transition from traditional appliances to smart systems. Resistance to change is inevitable and change management strategies are needed to fight this resistance and make change adoption smooth (Guo, 2025). 3. Data-Driven Supply Chains: Enhancing Efficiency and Customer Experience 3.1. Understanding Data-Driven Supply Chains Data driven supply chains use technology to gather, analyze, and act on tremendous volumes of data to make live decisions and build predictive power. Different from traditional supply chains where manual processes and fixed models dominate, data driven systems leverage AI, IoT, Blockchain, and advanced analytics to maximize any point in the supply chain. Data driven supply chains are especially disruptive in the U.S. foodservice industry where it is all about fresher, more consistent, on time, delivery. By allowing food chains to know customer demand and therefore manage inventory and minimize waste while guaranteeing their customers quality products (Kang et al., 2024). Global Journal of Engineering and Technology Advances, 2025, 22(01), 116-128 121 3.2. Key Components of Data-Driven Supply Chains Figure 4 The relationship between food safety, big data, blockchain, and AI 3.2.1. Internet of Things (IoT) IoT devices are important for collecting and transmitting real time data along the supply chain. In the food industry, IoT enabled sensors are used to keep track of parameters like temperature, humidity and location for transportation and storage parts. For example, smart refrigeration, enabled by IoT, guard against out of acceptable temperature ranges of perishable products, hence preventing tremendous spoilage and waste (Khan, et al., 2020). Further, IoT devices increase transparency by providing end to end visibility to goods. This real time tracking allows businesses to track shipments, predict delays, and change their operations accordingly. If this level of insight can be reaped in food chains which are dependent on timing of services (Sallam et al., 2023) by maintaining service quality, then this can happen anywhere. 3.2.2. Artificial Intelligence (AI) The vast amounts of data generated across the supply chain are analyzed with AI driven algorithms that identify patterns and revealing insights to fuel informed decision making. For example, AI can use historical sales data and external factors—such as weather conditions—to predict how much to manufacture. The market intelligence helps businesses match their inventory levels with their customer needs so as to avoid overstocking and stockouts (Ghodake, Bansal, & Agarwal, 2024). It also determines the most efficient route or ways for delivering vehicles. This lowers the cost of transportation, cuts greenhouse gas emissions, and assures goods arrive at target customers in pristine form (Ding et al., 2023). Figure 5 AI for Optimizing Supply Chain Management Global Journal of Engineering and Technology Advances, 2025, 22(01), 116-128 122 3.2.3. Blockchain Technology Blockchain is a secured and decentralized way to record transactions and track movements of goods. Blockchain is helping to trace products in the food industry because of every step in the supply chain – from farm to fork – is recorded. By knowing the origin and authenticity of products, this transparency creates customers' trust (Khan et al., 2020). Consider blockchain technology that can trace one seafood from a fishing vessel to a restaurant and comply with sustainability standards and cut the risk of fraud. In an era where customers expect higher levels of accountability and ethical sourcing (Zhang et al., 2024), such capabilities are very valuable. 3.2.4. Predictive Analytics Historical and real time data is leveraged by Predictive Analytics to predict future trends and events. This capability is useful in supply chain management for proactively responding to potential disruption—such as supplier delays or equipment failures. For example, in predictive analytics machines can be made to scan patterns in equipment performance allowing the business to plan maintenance before breakdown (Cherenkov et al., 2024). 3.3. Enhancing Customer Experience through Data-Driven Supply Chains Food chains are able to offer personalized products and services based on customer’s preference using data driven insights. For instance, analytics help determine popular menu items in a certain region, and help businesses localize their offerings. Iliadi (2023) explains that the level of customization in this case improves customer satisfaction and loyalty. Supply chains that are thought to be optimized are able to provide ingredients and products quickly enough to decrease preparation times and increase service speed overall. Businesses are able to predict and manage peak demand periods in real time and with AI driven scheduling, thereby limiting delays and deliveries made on time to customers (Kang et al., 2024). IoT and Blockchain technologies help us integrate such that quality and safety of food products is assured in supply chains. For example, temperature reading during transportation prevents spoiled perishable items and blockchain records prove ingredients' authenticity and compiliance. Yet, as Sallam et al. (2023) emphasized, these measures help to create client belief and conviction in the brand. Customers of today are desiring more sustainable and ethical practices. Some businesses are using data to drive supply chains that allow them to track and decrease their environmental impact. For example, it can manage delivery routes to reduce fuel consumption, and it can also monitor energy consumption in industrial kitchens, using IoT sensors. Furthermore, the blockchain ensures ethical sourcing; here blockchain ensures that the sourcing is done from trusted suppliers who adhere to sustainability standards (Galanakis, 2024). 3.4. Case Studies 3.4.1. Case Study 1: AI-Driven Demand Forecasting One of the leading U.S. fast food chains put AI powered demand forecasting in use to enhance its Inventory Management. The system analysed sales history, weather and local event patterns in order to accurately predict demand for various menu items. It enabled the chain to keep enough stock on shelf to optimize profitability (increasing 8%), without unnecessary waste (15% reduction). 3.4.2. Case Study 2: Cold Chain Management using IoT. IoT enabled sensors helped a major seafood supplier to measure temperature and humidity levels on the way of supply. Operators were alerted in real time about any deviations, and they were able to respond right away. Reduced spoilage by 20% and fulfilled food safety regulations, which helped to restore the supplier's image of quality and reliability (Khan et al., 2020). 3.4.3. Case Study 3: Blockchain for Transparency A restaurant chain that moved from table to farm realized that blockchain can be used to increase transparency. We offered customers the opportunity to scan QR codes on their menus so they could view detailed information on the origin of each ingredient. As a result of this initiative, customer trust and loyalty was enhanced, leading to 25% rise in repeat business in six months of its implementation (Zhang et al., 2024). 3.5. Challenges in Implementing Data-Driven Supply Chains The benefits of data driven supply chains are big, but there are a number of challenges in implementing these systems in business. This integration of Internet of Things devices, Artificial Intelligence platforms and Blockchain technology requires substantial investment in infrastructure and training. These costs can be prohibitive for small and medium Global Journal of Engineering and Technology Advances, 2025, 22(01), 116-128 123 sized businesses (Ghodake et al., 2024). Connected devices can generate an enormous amount of data, and create security risks. They can compromise sensitive customer and operational information, resulting in financial losses and reputational damage. Results show that the risks associated with operations requiring a long synchronization period involve the highest frequencies while those that require a short period are more sensitive (Manna et al., 2016). In this case, businesses must put in place robust cybersecurity frameworks to curb the risk of compromise (Khan et al., 2020). Incorporating sophisticated technologies into currently existing supply chains can prove challenging and time consuming. To prevent disruptions, businesses need to make sure new systems fit in with their old infrastructure (Ramachandran, 2024). To realize effective data driven supply chains, a knowledgeable workforce with expertise on advanced systems is required. The training of employees on how to use AI, IoT, and blockchain technologies must be done but it can be time consuming and expensive (Guo, 2025). 4. Technological Innovations in Industrial Kitchen Appliances and Their Role in Data-Driven Supply Chains 4.1. The Intersection of Technology and Industrial Kitchens As the food industry undergoes a digital transformation, industrial kitchen appliances are leading the way, combining sophisticated technologies to enhance performance, efficiency, and connectivity. Smart ovens and automated fryers are now fitted with IoT sensors, AI driven analytics and smart automation to become integral parts of data driven supply chains. These appliances align kitchen operations with real time supply chain data allowing food chains to increase productivity, minimize waste, and deliver superior customer experiences. Driven by increasing demand for consistency and speed in foodservice, these innovations have widely been adopted by U.S. foodservice industry (Zatsu et al., 2024). 4.2. Core Technological Innovations in Industrial Kitchen Appliances 4.2.1. Smart appliances enabled with IoT IoT revolutionized industrial kitchen appliances through real time monitoring, diagnostic and control. For instance, smart ovens fine tune cooking times and temperatures by recipe to achieve uniform quality across all these locations. For example, IoT connectivity also enables predictive maintenance by sighting possible problems before equipment fails. This minimizes downtime and repair costs, guaranteeing zero disruption in operations (Ding et al., 2023). Example: A food chain from the U.S. that integrated an IoT enabled fryer designed to monitor oil usage/maintenance patterns. By ensuring consistent frying conditions this system not only improved food quality (Sallam et al., 2023), but also reduced oil wastage by 25%. 4.2.2. AI and Machine Learning Integration Machine learning algorithms are used by AI powered appliances to optimise operations. For example, AI driven grills can pick up on cooking habits and adjust settings to obtain temperature accuracy thus removing the human error. AI makes inventory management better by using data from connected appliances to forecast ingredient usage and automatically reorder supplies, too. It guarantees that kitchen has all ingredients required complemented with nonoverstocking of materials which causes wastage (Sachani et al., 2021). 4.2.3. Robotics and Automation Thanks to automation, industrial kitchens are now much more efficient, as repetitive tasks such as chopping, stirring, and assembling ingredients can all be taken over. AI and computer vision robots are able to do complex tasks like — plating dishes — with much precision. Additionally, automated systems not only reduce man's part in the hazardous activities but also minimize labor cost and enhance safety (Guo, 2025). Example: One worldwide food chain employed robotic arms for burger assembly in order to maintain with predictable quality, and cut down on preparation times of 30%. This way, staff could concentrate on customer service and other value-added activities (Ramachandran, 2024). 4.2.4. Blockchain For Transparency To track what ingredients and appliances have been through, blockchain technology is being used. For instance, blockchain could follow the maintenance history of kitchen equipment to be sure safety standards are being met. Furthermore, blockchain increases traceability of ingredients as it improves food chains’ ability to ensure the supply’s source and quality, which is particularly necessary to handle customer issues over food safety and ethical sources (Khan et al., 2020). Global Journal of Engineering and Technology Advances, 2025, 22(01), 116-128 124 4.3. Benefits of Technological Innovations in Industrial Kitchens Connected kitchen appliances simplify your operations by automating the most monotonous tasks and optimizing the use of energy. For instance, smart sensors in energy efficient appliances may adjust power outtake with respect to demand, lowering utility costs (Galanakis, 2024). The uniformity of preparation and cooking is anyways maintained, because of smart appliances that take care of the same regardless of where you are or who are the operators. Consistency is key for food chains wanting to create a standardized customer experience across multiple outlets (Iliadi, 2023). Appliances can dynamically adjust based on real time supply chain data integration. For example, if there is a delayed delivery of ingredients, schedules can be adjusted automatically so that food is not over prepared, or spoils (Kang et al., 2024). Technological innovations lead to sustainability by cutting down food waste, minimizing energy usage, and encouraging ethical sourcing. For example, automated portion control systems eliminate precisely and sufficiently till leftovers (Cherenkov et al., 2024). 4.4. Challenges in Adopting Advanced Kitchen Technologies Smart appliances and automation systems can be quite an expensive initial investment, particularly for small food chains. Costs of these include equipment costs as well as training the staff members and integration of these with the already existing systems (Ghodake et al., 2024). But integrating advanced kitchen technologies with supply chain systems can be complicated and laborious. Compatibility with existing legacy systems must be ensured in conjunction with different departments at businesses (Sallam et al., 2023). Many appliances connected to an Internet of Things are vulnerable to cyberattack because they generate and transmit high volumes of data. Businesses who adopt these technologies need to ensure data security and its relevance to privacy regulations (Khan et al., 2020). 4.5. Case Studies 4.5.1. Case Study 1: IoT-Enabled Smart Kitchens An IoT solution for an oven was developed by a global pizza chain, which adjusted baking times according to real time demand data. The energy consumption for this system was reduced by 15% while improving throughput during peak hours, as a result of customer satisfaction improved (Ding et al., 2023). 4.5.2. Case Study 2: Inventory Management with AI AI-powered appliances were used by a fast-casual restaurant chain to analyze usage patterns of the ingredients. This system automatically placed orders for supplies, thus leading to 20% savings in inventory costs, and 40% reduction of stock out (Ramachandran, 2024). 4.5.3. Case Study 3: Food Preparation using Robotic Automation At a time when a major fast food chain is deploying robotic fry stations to manage through high-demand periods, the applications are not limited to advanced robotics, but to everyday technology as well. Sachani et al. (2021) reported that the robots ensured consistently good quality and reduced preparation times, thus increasing the order fulfillment by 25% when there was a peak hour. 4.6. Future Directions in Kitchen Technology You may have robots and AI systems taking over your industrial kitchen now, from preparing food to cleaning. This would decrease labor costs by an extreme and it would also improve consistency (Guo, 2025). With AI and IoT more sophisticated predictive maintenance can be implemented further reducing downtime and extending the lifespan of the equipment. Real time monitoring along with machine learning models will be developed to predict failures with high accuracy (Zatsu et al., 2024). Kitchen technologies will become increasingly integrated within data driven supply chains allowing for easy communication and coordination throughout the ecosystem (Kang et al. 2024).