Smart Domain: Smart Manufacturing

This paper aims to investigate how a firm’s supply chain design is influenced by lean operations. Firms across the globe are experiencing increased pressure to optimize their return on investment from their supply chains. Recent events such as the Covid-19 pandemic and resulting in disruptions in supply chains across the world, highlighting risks inherent in interconnected supply chains of the modern era. In addition, global firms are adopting novel ways of maximizing their returns such as platform business models, integrating their supply chains both horizontally and vertically. Both firms and individual are increasingly pressured by rising costs of living, translating into the need for firms to reduce the cost of their goods and services. This has led to increased uncertainty in supply chain risks, these include higher than usual lead times for inventory, costs associated with movement of inventory and inventory acquisition costs. As firms across the globe experience this supply chain pressures, the competitive landscape requires. The proliferation of e-commerce has changed the world of retail, translating in greater. Block chain provides for increased visibility of up and downstream supply chains, providing firms with the visibility to navigate the uncertainty. Social media is also introducing new ways of connecting to supply chains, facilitating.

The digitalisation of scheduling transforms aggregate planning in production systems, providing enhanced capabilities for aligning supply with demand through optimised scheduling and resource allocation. This shift is particularly relevant within the smart metering organisation iSwitch as it enables real-time data utilisation, improved forecasting, and heightened adaptability to uncertainties in dynamic production environments. A major digital tool facilitating this shift is the digital twin, which generates a virtual version of physical production processes, enabling ongoing monitoring and scheduling adjustments based on real-time data. Wang and Wu (2020) underscore the role of digital twins in managing uncertain factors that typically disrupt traditional scheduling, enhancing the accuracy of production planning. Additionally, Gao et al. (2022) demonstrate that digital twins support dynamic adjustments to production plans, mitigating the impact of unforeseen disturbances. Integrating advanced data analytics and machine learning into digital scheduling frameworks fosters more sophisticated decision-making processes. The emergence of Industry 4.0, encompassing IoT and cloud computing, has substantially enhanced data collecting and analysis capabilities, facilitating optimum scheduling models that adapt to intricate production situations (Deb & Gupta, 2023). Furthermore, digital twin-oriented models improve the predictability and efficiency of aggregate planning, as Chen et al. (2022) indicate, promoting intelligent operations within manufacturing by allowing real-time feedback adjustments in response to demand fluctuationsFinally, the literature suggests that digitalisation allows for a multi-objective approach to aggregate planning, incorporating economic, social, and environmental considerations (Rasmi et al., 2019). This paper adopts a systems perspective to explore how a digitalised approach to aggregate planning can address these challenges, guided by the following research question: “How can integrating digital technologies, such as digital twins and advanced analytics, optimise scheduling and resource allocation to improve aggregate planning outcomes within iSwitch’s production systems?”

In an era where operational efficiency and customer satisfaction are paramount, the intersection between product and service design and lean operations has emerged as a critical area of study. This paper investigates the dynamic relationship between lean operations decisions and the design of goods and services, focusing on how these two elements influence one another to optimize efficiency and drive value. Lean operations, with their core focus on waste reduction, continuous improvement and value maximization, significantly shape the design process by promoting simplicity, standardization, and customer-centric approaches. On the other hand, product and service designs are increasingly driven by the need for operational agility, flexibility, and the ability to quickly adapt to changing market conditions. Specifically, we delve into JIT’s role in synchronizing production flows with demand, allowing resources to be used precisely when needed thereby reducing holding costs, obsolescence and waste (Armstrong & Lee, 2021). The TPS principles, grounded in Ohno’s Seven Wastes guide the identification and elimination of non-value-added activities across product design and layout processes, fostering operational efficiencies that enhance product quality and customer satisfaction (Heyzer et al., 2020). The lean layout concept is examined to showcase its pivotal role in designing workspaces that support a smooth, continuous flow of materials, ultimately reducing transit times and streamlining workflows (Heyzer et al., 2020). According to Rother et al., (1999), and Abdulmaleka et al., (2007) lean inventory principles, emphases the reduction of excessive inventory and unnecessary storage, reflect the influence of product design on minimizing the resource footprint. Using case studies and examples, this paper underscores how service and product design decisions aligned with Lean and JIT principles can reduce lead times, improve quality, and enhance responsiveness to customer demands. In their paper, Womack & Jones (2003), details lean thinking as the antidote to waste, furthermore, discussing 5 lean principles that proved to be successful. The research also explores emerging challenges, such as the integration of disruptive technologies and increased customization, and how these factors complicate the balance between lean principles and product innovation. Ultimately, the paper argues that a symbiotic relationship between product design and lean operations is crucial for achieving operational resilience, sustainable growth, and competitive advantage in today’s dynamic business environment. It has been discussed the converting a batch and queue system to continuous flow with pull will yield positive results in operations. Oludapo et al. (2024), who provides foundational perspectives on lean operations, product design, and the critical role of process optimization in modern enterprises. Through a synthesis of foundational lean concepts and contemporary examples, this study underscores the necessity of viewing product and service design as a dynamic components within the lean operations strategy, essentially fostering resilience and adaptability in a competitive global market guided by the following research question:“How does the design of products/service influence /or is influenced by lean operations decisions?

In today’s fast-paced business landscape, effective short-term inventory scheduling is crucial for meeting production targets and responding swiftly to market demands. This planning process focuses on making optimal decisions over a timeframe of days to months, ensuring that businesses can fulfill customer orders while managing associated costs (Haddud & Khare,2020). Given the increasing demand uncertainty—often reflected in forecasts over lead times—digitalization plays an essential role in enhancing inventory management Both & Dimitrakopoulos,2020). This paper aims to reflect on how digitalisation can improve short-term inventory scheduling. Understanding Short-Term Inventory Scheduling: In operations management, short-term scheduling refers to the allocation of resources and the planning of tasks over a short time frame, usually focusing on hours days or weeks. It involves the timing of operations to achieve efficient movement of units through a system. It is a key component of production planning and control, ensuring that resources like labour, machines, and materials are efficiently utilised to meet immediate goals (Heizer, Render & Munson, 2020). Scheduling decisions range from efficient scheduling is how companies drive down costs and meet promised due dates (Heizer, Render & Munson, 2020). Short-term inventory scheduling aims to align operational decisions with long-term production goals. Accurate forecasting of demand during the lead time is vital, as discrepancies between actual demand and forecasts can lead to stockouts or excessive holding costs (Taghizadeh & Taghizadeh 2021). When demand exceeds expectations, businesses may struggle to meet customer needs, risking lost sales and damage to their reputation. Conversely, if demand falls short, companies incur additional costs from unsold inventory. Therefore, balancing inventory levels is critical. Pasupuleti, Thuraka, Kodete & Malisetty 2024). Key Aspects of Short-Term Inventory Scheduling: Demand Forecasting: This involves predicting short-term demand based on sales data, market trends, and seasonal factors. Digital tools can enhance forecasting accuracy by analysing historical data and identifying patterns, enabling businesses to anticipate changes in demand more effectively (Haddud & Khare, 2020). Reorder Points: Establishing reorder points is essential for maintaining optimal inventory levels. These thresholds trigger reordering when stock falls below a certain level, helping to prevent stockouts. Digital systems can automate this process, ensuring timely replenishment based on real-time data. (Haddud & Khare, 2020).Lead Time Management: Understanding lead times—the duration it takes for new stock to arrive—is crucial when scheduling orders. Digital tools enable organizations to account for lead times more accurately, allowing them to order inventory precisely when needed. (Haddud & Khare, 2020). Allocation: Efficiently distributing available inventory across different locations or sales channels is vital for meeting demand. Digitalization allows businesses to analyze where stock is needed most and allocate resources accordingly, ensuring that customer needs are met promptly. (Haddud & Khare, 2020). Monitoring and Adjusting: Continuously tracking inventory levels and adjusting schedules as needed is a hallmark of effective inventory management. Digital solutions provide real-time visibility, allowing businesses to respond swiftly to fluctuations in demand or supply chain conditions. (Haddud & Khare, 2020). The Role of Digitalization in Inventory Management. According to Niaz (2022) the transition from conventional paper-based methods to digital supply data integration represents a significant shift in inventory management practices. This evolution is driven by the need for real-time insights and the ability to overcome challenges such as space limitations and supply chain disruptions (Haddud, & Khare, 2020). By embracing digital tools, businesses can quickly adapt to market dynamics, optimize storage utilization, and enhance overall operational efficiency. Recent global events, such as the COVID-19 pandemic, have underscored the importance of robust inventory management systems. Companies that adopted dynamic inventory systems and collaborative supply chain approaches demonstrated greater resilience in navigating uncertainty (Taghizadeh, E., & Taghizadeh, 2021). Digital tools enable organizations to respond effectively to changes in demand and supply chain conditions, ensuring they remain competitive. Enhancing Forecasting Accuracy through Digital Tools: One of the most significant advantages of digitalization in inventory scheduling is the improvement of forecasting accuracy. Traditional methods often struggle to accommodate fluctuations in demand or supply chain disruptions (Both & Dimitrakopoulos,2020) By leveraging digital supply data from various sources—including suppliers, manufacturers, distributors, and customer behavior patterns—organizations can gain comprehensive insights into market conditions. Advanced technologies such as artificial intelligence (AI) and machine learning facilitate real-time data collection, processing, and analysis. These tools empower businesses to understand changes in demand and consumer behavior more effectively (Niaz, 2022) As a result, organizations can proactively adjust their inventory levels, minimising the risk of overstocking or understocking, both of which can lead to substantial financial losses. Supporting Just-In-Time Inventory Management: Digitalization also enhances the implementation of Just-In-Time (JIT) inventory management principles. JIT allows businesses to maintain lower inventory levels by ordering products only as needed, thereby reducing holding costs (Niaz, 2022). This approach is particularly beneficial in environments characterized by rapid demand fluctuations. By integrating digital supply data, companies can time their orders more precisely, ensuring that they receive products, when necessary, without excess inventory buildup Taghizadeh, & Taghizadeh, 2021). In conclusion, digitalization serves as a transformative force in short-term inventory scheduling, enabling organizations to optimize their inventory management practices. By enhancing forecasting accuracy, facilitating real-time insights, and supporting efficient reorder points and stock allocation, businesses can improve their agility and efficiency. As new technological trends continue to emerge, the integration of digital supply data will remain a critical focus for companies seeking to navigate the complexities of modern supply chain management. By capitalizing on these advancements, organizations can not only meet their short-term goals but also position themselves for long-term success in an increasingly competitive landscape.

In the past, many organizational departments were responsible for maintaining their own information systems without considering other business units like marketing, purchasing, accounting, and so on. Depending on their specific requirements, the various unit-level systems had varied ways of collecting and storing data. Managers in their departments were able to improve and make better judgments with the aid of the business unit systems. (Mahesh Guptaa, 2006). While the various systems could help at the unit level, but sustaining effective inventory management necessitated the implementation of appropriate ERP/MRP, which would encourage consolidated information of the entire organization. According to (Wei, Idrus, & Abdullah, 2017), raw material shortages or excesses brought on by poor inventory management have a direct effect on the organization’s success. Additionally, the article further explained that using both the ERP/MRP system results in increased efficiency AND enables manufacturing organizations do away with manual inventory management. My argument is that inventory management is influenced by MRP/ERP decisions as these tools provide a vast of benefits which improve the handling of inventory in an organization. According to an article written by (Kuse, 2023), ERP system enables tasks around inventory to be automated and consolidated with other functions such as scheduling, production and planning, demand forecasting and so forth. The system furthermore benefits the organization by providing real time inventory visibility, automated processes, delivering on time streamlined supply chain and accurate demand forecasting. Additionally, according to (Shari Shang, 2000),ERP system offers operational benefits for inventory management which include reduced labor costs due to the automation of redundant, archaic processes, the creation of new, efficient work methods, and lower expenses for human resources and warehouse space. To further support my argument, any organization’s primary objective is to maximize growth and please its customers, and putting in place an ERP/MRP will assist them accomplish that objective. According to Layth Abuhilal (2006), choosing the right inventory methodology is crucial to achieving the organization’s objective. Additionally, operations management has changed over the years and can now be challenging because of the size of the customer base, which is more international than your local community, and the consolidation of processes and services to meet specific needs while taking into account all kinds of methods, whether mass production or make-to-order. Therefore, putting in place an ERP system is a major part of the company’s strategy since it enables other departments to make sure they are making the most use of their capital, personnel, and equipment while also keeping in mind of meeting the demands of their end-user.

Lean operations aims to minimize and/or eliminate waste, leading to cost reduction and performance improvement (Matope, Dondofema & Maradzano, 2019). Integrating lean operations in product design enhances the design process, thereby creating higher quality products, more quickly, with fewer resources and minimal waste. Deloitte Insights (Rao, Prusty, Bhat, & Khan, 2020) notes that digital technologies helps increases the impact of lean principles. A cost reduction of 30% can be achieved for lean initiatives utilizing digitilisation vs 15% cost reduction for lean effort without digitilisation (Serlenga, Leppavuori, & Moraes, 2019). There are three key areas identified for better integration of product design in lean operations using digital technologies: Data Driven Decision-Making, Efficient Product Design Process, Team Effectiveness. Three key areas identified for product design integration in lean operation using digital technologies. Data Driven Decision-Making: During the product design ideation phase, potential solutions are generated to address a customer problem or market limitation (Armstrong & Lee, 2024). Digital technologies can enhance decision-making by using big data analytics and machine learning to give insights into customer preferences and market trends. These insights can assist in design decisions, which will result in a final product that meets customer needs, thereby minimizing waste and redundancy. Efficient Product Design Process: In the ideation phase, prototypes are built to test the product’s viability. Design sprints, based on lean principles, offer a cost-effective way to prototype. This approach allows for earlier identification of issues early in the design process and reduces losses due to failed products (Armstrong & Lee, 2024). Digital tools and advanced simulation platforms can facilitate rapid and accurate prototyping, simulation, and testing in a virtual environment, allowing for quicker iteration and refinement of product designs. This eliminates the time and cost associated with physical prototyping. Digital technologies support agile and iterative design processes, which align with lean principles of waste elimination and continuous improvement (Armstrong & Lee, 2024). Agile methodologies, combined with digital tools, allow for continuous improvement and rapid iteration based on user feedback and testing. In addition, the use of digital tools and simulation platforms enables organizations to build desirable products quicker, thereby reducing time to market, which is crucial for competitiveness (Welo, 2011). This is turn reducing overall development costs and improves organisation agility (Albayrak & Poyrazoğlu, 2023). Team Effectiveness: Participation of everyone in the organization is key to success in lean operations for an effective problem-solving process and identification of opportunities for improvement (Calistan, 2016). Digital technology enhances collaboration and communication among design teams by offering cloud-based platforms that facilitate data sharing and collection of feedback. This process helps identify and rectify issues quicker thereby ensuring that the design process remains lean and efficient. At the same time, digitally enabled communication platforms can help organize design workflows and track progress efficiently, reducing bottlenecks and removing idle time.

The management of service operations often requires real-time adjustments to work schedules to accommodate changing demand and other disruptions (Hur et al., 2004). In the digital era, where industry life cycles are increasingly accelerated, the need for agile scheduling practices has become more critical. (Cook, n.d). Real-time schedule adjustments allow service managers to promptly respond to variations in demand, staff availability, and other unforeseen events that can impact operations. This study explores the decision-making processes and key considerations involved in real-time scheduling adjustments in service organisations. Scheduling in a services environment differs from traditional manufacturing scheduling in that it must account for uncertainties, interpersonal dynamics, and the need for immediate responsiveness to customer requirements. The present research examines how service managers assess the gap between scheduled staff capacity and actual experienced workload, and the range of options they consider making timely adjustments. The research draws on insights from several relevant studies on the topic. A case study on real-time schedule adjustments highlights the importance of correctly identifying the direction of demand changes, rather than precisely quantifying their magnitude (Hur et al., 2004). The study notes that workforce scheduling is critical to the success of many service organisations due to its direct impact on customer service, costs, and profitability. (Hur et al., 2004). Another study emphasises the need to address uncertainty at the day-to-day level, such as unplanned absenteeism, equipment failures, and unexpected spikes in demand, through real-time scheduling and control (Bard, 2004). A third study delves deeper into the real-time work schedule adjustment decision, defining it as the correction made to the staff schedule when there is a significant gap between experienced workload and scheduled staff capacity. (Hur et al., 2004). In the context of the digital economy, the ability to rapidly adapt work schedules to evolving conditions can provide a competitive advantage for service providers. And on the contrary, the failure to have robust scheduling practices and capabilities can lead to suboptimal resource utilisation, poorer customer experience, and diminished financial performance.This paper examines the key decisions and tradeoffs involved in real-time schedule adjustments, drawing on relevant academic literature to provide insights for service operations managers and higher education institutions such as Stadio Holdings (https://stadio.ac.za/about-stadio).Finding what to fix: Herbert A. Simon’s decision-making model, particularly his concept of bounded rationality, offers a robust framework for understanding and enhancing the intelligence phase of decision-making processes in organisations like Stadio. The intelligence phase is crucial as it involves identifying problems, gathering relevant information, and understanding the context within which decisions will be made. We expand below on how Simon’s model can be applied during this phase, particularly in the context of Stadio’s operations and strategic initiatives. Understanding Bounded Rationality: Simon’s notion of bounded rationality posits that decision-makers operate under constraints of limited information, cognitive limitations, and time restrictions. This means that while individuals strive for rationality, their decisions are often made within the confines of what they can realistically process. In the context of Stadio, this is particularly relevant as the institution navigates the complexities of higher education, including regulatory requirements, market demands, and internal capabilities. Identifying Problems and Opportunities: In the intelligence phase, Stadio must first recognise the challenges and opportunities it faces. This could involve: Market Analysis: Utilising performance metrics and feedback from stakeholders (students, faculty, industry partners) to identify gaps in current offerings or emerging trends in education. SWOT Analysis: Conducting a SWOT analysis (Strengths, Weaknesses, Opportunities, Threats) to systematically evaluate internal capabilities against external market conditions. Simon’s model suggests that decision-makers should not only focus on the most apparent problems but also consider underlying issues that may not be immediately visible. This requires a comprehensive approach to data collection and analysis, which can be facilitated through both qualitative and quantitative methods. Gathering Information: Once problems have been identified, the next step is to gather relevant information. Simon’s model emphasises the importance of information in decision-making: Data Collection: Stadio can leverage various data sources, including academic performance statistics, student satisfaction surveys, and employment outcomes of graduates, to inform their decision-making. Stakeholder Engagement: Engaging with faculty, students, and industry representatives to gather insights and perspectives that may not be captured through traditional data collection methods. Benchmarking: Comparing Stadio’s performance against other institutions can provide valuable context and identify best practices that can be adopted. Analysing Information: In this phase, Stadio must analyse the collected information to understand the implications of different choices: Decision Trees: Utilising decision trees to visualise potential outcomes of various courses of action can help clarify the risks and benefits associated with each option. Scenario Planning: Developing scenarios based on different assumptions about future conditions can help Stadio prepare for uncertainty and make more informed decisions.Simon’s model encourages a systematic approach to analysing information, recognising that decision-makers may not have the capacity to process all available data. Therefore, prioritising key metrics and insights that directly impact strategic goals is essential.Trend Analysis: The digitalisation of operations in developing economies presents both significant challenges and opportunities. This analysis identifies critical technological and societal trends influencing this dilemma, including the technology bottleneck, the importance of digital infrastructure, and the role of stakeholder engagement. These trends highlight the necessity for organisations to adapt their strategies considering the prohibitive costs and complexities associated with digital transformation. Technological Trends: Technology Bottleneck: Definition: A technology bottleneck occurs when the capacity of a technological system is limited, hindering the overall performance and effectiveness of operations. Impact: In developing economies, limited access to advanced technologies can restrict organisational growth and competitiveness. According to a report by the International Telecommunication Union (ITU, 2020), only 19% of individuals in low-income countries have access to the internet, compared to 87% in high-income countries. This digital divide exacerbates the technology bottleneck, limiting the ability of organisations to leverage digital tools for operational efficiency. Digital Infrastructure Development: Importance: Developing robust digital infrastructure is critical for facilitating digitalisation. The World Bank (2021) emphasises that investments in digital infrastructure can lead to improved economic outcomes, with estimates suggesting a potential increase in GDP by up to 5% in developing countries through enhanced digital connectivity. A study by the McKinsey Global Institute (2021) found that every 10% increase in broadband penetration can lead to a 1.38% increase in GDP in developing economies. This demonstrates the significant economic opportunity associated with improving digital infrastructure. Adoption of Digital Twins: Definition: Digital twins are virtual representations of physical systems that can be used for simulation and optimisation. Application: The use of digital twins can enhance operational efficiency and decision-making processes. According to Gartner (2022), the adoption of digital twins is expected to grow by 30% annually, particularly in manufacturing and supply chain management sectors, providing organisations with the ability to model and optimise their operations in real-time. Societal Trends: Asymmetric Information Exchange: Challenge: The current digital landscape often favours internal stakeholders, leading to an imbalance in information exchange. This can result in decision-making processes that do not adequately consider the perspectives of all stakeholders, particularly those negatively affected by operational decisions (Jackson, 2019). Opportunity: By fostering transparent platforms and participative governance mechanisms, organisations can mitigate the effects of asymmetric information, leading to more equitable outcomes and improved stakeholder engagement. Customer-Centricity and Co-Creation: Trend: There is a growing emphasis on customer-centric approaches in digital operations, where customers are viewed as active participants in the value creation process. Impact: This shift towards co-creation can enhance customer satisfaction and loyalty. A study by the Harvard Business Review (2021) found that organisations that engage customers in the co-creation process can achieve up to 25% higher customer satisfaction scores compared to those that do not. Sustainability and Ethical Considerations: Importance: As organisations digitalise, there is an increasing recognition of the need to integrate sustainability and ethical considerations into their operations. This includes ensuring that digitalisation efforts do not exacerbate existing inequalities or harm the environment. According to a report by the United Nations (2020), 70% of consumers are willing to pay more for sustainable products, highlighting the market opportunity for organisations that prioritise ethical practices in their digital transformation efforts. Reflection on the Digitalised Approach to Short-Term Scheduling: The digital transformation dilemmas faced by organisations, particularly in developing economies, necessitate a nuanced understanding of how digitalisation can reshape operational strategies. The context provided highlights key issues surrounding digitalisation, including the costs associated with technology ownership, the importance of expertise in operations management, and the need for ethical considerations in decision-making. In this light, a digitalised-based approach to short-term scheduling for Stadio can be instrumental in addressing these dilemmas effectively. a) Key Costs and Strategic Realignment: 1. Technology Ownership Costs: The prohibitive costs associated with digital technologies can serve as a significant barrier to digital transformation in developing economies. Organisations must recognise that investing in their own digital infrastructure can lead to long-term benefits. By focusing on developing in-house capabilities, Stadio can mitigate the risks associated with technology ownership, such as maintenance costs and vendor lock-in. This strategic realignment can enhance operational efficiency and reduce dependency on external providers. Short-term Scheduling Benefits: A digitalised approach to short-term scheduling can optimise resource allocation, minimise downtime, and enhance responsiveness to changing market conditions. By leveraging digital tools such as predictive analytics and machine learning algorithms, Stadio can forecast demand fluctuations and adjust its scheduling accordingly. This agility is crucial in a competitive environment where customer preferences can shift rapidly. b) Expertise and Knowledge Management: Sources of Knowledge: Several articles emphasis the importance of expertise in operations management and the need for relevant knowledge and skills. Stadio should invest in training its workforce to utilise digital tools effectively. This includes understanding how to interpret data generated by digital scheduling systems and making informed decisions based on real-time insights. Collaborative Network Design: Engaging in open innovation and collaborative network design can enhance Stadio’s ability to co-create value with stakeholders. By fostering partnerships with technology vendors and other organisations, Stadio can leverage shared knowledge and resources, leading to improved scheduling outcomes and overall operational effectiveness. c) Ethical Considerations and Stakeholder Engagement: Transparency and Stakeholder Engagement: The ethical defensibility of digital transformation initiatives is paramount. Stadio must ensure that its digital scheduling systems are transparent and that stakeholders are engaged in meaningful ways. This can help address the asymmetry of information that often favours internal stakeholders, ensuring that all parties have a voice in the decision-making process. Emancipation of Negatively Affected Stakeholders: The digitalisation process should also consider the interests of those who may be negatively affected by operational changes. By implementing feedback mechanisms and involving employees in the scheduling process, Stadio can create a more inclusive environment that values diverse perspectives. Measures of Succes Holistic Framework for Success: Traditional lagging measures of success, such as productivity and profitability, may not fully capture the impact of digitalisation on operations. Stadio should adopt a holistic framework that integrates operational efficiency, data accuracy, collaborative effectiveness, sustainability, and customer satisfaction. This approach will provide a more comprehensive understanding of the benefits derived from digitalised scheduling. Continuous Improvement: Digital transformation is not a one-time event but an ongoing process. Stadio must establish mechanisms for continuous improvement, leveraging data analytics to refine its scheduling processes continually. This iterative approach will enable the organisation to adapt to changing conditions and enhance its competitive advantage.

This paper explores how quality management influences (or is influenced by) the design of work for digital operations. The influence of quality management on work design in digital operations relates to the rise of digital innovations and Industry 4.0 technologies. As digital transformation progresses, quality management is redesigned to fit the requirements of agile, technology-driven environments. This can help organisations gain a competitive edge if they are known to produce products that meet or exceed global quality, design and price standards. Quality management is evolving to integrate traditional quality practices with technologies such as IoT, machine learning, and data analytics. This shift enables more accurate and real-time quality control, allowing work designs to become more responsive and less dependent on human judgement alone. This integration reduces errors and enhances process efficiency, as digital tools identify quality issues at early stages, thus reducing rework and delays. Appraisal costs related to evaluating products, processes, parts and services are reduced. Digital technologies in quality management redefine work design to emphasise human-centric approaches, especially in knowledge-intensive areas. Digital innovations shift routine quality checks towards automated processes, freeing up workers for more strategic tasks and problem-solving roles. This redesign of work functions helps organizations maintain higher quality standards while optimising labor distribution, especially for complex tasks that require human intervention. Advanced data analytics and machine learning allow for predictive quality management, where potential defects are anticipated before they occur. This proactive approach influences the structure of work by requiring cross-functional collaboration among data scientists, quality managers, and operational teams. It shifts work design towards a predictive maintenance model, which helps maintain consistent quality in digital operations. With digitalised quality management, work design can be more flexible and economically efficient. Processes are more adaptable to fluctuating demands and varying quality requirements, allowing organizations to adjust operational procedures based on real-time data. This digital flexibility promotes continuous improvement in quality processes, which can be rapidly deployed and scaled across operations. A good example of this is Dell Computers who rapidly respond to customer orders because quality systems enable it to achieve rapid through- put in its factories, with little rework. Quality management’s impact on work design is further enhanced when integrated with Business Process Management (BPM) principles. Digital tools facilitate BPM by automating workflow steps and quality checks, which reduces lead times and helps manage customer expectations in a dynamic environment. This integration aids in aligning quality goals with organisational objectives, ensuring work designs support continuous improvement. Quality affects the whole organisation from suppliers to customers and from the point of product design to product maintenance. Total Quality Management emphasises quality that incorporates the entire organisation from supplier to customer. It is a drive towards excellence in all aspects of the products and services that are deemed important to the customer. Improvements in quality can drive an increase in sales and reduce costs therefore, increasing profitability.

Research aimed at improving the measures of customer demand in lean operation identified a few key strategies. Lean principles are aimed at meeting customer demand enabling process efficiencies and the elimination of waste. Studies emphasise the integration of real-time data analytics for continuous demand monitoring as the main catalyst to enable accurate demand forecasting A data-driven approach is enriched further by applying metrics such as customer demand variability and pull-based replenishment strategies. These dynamically readjust towards real demand and avoid overproduction or underproduction. Fernando & Cadavid, 2007). Lean operations focus on the synchronising production to customers’ needs as a basis for process improvement. Flexible manufacturing cells that automatically adjust output levels based on the levels of demand have been shown to shorten lead times and increase responsiveness (Fernando & Cadavid, 2007).Studies on lean practices in manufacturing and service sectors show that automatic changes in the level of production, support lean objectives and reduces inventory wastage (Goshime et al., 2019). Research supports the combination of agile forecasting models with customer feedback loops provide crucial insight into customer needs in real-time and ensure production is aligned to actual demand (Shah & Ward, 2007). The implementation of agile demand assessment tools can overcome the delays associated with batch processing and move towards a more continuous flow aligned with the “just-in-time” philosophy of lean (Losonci & Demeter, 2013). In addition to informing production efficiency, customer demand measurement in lean operation also influences the success of lean implementation. For example, consideration of demand variability indices alongside traditional metrics such as takt time may inform process adjustments which have direct implications for lean outcomes (Shah & Ward, 2007). Integrative measures of demand directly affect lean operations by reduction of cycle times and improved customer satisfaction supporting the principle that lean is about customers at its core (Chauhan & Singh, 2012).