This paper explores how a processual approach can lead to the development of digital adaptation mechanisms for resilient operations in the digital society. This form of conceptualization implies a change of enterprise, from one state to another. Recent events such as the lingering COVID-19 pandemic proved that the current operating models of enterprises are woefully unprepared for interconnected, but disruptive global events despite Castells (2000) warning that there was already a shift to a ‘technological paradigm of the ‘network society’, or in the contemporary sense, the “post-industrial” digital society. In this technological paradigm, the ‘network’ concept is used as an organizing metaphor in which new forms of flexible and adaptable organizational structures are required to address the digital challenges facing enterprises today, yet the hierarchical ‘factory’ model of the industrial revolution remains dominant in contemporary enterprises. The adaptability of the enterprise to the new ‘technological paradigm’ remains challenging to navigate, evidenced through the pattern of digital transformation failures documented in both academic (Oludapo et al., 2024)and practitioner literature, even as the societal trends of ubiquitous data, unlimited connectivity and massive processing power are driving disruptions in several sectors of the digital society (Ross, 2019). These social trends, spurred by the commercialization of the Internet in the early 1990s, resulted in a step-change in the complexity of the effective world (Merali, 2006) resulting in various dilemmas, the root causes of which can be explored by challenging the assumptions of technological determinism that characterize many current digitalization initiatives. These undesirable outcomes from the implications of the three social trends and the assumptions linked to technological determinism (Oludapo et al., 2024) have seen emergence of dilemmas linked to increased dynamism, uncertainty and discontinuity in the competitive context, increased pressure for fast decision making under conditions of greater informational uncertainty due to global conflicts, increased importance of (internal and external) intelligence and the importance of flexibility and adaptability (and learning) for survival. To transcend technological determinism, Castells emphasized the need to recognize technologies as enablers, a concept which has improved understanding of digitalization, but remains limiting. Much more recently, Shapiro and Jean Ross have called for considering digital technologies as ‘inspiration’, that is, they inspire and are the foundation for rapid business innovations. While some (Following Piccoli et al., (2024), the view of digital transformation as the metamorphosis of an IT-enabled organization into a digital organization – is used to motivate for a digital architecture, based on design principles. This paper adopts a systems perspective to explore how a process-based approach can lead to addressing these dilemmas guided by the following research question:“How can a process-based approach guide the development of digital adaptation mechanism for resilient operations?”
Archives: Build Challenges
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How has location strategies been transformed using digitalization in operations?
Digital Adaptation for Resilient Operations: Location Strategies Location plans in operations are becoming very digital. This is a major change in how organisations make decisions about the. location and their use of resources. This change is caused by the global business environment becoming more complicated and changing quickly as Covid-19 have shown. Traditional placement strategies frequently depended on fixed, long-term planning with minimal data. However, the rise of the ‘network society’ and the digital paradigm has forced more adaptable ways. The abundance of data, limitless connections and massive computing capacity have radically transformed and the environment of location-based decision-making. Context of Digitalization in Location Strategies: Digitalization has introduced new capabilities that transform location strategies: Real-time Data Analytics: Organizations may now collect and analyse large amounts of location-based data in real-time, allowing for more informed and flexible choices to decide on. Predictive Modelling: Advanced algorithms and machine learning techniques allow for sophisticated predictive modelling of location-based factors, enhancing strategic planning. Digital Twin Technology: Virtual representations of physical locations and assets enable scenario testing and optimization without physical constraints. IoT and Sensor Integration: The Internet of Things (IoT) provides continuous, granular data on location-specific conditions, facilitating dynamic adjustments to strategies. Automated Decision Support: AI-powered systems can suggest optimal location strategies based on complex, multi-variable analyses. Key trends influencing the digitalization of location strategies include: Increasing adoption of cloud-based location intelligence platforms. Growing integration of location data with other business intelligence systems. Rising importance of geospatial analytics in supply chain optimization. Emergence of location-based services as a critical component of customer experience. Reflection on the Process Approach: A process-based approach to digitalizing location tactics enables continual adaptation and refining. This approach understands that location decisions are not one-time events but ongoing processes that must evolve with changing market conditions, technological advancements, and organizational needs. Viewing location strategy as a dynamic process rather than a static plan, organisations can employ digital capabilities to construct more resilient and responsive operating models.This synthesis demonstrates how digitalization has transformed location strategies from rigid, infrequently updated plans to dynamic, data-driven processes that continuously adapt to the complex realities of the digital age.
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How can the digitalization of inventory management impact location strategies?
This discussion explores how a process-based approach to digital inventory management can influence location strategy development to drive operational resilience in a digitalised business landscape. This perspective implies a transformation in enterprise strategy, moving from traditional, manual supply chains to interconnected, digital ones to enable real-time data collection, analysis, and monitoring of inventory levels and demand patterns (Vaka, 2024). Location strategies encompass decisions regarding the placement of warehouses, distribution centres, and pick-up points to support business goals, such as cost-effectiveness, accessibility, and environmental sustainability (Kostecka & Kopczewska, 2023). Traditional location strategies are often driven by proximity to markets or resources to minimise costs. However, this focus can limit an organisation’s responsiveness to sudden shifts in demand or supply chain disruptions (Polhong et al., 2022). Inventory systems directly impact these strategies. For example, by using digital tools to predict demand with precision, firms can position warehouses closer to high-demand areas, thus reducing transportation costs and enhancing service levels (Ho et al., 2022). This alignment is important for operation management, since it not only lowers operational expenses but also strengthens a firm’s ability to swiftly respond to fluctuations in market demand (Vaka, 2024). The integration of inventory management with location strategies can therefore strengthen operational efficiency and drive competitive advantage in the supply chain by optimising the balance between inventory holding and distribution costs. In a conventional inventory model, businesses often face challenges coordinating inventory levels with suppliers, which can impact their location decisions and lead to inefficiencies (Vaka, 2024). Digitalised vendor managed inventory (VMI) and connected supply chain solutions address this by enabling synchronised, real-time updates on inventory levels across locations, streamlining supply and demand alignment. This integration provides firms with greater flexibility in location strategy, as it reduces dependence on single suppliers and supports a more fluid, decentralised location model (Polhong et al., 2022). For firms that depend on cost-effective distribution, there is a dilemma between choosing central locations that reduce logistical complexity and decentralising to stay closer to customers. Digitalised inventory management, leveraging data-driven insights and route optimisation models can help firms identify optimal distribution hubs. This strategy minimises logistical costs by streamlining transportation and inventory flow while maintaining proximity to high-demand regions (Vaka, 2024). Additionally, in the era of e-commerce, effective location strategies are crucial, and digitalised inventory systems can enable firms to analyse potential distribution points that minimise costs and enhance delivery speed (Romero & Diego, 2021). This digital evolution is important for businesses because it addresses growing pressures from customers for faster delivery and sustainable practices. For instance, as highlighted in the study of Kostecka and Kopczewska (2023), e-commerce giants such as IKEA have adopted spatial customer relationship management (CRM) in their location strategies, establishing strategically positioned pick-up points that allow customers to collect purchases with minimal environmental impact. Current trends influencing inventory digitalisation and location strategies include the push for greater automation, data-driven decision-making, and enhanced supply chain resilience in the face of disruptions (Niaz, 2022). Automation and robotics improve the efficiency of warehouse operations, while data analytics enable predictive insights to anticipate demand and adjust inventory accordingly. Additionally, key innovations include automation, IoT devices, AI, and digital twin technology, which provide businesses with precise control and visibility over inventory, improving decision-making in logistics and operations. Adopting a process approach can help businesses manage inventory digitalisation by integrating these digital technologies systematically across the supply chain. Such an approach involves mapping, analysing, and optimising inventory-related processes to support digital transition. By breaking down inventory tasks into processes, firms can implement digital tools in a stepwise manner, minimising disruption (Polhong et al., 2022). A process approach also enables continuous improvement, as firms can assess the impact of digital tools on performance and adjust their location strategies accordingly. Therefore, digitalised inventory management, combined with a structured approach, enables businesses to optimise both inventory and location strategies, driving operational success in a digital economy.
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How can layout strategies influence/be influenced by maintenance decisions?
The Strategic Decision Area (SDA) assigned is “Maintenance,” based on the views of various students who reflected on how Critical Systems Heuristics (CSH) and systems thinking can enhance the understanding and management of operational complexities. Students have shared their insights on how these approaches can help manage the complexities of operations. Based on the analysis of Pre -Course Survey findings and group talks, it is evident that maintenance is viewed as a crucial element in guaranteeing the seamless operation of organizational activities, prioritizing the improvement of processes, increasing effectiveness, and simplifying workflows via digital transformation. Student Perspectives on Maintenance: Understanding maintenance from the students’ perspectives reveals that it is not just about fixing or preserving systems but involves aligning operational objectives with strategic goals. For example, Monica discusses the importance of digital technologies in modernizing mining operations, which directly influences maintenance by making processes more efficient and cost-effective. Similarly, Maxwell’s view ties maintenance to data-driven decision-making, where scheduling and resource allocation are optimized through digital solutions. Role of Maintenance in Digital Transformation: Maintenance, as and SDA also plays a crucial part in aiding digital transformation initiatives in various industries. It guarantees that all operational systems stay operational, trustworthy, and flexible to change, which is especially important within the group’s overall focus on “digitizing operations.” One example is when a student pointing out how automating the value chain can improve operational efficiency by integrating maintenance with digital tools, which reduces errors and allows resources to focus on strategic tasks. Bidirectional Influence of Maintenance and Digital Operations: Maintenance has a bidirectional impact on the group’s SDA theme, “Maintenance and Digital Operations.” Maintenance practices guarantee that operational continuity and performance are maintained, but the adoption of digital operations is changing how maintenance is carried out. Digital technologies including IoT, automation, and data analytics offer predictive maintenance abilities, enabling companies to tackle possible problems beforehand rather than after they occur. Thus, maintenance upholds operational integrity to support digital transformation, while digitalization improves maintenance by increasing predictability, efficiency, and strategic planning. Scholarly Articles: Recent scholarly articles emphasize the integration of digital tools and advanced methodologies to enhance maintenance practices. Introna and Santolamazza (2024) highlight strategic maintenance planning using digitalization for better asset performance. Dui et al. (2024) focus on optimizing maintenance strategies with AI and IoT. A 2021 review covers various paradigms for selecting maintenance strategies and evaluates how strategic decision-making for maintenance types can impact organizations. A 2021 article outlines developing smart maintenance strategies with digital tools. Thes studies highlights the importance of digital transformation in modernizing maintenance with the goal of improving operational efficiency.
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How can the digitalization aid in mitigating schedule risk?
Digitalisation has emerged as a transformative force in project management, particularly in mitigating schedule risks. My research synthesis aims to highlight key findings on how digital tools and technologies can enhance schedule reliability. Digital transformation in project management is a fundamental shift in an organisation’s operational mindset that goes beyond the simple adoption of digital tools. It changes how projects are managed by replacing traditional, paper-based procedures with digital workflows that are quick, effective, and scalable. (Ogungubukola, 2024). Firstly, Digital project management tools, such as Gantt charts and Kanban boards, facilitate real-time tracking of project progress. According to a study by Peter Landau (Landau, 2024) , these tools allow project managers to visualize timelines and dependencies, which helps in identifying potential delays early. By maintaining an updated view of project status, teams can proactively address issues before they escalate. Secondly, the implementation of Building Information Modelling (BIM) in construction projects has shown significant potential in reducing schedule risks. According to a publication by Rozita and Ehsan (Samimpay & Saghatforoush, 2020), BIM not only facilitates better stakeholder communication but also work coordination, reducing misalignments that may cause delays. Better risk management techniques are made possible by the collaborative nature of BIM, which promotes a common knowledge of project deadlines. Thirdly, the use of predictive analytics powered by artificial intelligence (AI) has gained attention in project scheduling. Research by Muhammad Nabeel. (Nabeel, 2024) shows that AI can foresee possible schedule interruptions by analysing past data, allowing project managers to create backup plans. Organisations can reduce the risks associated with unanticipated events by using data-driven insights to inform their decisions. Moreover, mobile technologies facilitate on-site communication, as discussed in an article on On-site construction management using mobile computing technology (Kim & Taeil Park, 2013). Mobile apps make it possible for team members to provide real-time updates and comments, which helps keep everyone on the same page about the project schedule and lowers the possibility of delays brought on by misunderstandings. Finally, stakeholder participation is improved by the incorporation of modern communication tools. Maintaining schedules requires efficient communication since it guarantees that everyone is aware of any changes and can react quickly. It is clear how schedule risk management and digitisation interact. Organisations may improve project outcomes and reduce schedule risks by utilising digital tools to better communication, collaborate, and use data analytics.
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How does scheduling strategies influence the aggregate planning?
Aggregate planning is a strategic operations management process in business to manage supply and demand through effective management of resources, inventory and production over a period (Keup, 2021). Research highlights that aggregate planning is fundamental to business operations, enabling companies to effectively navigate the constantly evolving landscape of market demands. Literature emphasises the strategic importance of aggregate planning, which enables organisations to achieve operational efficiency and align with overall business objectives. When we look at how aggregate planning and scheduling interact, it is clear that efficient scheduling is crucial for successful aggregate planning. Good scheduling reduces idle time and boosts productivity and resource allocation, helping businesses adapt to changing demand. It ensures that production aligns with market needs and uses resources efficiently and effectively, which supports capacity planning (Averbuch, 2022). By optimising scheduling, organisations can avoid bottlenecks, improve customer satisfaction, and enhance overall operational reliability and throughput (Schregardus, 2023). According to Kumar et al. (2020), scheduling strategies directly affect aggregate planning through resource allocation and capacity utilisation. Their studies found that organisations that implemented these strategies achieved 18% higher operational efficiencies. Aggregate planning and scheduling can significantly enhance the operations of iSwitch metering by aligning production and service calls and scheduling according to market demands. This will improve customer satisfaction and loyalty whilst improving operational efficiencies. Effective scheduling will support iSwitch to streamline their operations and avoid bottlenecks, which will lead to more reliable service delivery and improved throughput. In addition, this will inform capacity planning and management. Additionally scheduling in aggregate planning will further equip iSwitch in managing their customer interaction and touchpoints such as call centres and walk in customer centres. Preventive maintenance plays a vital role in both scheduling and aggregate planning. By adhering to regular maintenance schedules, organisations can ensure that their production equipment remains operational and reliable, thereby minimising unexpected outages and downtime. This proactive maintenance approach enables organisations to create accurate aggregate plans, as they can reliably predict the availability of equipment for production. Implementing a comprehensive maintenance strategy also allows businesses to forecast production schedules more precisely, ensuring that resources are available when needed (Averbuch, 2022). The impact on maintenance planning was extensively studied by Wang and Zhang (2023) who illustrated that preventive maintence scheduling integration into aggregate planning improved overall equipment effectiveness and reduced unexpected downtime. This research illustrated the nature of matching maintenance activities with operations and production schedules minimises disruptions and optimises resource utilisation. Aggregate planning and preventive maintenance can greatly enhance the operations of iSwitch metering by balancing supply and demand for their metering services. Furthermore, equipping iSwitch to have the correct amount of inventory available to meet customer needs. Efficient scheduling will ensure that iSwitch workforce are equipped and used efficiently. This will reduce idle time and improve productivity allowing the company to oversee more installations and maintenance tasks efficiently. Regular maintence schedules will further ensure that all metering equipment is operational and reliable which will mitigate any unplanned events or downtime, which can also impact on customer experience (Planet Together, 2021). By adopting a balanced approach to aggregate planning, implementing effective scheduling strategies, and engaging in proactive maintenance, organisations can significantly boost their operational efficiencies. These improvements not only enhance the customer experience but also strengthens the organisation’s competitive position in the market in which it operates in
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How can analytics enable quality aggregate planning in enterprises?
In Supply chain management, strategic decision area analytics to quality aggregate planning usually plays a significant role in enabling quality towards focused aggregate planning within every enterprise. This synthesis can be used to examine how the enterprise could analyze the every approach to enhance control and quality planning across the entire enterprise supply chain management. In this synthesis, we will divide it into three phases which are the core understanding of analytics in quality aggregate planning, Integration with supply chain management and the Impact on enterprise performance. Core Understanding of Analytics in Quality Aggregate Planning: Consist of: Predictive Quality Management consist of: Advanced analytics that enables predictive issues in quality before they happen. Machine learning models that identifies patterns in the deviation to quality. And real-time monitoring tools that provides early warning signals (Zhang & Wang, 2023). Data-Driven Decision making considers : Big data analytical information as a capability into planning decisions. Quality metrics that are integrated into resource allocation. And Stats on process controls that enhance real-time data driven decisions (Strategies for data analytics projects in business performance forecasting: a field study, 2022). Integration with Supply Chain Management with focus to: End-to-End Visibility of Supply Chain analytics Management that provides transparency to quality metrics. Real time monitoring capabilities that enables proactive quality management metrics (Singh & Agarwal, 2023). Impact on Enterprise Performance With focus to Integrated analytics to aggregate quality planning that leads to: Reduced cost of related quality. Improved customer service experience. Improved operational efficiencies. And better resource allocation and utilization (Farivar, Golmohammadi, & Ramirez, 2022). In conclusion, this synthesis shows how analytics could serve as one of the important digital enabler of quality in aggregate planning especially when collaborating with supply chain management with considerations to the approach to combining both modern and traditional quality management principles to create a more responsive and efficient system planning.
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How can digitalization of scheduling improve aggregate planning?
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?”
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How can ERPs influence quality management in enteprises?
This synthesis explores how implementing Enterprise Resource Planning (ERP) technologies may significatly affect quality control in companies. ERP systems are complex software solutions designed for integrating several business operations within a single unified system, thus enhancing data visibility, efficiency, and collaboration. Common challenges of reaching consistent quality standards is that many businesses have fragmented data and isolated functions, which ERP systems can address. ERP solutions helps companies to centralize data, simplify procedures, and guarantees adherence to quality control measures in a fast-changing competitive environment where success depends mostly on quality. (Johansson et al., 2019; Ahmed et al., 2021. As businesses manage complicated supply chains and growing demand for product transparency, the value of integrated quality management has grown significantly. Lee and Grover (2020) cite that ERP systems not only assist in operational process management but also plays a strategic role in tracking quality metrics across departments, therefore “guaranteeing” real-time insights that support ongoing efforts for continuous improvement. In addition, ERP systems can tailored/custom made to fit industry-specific quality criteria, therefore enabling companies to effectively meet regulatory requirements and improve consumer satisfaction. Therefore, companies using ERP systems for quality control can effectively apply standardized processes which will decrease variability and minimise defects (Davis & Weber, 2022; Zhao & Chen, 2021). It is noted that ERPs has made a significant impact on predictive analytics as well as quality management. ERP systems has also enabled proactive quality management, providing the ability to forecast possible quality problems through the analysis of past data. Research by Thompson and Green (2023), for instance, highlights how ERP-based predictive analytics can assist in determining the underlying causes of recurring quality problems in order to put preventative measures in place. In addition to improving product quality, this strategy reduces waste and supports sustainability objectives that are becoming more and more significant in modern business strategic objectives. In summary, by facilitating regulatory compliance, facilitating real-time data availability, and encouraging proactive quality measures, ERP systems provide a strong foundation for quality management. ERPs enable businesses to continuously meet and surpass quality requirements by centralizing operations and enabling thorough data analysis. This improves customer happiness and helps them succeed over the long run-in cutthroat marketplaces.