The purpose of this paper it to review how organizations can design a resilient future of work systems. The aforementioned Strategic Decision Area (SDA) will be unpacked better in order to adequately answer this question as well as to review its relevance against the SDA assigned to group 1. With the conclusion of the recent COVID-19 pandemic which took place globally, a good point to start in reviewing this SDA is on how organizations have had to adapt their policies and processes to accommodate a post COVID-19 work environment. According to Antonacopoulou & Georgiadou (2021), the COVID-19 pandemic significantly changed how we live, work and relate to each other. Organizations are therefore having to decide how they can adjust their work processes to suit this new reality. One of the major impacts of the pandemic was the ability for employees to work remotely, provided they have the tools and resources to do their job (Antonacopoulou et al. 2021). This has served as a positive change for many organizations as employees are now able to practice work/life balance (Antonacopoulou et al. 2021). This, however, is dependent on context as some individuals are not able to ‘log off’ after hours. It is then crucial for organizational leaders to review how employees can continue to maintain work/life balance (Antonacopoulou et al. 2021), thus advocating for a resilient future of work for all employees. Antonacopoulou et al. (2021) also state that a major part of advocating for remote work is the digital capabilities involved. Digitalization has changed the way employees interact with each other, their expectations from their employees and their career advancements (Antonacopoulou et al. 2021). Leaders now need to be more aware of what impact can digitalization have on becoming a detriment to employees’ way of working. By having the correct policies and tools in place for employees to utilize, employers can start to create resilient work environments for their employees. This also includes incorporating the correct mental and psychological resources to assist employees who may not find the balance between work/life, thus leading to burnout. As we continue the theme of the impact of digital tools on employees in the workplace, it is most appropriate to mention the new generation of workers entering the workforce, also known as Generation Z/GenZ. This is the generation of individuals born between 1995 and 2012 (Gomes, Duarte, Marques & Cunha, 2023). Gomes et al. (2023) mention that it is important for organizations to accommodate GenZers in their work processes as this will help with creating a resilient workforce. This is also the generation that will help bridge the technological gap between older generations and any current or future generations as they are the most technologically advanced. Gomes et al. (2023) also state that Human Resources (HR) departments also need to actively plan and organize initiatives that create employee resilience in their younger workforce as failure to do so may impact company performance. This also aligns with group 1’s SDA which is job design and sustainability. HR departments can ensure that job designs incorporate strategic positions which are aligned to the needs of the newer generations, ultimately ensuring a sustainable and resilient workforce. The GenZ workforce currently is most likely to enter the work environment into entry level jobs or as graduates. According to McQuillan, Wightman, Moore, McMahon-Beattie and Farley (2021), corporations expect young employees to enter the workforce with certain skills such as the ability to quickly adapt to change and with a level of professionalism. Practically it is not possible for new, young employees to possess these skills without prior exposure and training. Therefore, it is imperative for higher learning institutions to provide training opportunities such as vocational training to upskill their students (McQuillan et al. 2021). It is then important for organizations to collaborate with the same higher learning institutions to obtain the correct individuals when they enter their workforce as graduates (McQuillan et al. 2021). By advocating for collaboration with learning institutions, organizations can begin to create a sustainable and resilient workforce which has been carefully crafted for the intended outcome. Wojčák, Poláková, Copuš & Suleimanová (2021) state that in order to continue to remain competitive in the market, employees and organizations need to start asking the correct questions such as: Where will I work? How will I work? Etc. these questions provide a good framework for how organizations can create a resilient work future for their employees. Wojčák et al. (2021) also mention that the main answer to these questions lies in the flexibility of the organisation. Flexibility in terms of digital resources, work flexibility, time flexibility etc. therefore corporations can make provision for the various flexibilities that are important for their specific workforce to create a sustainable workforce that remains competitive. A number of these provisions can also be facilitated by HR work designs to cater for a more dynamic workforce. This aligns with group 1’s SDA as many elements included were directed to the HR department and the crucial role they play in creating a sustainable future for its workforce. Rankin, Lundberg, Woltjer, Rollenhagen& Hollnagel (2014) state that when designing a resilient work system, it is important to understand how adaptations take place and how many people actually perform. This will allow organizations to be able to make decisions on behalf of employees based on information that is tangible. In addition, from their study, Rankin et al. (2014) concluded that employees can adapt in complex and uncertain environments. Therefore, it is up to organizations to provide the resources to allow employees to be adaptable in their work environments (Rankin et al. 2014). These resources can include the update of policies to suit evolving work environments or tools to be able to work adequately in the face of uncertainties. This will ensure that a resilient work culture is formed and that sustainability can take place for the organization. In conclusion, this paper has unpacked the question of how can organizations design a resilient future of work systems? It specifically made reference to the newer generations entering the workforce (GenZ) as they are most appliable when referring to future work systems. This paper has also demonstrated the close correlation between the aforementioned SDA and that of group 1, ultimately indeed there are various initiatives/resources/tools that organizations can use to create a resilient future of work systems.
Archives: Build Challenges
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How can digitalization improve inventory management in enterprises?
Lee & Armstrong (2023) highlight that digitalisation, the process of converting analogue information into digital information, has many benefits such as cost saving and improved data-led decision-making capabilities. In the inventory management space in particular, digitalisation has become a key determinant for success. Turvo (2024) elaborates on how inventory management can be made more responsive, consistent and efficient with the implementation of digital tools. Digitalization in the inventory management space also comes with enhanced accuracy with less reliance on human intervention and much better decision-making through predictive modelling (Turvo, 2024). This better decision-making can be used to improve the supply of popular goods when demand is high to ensure customer satisfaction while allowing businesses to fully capitalise on highly seasonal events. Perez et al. (2021) make use of various predictive algorithm strategies to optimise stock levels and conclude that combining a series of advanced algorithms, such as reinforced learning and deterministic linear programming, can significantly enhance inventory management. This is a strategy that was successfully employed by Villegas-Ch et al. (2024) who utilised machine learning methods to reduce time spent on inventory counting by 45%, improved inventory accuracy with an increase in recognition precision and yielded a significant drop in overcounting and undercounting across multiple product categories. It is also important to note that once inventory management is digitalized, it unlocks other capabilities such as automated AI-enhancements. De Ponteves & Eremenko (2023) run through a use case where they build and train an automated warehouse robot through Q-Learning to automatically pick up goods at priority locations with the AI agent picking the optimal route to enhance efficiency. This level of automation reduces the need for manual human intervention and is a strategy employed by many of Amazon’s robotic fulfilment centres. McLaughlin, K. (2023) emphasises that this reduces the time and costs associated with manual fulfilment processes and speeds up delivery for Amazon by 25%. There are many ways to digitalize inventory management, ranging from cloud-based solutions that provide a centralized view for managing inventory to barcode-based ones (Osa Commerce, 2023).
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How can digitalization of scheduling improve inventory management decisions?
Digital transformation is changing how businesses manage operations, particularly in inventory management and scheduling. Traditionally, inventory focused on maintaining stock levels to meet demand, while scheduling aimed at optimising resource allocation for timely production. Today, digitalisation allows companies to make smarter decisions, enhancing their competitive edge in volatile markets. Digitalisation brings together real-time data integration, predictive analytics, and automation to synchronise inventory management and scheduling. This integration allows businesses to respond more dynamically to demand fluctuations and operational requirements. As Boute and Van Mieghem (2021) highlight, real-time data processing through digital scheduling systems enhances the accuracy of inventory forecasts by leveraging historical data, market trends, and consumer behaviour analysis. These systems allow businesses to predict demand with greater precision, dynamically adjust stock levels, and mitigate common inventory challenges such as overstocking and stockouts. This marks a significant departure from traditional, manual inventory management processes that are often slow to react and prone to human error. Automation is a critical component of digital scheduling, reducing the reliance on manual intervention. Ross et al. (2019) argue that automation in inventory management enhances decision-making by streamlining routine tasks like stock monitoring and reordering. By automating these processes, digital systems not only reduce the likelihood of human error but also optimise inventory levels and ensure timely replenishments. Chuang and Yang (2014) further emphasise how digital scheduling can optimise resource allocation, aligning production schedules with demand forecasts to reduce excess costs and prevent production delays. The benefits of digital scheduling extend beyond internal operations, enhancing supply chain visibility and coordination. Vanpoucke et al. (2017) note that by integrating scheduling systems with various components of the supply chain, businesses can synchronise their inventory needs with supplier schedules, improving lead times and reducing delays. This synchronisation is particularly important for just-in-time (JIT) inventory management, where real-time updates on supplier deliveries and production progress are essential for minimising storage costs and reducing supply chain disruptions. In an era of rapid market changes, flexibility and adaptability in inventory management are crucial. Traditional systems often struggle to respond to sudden shifts in demand, leading to stockouts or excessive inventory. However, Oludapo et al. (2024) explain that digital scheduling systems, powered by artificial intelligence (AI) and machine learning, enable businesses to adjust their production schedules in real-time based on demand patterns and market trends. This adaptability is especially valuable in industries facing fluctuating demand, allowing businesses to remain agile and responsive. Moreover, the cost-reduction potential of digital scheduling is significant. Chuang and Yang (2014) suggest that optimised scheduling systems lower the need for safety stock by providing accurate demand forecasts and automating replenishment processes. This reduces storage costs and ties up less capital in excess inventory. Additionally, digital systems decrease labour costs by automating many of the manual tasks associated with inventory management, enabling businesses to allocate human resources more efficiently. In conclusion, digital scheduling in inventory management significantly enhances operational efficiency and cost-effectiveness. By leveraging real-time data, predictive analytics, and automation, businesses can make informed decisions, improve supply chain coordination, and quickly respond to market changes. In an increasingly competitive global market, adopting digital scheduling systems is essential for long-term success.
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How can digital product/service design influence inventory management in operations?
Inventory management manages stock movement and ensures that there is economic and logistic justification of such action as these actions significantly impact the financial performance of an organisation (Czarny, 2024). The primary goal of inventory management is to meet demand while sustaining minimal loss in profit, avoiding overstock of unsold goods while preventing shortages (Koren, Perlman, & Shnaiderman, 2023). Inventory management entails knowing the various types of inventory strategies available and utilizing the most suitable (Czarny, 2024). A relevant production strategy is the “Just In Time” (JIT) methodology which emphasises the importance of maintaining low inventory levels as a way of reducing costs subsequently leading to enhanced operational efficiency (Dange, Shende, & Sethia, 2016). Accurate inventory control further facilitates a quality-centric approach where matching inventory with demand can minimize waste and maximize consumer satisfaction (Kumar, Rabbani, & Khan, 2024). The role of digital inventory technologies on product design and quality: Digital technologies such as RFID, IoT, and advanced analytics facilitate real-time inventory tracking, enhance the level of efficiency, and decision-making in organisations and are central to efficient inventory management systems (Ali, Fayad, Alomair, & Al Naim, 2024). The nature of inventory management has shifted toward quality with much of the operational efficiency due to the digital design of products and services (Martinelli, 2024). Digital tools and systems have changed traditional approaches towards inventory management (Kumar et al., 2024). The design of products and services can be personalised by utilizing integrated digital technologies and products that are digitally designed can be modularised such that they can be swapped out easily at production stages thus making inventory management more efficient (Ceschin, 2016). Digital designs affect how products are produced and their quality through the design thinking process which places the customer at the center to ensure quality outcomes from the onset (Kumar et al., 2024). A study by Barlow, Bedford, Revie, Tan, & Walls, (2021) notes that the application of digital tools in a series of design processes has achieved an increasingly higher grade of control in quality due to increased precision and consistency. Improved design techniques facilitate a culture rooted in testing and prototyping thereby improving and ensuring better quality products (Barlow et al., 2021). Conclusion: By incorporating digital tools and automation, product designers realize a closer correlation between production outputs and consumer expectations, which in turn optimize inventory levels. High-quality digital design frameworks of an inventory system reduce errors and ensure the upkeep of consistent quality standards of goods and services, something that directly influences customer trust and repeat purchasing behaviour (Martinelli, 2024).
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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.