This report seeks to understand the influence of maintenance in short-term scheduling decisions, particularly in digitalizing operations in e-learning platforms. Higher education institutions heavily rely on online learning. The competence and efficacy of the e-Learning platform, especially in the short-term scheduling of tasks or activities related to the scheduling of classes, exams, reporting, feedback, and assessments, are significant elements influencing the effectiveness of e-Learning. The maintenance difficulty is a substantial component of short-term scheduling in online education. A critical factor of short-term scheduling in online learning is the maintenance challenge illustrated in a study by Olanrewaju et al., 2020), which includes issues relating to; There is a need for a comprehensive and systematic framework for maintenance management that guides decision-making in online learning. A convoluted and disjointed maintenance issue reporting system that causes delays and higher expenses. Since the maintenance department is understaffed and needs properly qualified workers, contractors are being outsourced. Delays and backlogs in complaint resolution due to unavailability of maintenance department is closed on weekends. eLearning infrastructure is crutial and can presents several maintenance challenges, as shown in papers by Nawaz & khan, 2012 and Khedr et al., 2021. These include issues pertaining to; Frequent updates and modifications are required in response to rapid technological improvements. The initial investment and continuing costs for maintaining and enhancing infrastructure are costly and can cause budget constraints. The security of the eLearning systems requires constant attention and resources. Addressing hacking, data breaches, and other cyber threats requires continuous dedication. The co-dependency between the users and the technical department due to a lack of skills and expertise. The monitoring of load distribution to prevent bottlenecks and guarantee seamless operation when they receive less technical assistance. Data backup and recovery mechanisms are required to preserve data availability and integrity in the case of malfunctions. Elghibari et al., 2015 and Papathanasiou et al., 2012 focused on digitalizing eLearning processes, such as intelligent eLearning systems and algorithms, to address maintenance issues within eLearning platforms. Elghibari, concluded that automated updating mechanisms ensured consistent and high-quality course materials by detecting anomalies in current and obsolete information. Subsequently, Papathanasiou claimed that utilizing algorithms for e-maintenance systems improves eLearning systems’ efficiency, accuracy, and responsiveness. These improvements included preventative and condition-based maintenance, real-time data access, and integrated systems to streamline maintenance processes. “How can short-term scheduling decisions be influenced by the digitalization of maintenance systems within the Stadio e-learning platforms?”
Archives: Build Challenges
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How can digitalization improve short term inventory scheduling?
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.
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How can digitalization enable better inventory management in enterprises?
Traditional or manual inventory management techniques that rely on manual data collection, are marred by wastage, escalated costs and often lead to poor customer satisfaction (Niaz, 2022). These were based on broad planning methods based on historical demand and trends, for example, if in the last 3 seasons you needed X parts to produce Y product, the operations managers would assume that even in this season they would need the same number of parts based on previous experience. If market demand suddenly rises or diminishes, then you are stuck with additional unwanted and unused inventory, which attracts massive costs, or you are not able to meet customer demand, which leads to customer attrition. Issues of overstocking, understocking and inability to accurately measure inventory on hand are also quite common where manual inventory management practices are in place (Chan et al, 2017). Inventory management also has a direct relationship with supply chain management. The inability to manage stock adequately may result in understocking and when making back orders to meet the customer demand, there may be a delay in the delivery of the raw materials to enable product development, further leading to lost sales and customer attrition. Ali et al (2024), also argue that stock management is an issue across many industries, with businesses struggling to keep the right amount of inventory, or getting their inventory timeously when needed. Digitalizing the manufacturing value chain provides enterprises with valuable data that can help them better understand customer demand, behaviour and product lifecycles and improves demand forecasting with then allows them to better do inventory management. This translates into appropriate inventory levels, minimizes the risks of understocking or overstocking which can result in the inability to deliver goods on demand, or high inventory costs respectively. (Niaz, 2022). Digitization therefore allows entities to quickly adapt to changing external events, such as a drop in demand or an unexpected increase in demand. Because there are automated tools that use data to monitor these events on a near real time basis, it means that the business can be more nimble and adapt to change quickly, therefore giving it competitive advantage as it is able to react to the market quickly (Ali et al, 2024). Balon et al (2022), also argue that technologies such as Blockchain can enhance the allocation of Human resources in order to perform production tasks. This further illustrates the importance of digitalization in enhancing operations.
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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.