Smart Domain: Smart Infrastructure

This paper explores how maintenance influences the sustainability of digital operations within supply chain management. Maintenance is seen as a critical component influencing the sustainability of digital operations due to it ensuring reliability, longevity and efficiency in hardware and software systems. Effective maintenance strategies extend the lifecycle of infrastructure, reducing the frequency of equipment replacement, which can have a significant environmental cost due to waste and the resource-intensive processes required for manufacturing and disposal (Chiravuri, Feroz, & Zo, 2021). This paper also makes specific reference to Mphilo Milling, a SME developed in 2006. Mphilo Milling currently faces scalability, supply chain and barrier to entry challenges. Although it has automated it’s milling process it is still limited in its output, producing between three to six hundred tons per month. The dilemma identified is how Mphilo Milling can scale and become sustainable through digitalising operations and the maintenance thereof. Through the adoption of digital technologies and the revisualisation of operations, businesses can increase operational efficiency, improve the customer experience and drive innovation. The use of data analytics (predictive and prescriptive), machine learning, AI and IoT can revolutionize the operation of Mphilo Milling.(Eyo-Udo, Ogundipe, Ololade, & Onesi-Ozigagun, 2024). The maintenance of the above-mentioned technologies can improve energy efficiency, as modern updates often include energy-saving enhancements. For example, optimized software and hardware management, such as server load balancing and energy-efficient protocols, contribute to reduced power consumption across data centres. This leads to decreased carbon emissions and aligns digital operations with broader sustainability goals, such as carbon neutrality and resource conservation. Proactive maintenance also strengthens cybersecurity, mitigating the risk of breaches and downtime that can result in costly data recovery and excessive resource use. Furthermore, predictive maintenance, facilitated by data analytics and AI, enhances sustainability by allowing organizations to pre-emptively address potential failures. This predictive approach helps to reduce both operational interruptions and the need for emergency repairs or replacements, thereby conserving resources and reducing waste. The influence of maintenance on digital sustainability is multi-faceted: it reduces environmental impact, enhances energy efficiency, and improves operational resilience. Organizations adopting comprehensive maintenance practices not only reduce costs but also contribute to global sustainability efforts by decreasing the environmental footprint of digital operations. In essence, maintenance transforms digital infrastructure from a potential environmental burden into a sustainable asset, underscoring its critical role in the digital age.

The Strategic Decision Area (SDA) in Supply Chain Management (SCM) is a framework focusing on critical areas where strategic decisions can significantly affect supply chain performance. Each SDA highlights core elements of SCM, such as procurement, production, distribution, and inventory management, all of which are essential for ensuring a smooth and cost-effective flow of goods and information across the supply chain. The selection and management of these SDAs determine how resources are allocated, suppliers are engaged, and customer needs are met. For instance, a company focusing on the procurement SDA would prioritize sourcing high-quality materials from reliable suppliers to maintain production standards, while an SDA focused on logistics might concentrate on optimizing transportation to minimize delivery costs and improve customer satisfaction. In essence, each SDA represents a focal point within SCM where strategic choices can drive efficiency, resilience, and competitive advantage. When examining an assigned SDA, it is essential to consider how it encompasses various strategic facets. Supplier relationships, for example, are a fundamental SDA that can be optimized to ensure the reliability and quality of materials sourced. This can include establishing long-term contracts with reputable suppliers or adopting a collaborative approach to foster trust and transparency. Another key SDA is inventory management, which focuses on maintaining optimal stock levels to meet customer demand without incurring excessive costs or risking stockouts. Logistics and distribution are also critical, emphasizing the efficient transportation and timely delivery of goods to enhance customer satisfaction while minimizing costs. Additionally, technology utilization serves as a transformative SDA, where adopting advanced technology such as automated systems and data analytics tools can enhance operational efficiency and decision-making capabilities across the supply chain. Understanding the focus and objectives of one’s assigned SDA is crucial to aligning it with the broader goals of SCM and other SDAs within the organization. The influence of an individual SDA on a group’s SDA reflects how interconnected these areas are and how they can create synergy or conflict. For instance, a strong focus on supplier relationships in one SDA can have a cascading positive effect on the group SDA by ensuring quality inputs, which impacts production, inventory management, and even customer satisfaction. Additionally, shared resources, such as technology, can lead to synergies across SDAs. If a company implements a centralized IT system under the technology utilization SDA, it enhances communication, data-sharing, and operational efficiency across departments. Aligning objectives across SDAs is equally important; for example, when inventory management is coordinated closely with production schedules, the risk of excess costs and stockouts is minimized. This interconnectedness highlights the need for a cohesive approach to decision-making across different areas of supply chain management. Digitalization is a transformative force influencing various capabilities within SCM. Advanced data analytics allow companies to make informed, data-driven decisions, forecast demand accurately, and identify areas for improvement. Automation also plays a significant role in increasing efficiency and reducing errors, with systems that can manage inventory levels autonomously, keeping stock in check and providing real-time updates. Furthermore, digital tools facilitate enhanced communication throughout the supply chain, promoting faster response times and collaboration between suppliers, manufacturers, and distributors. Supply chain visibility is another crucial benefit of digitalization, allowing companies to track goods in real-time and improve responsiveness to demand changes or disruptions. Digital platforms also enhance customer engagement by offering more personalized services and faster order fulfilment, ultimately boosting customer satisfaction. Overall, digitalization enhances flexibility and agility, allowing organizations to respond quickly to market shifts or unforeseen challenges, such as disruptions in the supply chain. In conclusion, the strategic decisions made within one SDA can profoundly impact and be influenced by decisions in related SDAs. This interconnectedness underscores the importance of cohesive, aligned decision-making across SCM. Digitalization acts as a powerful catalyst, advancing SCM capabilities by providing real-time data, reducing manual errors, and streamlining communication. Leveraging digital tools, companies can achieve greater efficiency, adaptability, and customer satisfaction. The integration of these solutions not only strengthens operational efficiency but also enhances service delivery, enabling companies to respond rapidly to evolving customer needs and changing market conditions.

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.

This paper briefly explores how the adoption of an Enterprise Resource Planning (ERP) system can be influenced by the digitalisation of product or services design. Data has become a key commodity to the success of products. Data gathered from launched products can be used to identify changes required on products (Cantamessa et al., 2020). Data collected on customers also improves the organisations’ understanding of their customers and their needs. It is to the organisation’s (specifically designers’) benefit, therefore, to keep constant interactions with customers to keep up with their evolving needs and to design products that will keep customers who have adopted its products and attract new customers (Cantamessa et al., 2020). Traditionally, product design was done on paper in a form of drawings. More and more, organisations are transitioning more to digital product design, with the use of Computer Aided Design (CAD) and Product Design Management systems. The continuous generation, collection and analysis of data has shifted the design paradigm (Cantamessa et al., 2020). Collaborations between the product/service design function and other organisational functions have become crucial to the product/service development process. Barna and Ionescu (2023) view Enterprise Resource Planning (ERP) systems as a representation of “integrated IT systems that have the role of incorporating a series of modules and functions necessary for the development of the organization’s activity” (p. 1). From a product design perspective, there are potential risks of recapturing the same data, data misalignment within functions, and/or possible human error where design systems (CAD/PDM and PLM systems) aren’t integrated with the ERP system (Janusz, 2015). The integration of product design systems and ERP allows for direct sharing of engineering and manufacturing data through automated processes (Batchelor & Anderson, n.d.). In summary, many organisations are using digital technologies to design, develop and manage their products. The gathering and analysis of data has become pivotal to product’s success. Digitalisation of product/service design encourages ERP the adoption as it improves organisational efficiency and product/service quality.

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).

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.

This report will reflect on how can supply chain design influence or is influenced by location strategies using digitalization. Nowadays, shorter product lifecycles and frequent product range changes influence necessary and reasonable locations, thereby creating supply chain requirements for the future (Henke et al., 2020). According to Marmolejo-Saucedo and Hartmann (2020), traditional supply chains lack integration and consist of various isolated steps; hence, there needs to be a digital supply chain (DSC). With the latest advances in technology, a number of companies have invested in the digitalization of operations and supply chains, such as Fedex, DHL, UPS, etc. DSC is considered an intelligent supply chain that leverages technological advancements by integrating various actors. The Internet of Things (IoT) plays a crucial role in monitoring and tracking location of IP-linked devices to generate information. Enterprises may make accurate decisions when they have the capacity to generate information and knowledge from their supply chain management systems. (Rahman et al., 2019) acknowledge that we are in IR 4.0 and that the Internet of Things has become the main agenda in different industries. Figure 1 illustrates how IR 4.0 technologies such as big data, data analytics, artificial intelligence, and others are disrupting the supply chain environment. Logistics entails movement and storage of products from locations playing a vital role. Due to the COVID-19 pandemic, the GDPs of many countries shrank, resulting in significant losses for businesses. This was due to the risk pause in the supply chain, which was used to deliver finished goods and raw materials using traditional supply chain systems. Pyun and Sung Rha (2021) assert that multinational companies such as BMW, Amazon, and Alibaba have made significant investments in technologies to digitalize their supply chains. One of the elaborate examples BMW was using a cloud-based supply chain receiving production schedules from suppliers. If there were delays in the supply chain because of accidents, they could secure visibility through simultaneous location identification utilizing a global positioning system (GPS). You can use the location of components, ordering costs, lead time, and inventory holding costs to oversee and regulate the inventory system. Data mining tools integrated with Big Data are effective in optimizing inventories inside industrial enterprises. RFID tags equip each inventory item, leading to the generation of substantial data (Taghipour, 2023).