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Disruptions to the supply chain have become more common in recent years, whether it’s ongoing geopolitical conflicts, inflationary pressures, the recessionary climate or climate change-related weather events. To thrive in this new landscape, manufacturers must consider ways to revamp their operations.

By undergoing supply chain transformation, businesses can create an agile, high-performance supply chain that makes it easier to respond to constantly changing business dynamics and customer expectations, as well as retain a competitive advantage.

It's definitely a hot topic in today's market. According to the IoT Analytics report titled 'What CEOs Talked About,' discussions related to generative AI, specifically regarding use cases and applications, saw a notable increase of +129% in Q2 2023. This suggests a growing interest and focus on the practical applications and potential benefits of digital technologies in reconstructing the supply chain. 

Key trends shaping the manufacturing industry

Several key trends are shaping the manufacturing industry's trajectory. They’re not only driving innovation but also redefining how products are created, developed, and delivered to meet evolving consumer demands.

Today, customers expect products that are not only customized to their exact needs but also sustainable. Simultaneously, a significant lack of digitally skilled individuals poses a fundamental challenge in modern business operations aimed at meeting these heightened expectations.

Above all this, factors such as trade tensions have intensified the risks associated with complex supply chains. In response, businesses are looking at sourcing materials and producing goods in closer proximity to their customers. In this context, the manufacturing landscape is facing unparalleled levels of complexity. 

Digital technologies helping manufacturers adapt their operations 

To combat uncertainties and sustain their businesses, market leaders have already launched various digital transformation initiatives. The emergence of Industry 4.0 has revolutionized manufacturing through IoT technologies like cloud, data analytics, and AI. This includes smart factories that enhance manufacturing with industry 4.0 tech and smart products offering internet-based services.

To achieve these objectives, manufacturers strategically prioritize several use cases in parallel, with larger manufacturers implementing hundreds of individual smart factory projects across their plant network simultaneously.

This suggests manufacturers recognize the importance of establishing a robust process that allows for quick project prioritization and experimentation for maximum impact. 

Top smart manufacturing use cases

Some of the top manufacturing use cases we see from our customers include:

  • Demand forecasting uses historical data to predict future market needs, optimizing inventory. By anticipating demand, businesses prevent stock issues, improving supply chain efficiency and customer satisfaction. This strategy enhances informed decision-making and profitability.
  • Asset maintenance, predictive, and uptime solutions use data analysis to anticipate and prevent equipment issues, minimize downtime, ensure smooth production, and cut costs by avoiding disruptions
  • Servitization involves offering both products and services, fostering customer relationships and added value. Beyond selling products, companies provide maintenance, insurance, and connectivity. This approach generates sustainable revenue streams and builds lasting customer connections
  • Mixed reality for remote inspections and commissioning, the shop floor and in-field service connects experts across various locations. This technology enables real-time guidance and instructions, resulting in enhanced training, maintenance, and troubleshooting for improved efficiency and fewer manufacturing errors

Challenges of supply chain transformation

However, strategically applying these use cases isn’t without its challenges. Each manufacturer faces their own unique scaling challenges that require a calculated approach to processes, planning and implementation to create a sustainable business model. According to Microsoft research on the leading manufacturing companies worldwide, most manufacturers have faced several challenges related to data, change management, incomplete OT-IT integration and applications development.

We also see these and some other common challenges working with our customers. Let’s look into them so you can get prepared once you start your journey. 

Building a well-defined transformation strategy

Often, companies have growth ideas but lack a clear path to achieve them. This could involve transitioning to product servitization for new revenue streams or adopting a more B2C approach to explore alternative routes into new markets.

Typically, they lack both the strategy and suitable software required. The right partner should be able to guide this journey end-to-end. They’ll be able to provide insights into upcoming challenges and help you discover how digital technology can unlock opportunities, creating new business value. Additionally, they’ll assist in identifying ways to release value incrementally and early, ensuring successful value management.

For example, to support any new technologies that are implemented, manufacturers need to create a digital-first culture. Digital transformation in manufacturing can change the way employees perform certain jobs and may even extend to the way they interact with people – either inside or outside of the organization.

That’s why you need a culture that’ll accept and support the transformation. A good digital-first environment is characterized by data-driven decision-making which is not possible without efficient cross-functional collaboration and data sharing culture in place.

Extracting the maximum value from data

Many businesses now have extensive data archives spanning several years, including substantial sales orders and operational performance records. Customers often ask us how they can extract the maximum value from this data. Typically, this involves consolidating information from several sources, including legacy and complex data like weather or currency exchange rates.

Ensuring data integrity is another key concern, with the challenge of alignment, matching, and continuity over time. Data cleansing, occasionally automated with AI, plays a crucial role. Real-time data from shop floor sensors is pivotal for immediate insights and action, especially if quality measures breach thresholds, for example.

Solutions such as business intelligence dashboards offer real-time production insights for informed decisions, boosting efficiency, cutting costs, and refining product quality. Alerting is another key area, delivering information when, for example, a sensor becomes misaligned.

But how do we notify people? And how do we send these messages? Are the alerts merely informing us of circumstances, or do they include recommendations? AI comes into play here. Predictions, recommendations – all of these insights can be derived from the data.

With the launch of Microsoft Copilot for various business applications ranging from CRM to ERP systems, we’re likely to see a significant democratization of AI technology that will fully transform workforce productivity and the way we do business. However, the degree of success will also depend on how effectively you train it using your data, highlighting the importance of data quality.

Data connectivity

We’re seeing more manufacturers showing an interest in establishing robust connections with shop floor assets to unlock enhanced operational efficiency, refine quality control processes, and make more informed decisions.

However, the journey towards successful connectivity begins with a fundamental question: What specific outcomes are you aiming to achieve from the data collected? Whether it's optimizing Overall Equipment Effectiveness (OEE) or elevating product quality standards, defining these objectives is a crucial starting point.

Insufficient data-related skills within the manufacturing business landscape means many manufacturers find themselves unable to effectively link with their machinery or manage the influx of data streams from sensors, highlighting a critical gap that needs to be addressed. Also, the convergence of operational technology (OT) and information technology (IT) often poses challenges, particularly where legacy equipment is still in use.

It's important to recognize that the solutions being implemented by manufacturers require tailored approaches due to the unique demands of each manufacturing organization. Developing applications within a business can be tricky, with not every business having the in-house skills to do this.

Tools such as Power Apps and Power Automate empower companies to create custom applications without requiring extensive coding expertise. Versatile solutions such as these make it easier for manufacturers to create applications that precisely align with their specific needs, boosting efficiency and innovation in the process.

Leveraging artificial intelligence

AI encompasses a range of technologies, including cognitive services like facial, gesture, and sentiment recognition, which can be used on the shop floor to streamline operations.

A main concern for manufacturers is securing data insights that track who operated a specific machine and when. This becomes paramount in industries like food and pharmaceutical manufacturing, where precise information tracing is essential.

AI can also be used to spot trends and patterns, with risk identification being one area that’s gaining signification traction among the manufacturing organizations we speak to. AI can take global big data and highlight supply chain vulnerabilities, enabling proactive risk mitigation.

But AI's capabilities doesn't stop at detection; it also generates actionable insights. For example, it can suggest preferred suppliers to purchase from based on their supply chain history, or issue alerts for impending weather events affecting supply chains.

Despite the growing investment in AI, a recent Gartner survey found that just 54% of AI projects progress from the pilot phase to production, highlighting the challenge businesses have in turning their AI ambitions into reality.

A good partner can support you in navigating and mitigating the challenges associated with integrating AI into your business operations, ensuring stakeholders are engaged at the right time to help you operationalize AI.

Building a sustainable supply chain

Many businesses are making efforts to report on their internal sustainable efforts such as energy consumption, but extending reporting down the supply chain poses challenges (for example, effectively reporting on a supplier's energy usage). To achieve a comprehensive sustainability profile, this reporting must span the entire supply chain.

Sustainability reporting tools provide comprehensive tracking and analysis of environmental and social impacts, enabling businesses to make informed decisions, ensure regulatory compliance, and communicate their sustainable practices transparently. Manufacturers are looking to achieve this connectivity, particularly in linking shopfloor equipment usage with sustainability goals.

Leading organizations are pushing for data standardization among their supply chain suppliers. Increased standardization can make the supply chain more efficient and easier to review, potentially reducing a company's risk.

However, there's more work needed to establish this standardization. As public and regulatory interest grows, having a clear view of your supply chain processes will become even more important. So, expect leading companies to keep investing time and effort to better organize their supply chain data.

Master your digital evolution

Transformation is a long and complex journey. Your preparedness plays a pivotal role in achieving optimal outcomes. Make sure you and your partner have considered all the highlighted challenges and have devised a strategy to effectively tackle them – this approach will greatly contribute to your success.

At Columbus, we work with all business-critical processes, eliminating the need for multiple partners. From finance to supply chain, data management to AI, and security to customer experience, we provide a comprehensive approach to fulfilling your digital transformation needs, ensuring seamless alignment across your entire business.

Feel free to reach out to us today if you’re looking to fine-tune your digital transformation strategy or need any help addressing the challenges we’ve talked about. 


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Tips til lesing

While the manufacturing landscape is changing, one thing remains the same – the pressure to grow revenue. Growth requires evolution. Manufacturers must respond and adapt to changes in today’s market, including evolving customer demands, global supply chain shifts and inconsistent new equipment sales. Manufacturers used to build strong businesses by making products, but that’s no longer enough in today’s competitive landscape. Over the last decade, revenue share from new product sales for manufacturers has declined. Additionally, when there is economic uncertainty, capital purchases tend to be restricted, so new equipment orders are slow. One way manufacturers have stabilized business during uncertain times is to grow their aftermarket service business. This service-based operational model is known as servitization and includes the sale and delivery of spare parts, maintenance and other value-added services. With the growth of smart, connected products, manufacturers have new opportunities to offer customers Internet of Things (IoT) software and services in addition to physical products. According to survey results shared in Microsoft’s IoT Manufacturing Spotlight, manufacturers who responded said 33% of their revenue comes from smart products. These products generate data that can then be used for other value-added services. Over the next three years, respondents expect the penetration of smart products to increase to 47%. Servitization gives manufacturers a way to improve performance, enhance resilience and stay in front of customers after the initial product sale with additional services. In addition to increasing revenue, servitization can improve the customer experience, increase customer loyalty and differentiate your company for a competitive advantage. Plus, margins are higher for services than they are for products. Many manufacturers operate in an open-loop system with no ongoing link to customers. As a result, they lose opportunities to provide additional services after the initial product sale. Servitization changes that. With a product life cycle potentially spanning more than 50 years, a servitization business model is appealing to manufacturers to build recurring revenue. Equipment is more complex and technical than ever before, so it’s simply impossible for customers to have the in-house expertise to service equipment effectively. You are perfectly positioned as the manufacturer of their equipment to understand how to operate and repair equipment and guide customers on ways to maximize their investment. Servitization could benefit you if you’re considering ways to optimize your business. With the right cloud ERP, taking the first steps toward a servitization business model is easy.
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