It's Time to Redesign Your Business with Manufacturing Analytics

MANUFACTURING ANALYTICS
Consumer demand has shifted dramatically in recent years, and manufacturers are trying to adapt to this shift. To maintain high product quality, minimize costs, and optimize supply chains, manufacturing analytics has become essential for manufacturers.

Manufacturing analytics is the process of gathering and analyzing data from various systems, equipment, and IoT devices in real-time to get essential insights.

As technology takes over and enhances many of the processes we used to handle with manual labor, we are freed up to use our minds creatively, which leads to bigger and better leaps in innovation and productivity.

– Matt Mong

Manufacturing analytics can assist in maintaining production quality, boost performance with high-profit returns, decrease costs, and optimize supply networks.

This article will outline manufacturing analytics and present a list of possible application cases. It will also highlight the benefits of manufacturing analytics for any shop floor or factory.

Manufacturing analytics: An Overview

With manufacturing analytics, we can streamline and speed up the entire process. Data interchange and automation helps in speeding up the production process. Manufacturing analytics uses predictive manufacturing, big data, Industrial IoT, network virtualization, and machine learning to produce better scalable production solutions.

Manufacturing analytics collects and analyses data from many sources via sensors embedded in machinery to identify areas for improvement. Data is collected and presented in an easy-to-understand structure to illustrate where difficulties emerge throughout the process.

In short, manufacturing analytics collects and analyses large volumes of data to reveal insights that might improve performance. Users can also obtain automated business reports to reply in real-time.


Why Manufacturing analytics is Vital for Leading Businesses

There are numerous benefits of manufacturing analytics that drive any company’s production and overall manufacturing business growth. The benefits of manufacturing analytics fall into three distinct categories as below.

It reduces the overall cost: Analytics may save a significant amount of money if used more efficiently. Labor costs are also reduced due to automation and semi-autonomous machinery. Similarly, preventive and prescriptive maintenance programs may save money while enhancing productivity.

It boosts profits for businesses: Manufacturers can respond swiftly to changes in demand using real-time insights in production, inventory management, and demand and supply forecasting. For example, assume the data indicates that they are approaching their maximum capacity. In such instances, they can increase over time, increase capacity, modify procedures, or tweak other production areas to adapt and maintain delivery times.

Other unforeseen benefits: There are several advantages to the increased capabilities enabled by manufacturing analytics. These benefits include lower energy use, safer environmental practices, fewer compliance failures, and more customer satisfaction.


Five Real-world Applications of Manufacturing Analytics

Predictive Maintenance  

A machine's analytics uses aggregate data from real-time detectors to anticipate when it needs to be replaced or functioning irregularly. This process helps predict machine failure or equipment defects.

Analytics can assist in determining a plant's capacity and how many products are produced by the unit in every production cycle, which is helpful in capacity planning. In addition, analytics may help determine the ideal number of units to create over time by considering capacity, sales predictions, and parallel schedules.

Predictive analytics solutions can automate maintenance requests and readings that shortens the procedure and reduce maintenance expenses. 

Product Development

Product development is an expensive process in manufacturing. As a result, businesses must invest in R&D to develop new product lines, improve existing models, and generate new value-added services.

Earlier, this approach was in place by repeated modeling to get the finest outcome. This approach can now be modeled to a large extent, with the help of data science and technologically superior analytics. Real-world circumstances can be replicated electronically using "digital twins" and other modeling approaches to anticipate performance and decrease R&D expenses.


Demand Forecasting

Many factors that might help in the plan significant capital expenditures or brief breakdowns can be explained using historical data and a few high-impact variable strategies. For example, consider the seasonality of products like ice cream. As a result, historical market data and a few high-impact factors can help explain numerous variables and plan major capital expenditures or short-term shutdowns.  

In addition to demand forecasting, predictive analytics incorporates advanced statistical techniques. With predictive analytics, a wide range of parameters, including customer buying behavior, raw material availability, and trade war implications, may be taken into consideration.

Warranty Analysis

Warranty support may be a load for many manufacturers. Warranties are frequently based on a "one-size-fits-all" approach that is broader. This approach introduces uncertainty and unanticipated complications into the equation.
Products may be modified or updated to decrease failure and hence expense by using data science and obtaining information from active warranties in the field. It can also lead to better-informed iterations for new product lines to minimize field complaints.


Managing Supply Chain Risks   

Data may be recorded from commodities in transit and sent straight from vendor equipment to the software platform, helping to enable end-to-end visibility in the supply chain.
Manufacturing analytics allows organizations to manage their supply chains like a "control tower," directing resources to speed up or slow down. They may also order backup supplies and activate secondary suppliers when demand changes.


Final Words

Businesses should adapt to changing times. Using analytics in manufacturing has altered the business industry and spared it from possible hazards while boosting production lines. Industry 4.0's route has been carved. Manufacturing analytics is the key to true Industry 4.0, and without it, the data produced by clever IoT devices is meaningless. The future is data-driven, and success will go to those who are ready to adopt it. The faster adoption, the sooner firms go ahead of the competition.


FAQ


How can data analytics help manufacturers? 

Data analytics tools can help manufacturers analyze machine conditions and efficiency in real-time. It enables manufacturers to do predictive maintenance, something they were previously unable to accomplish.


Why is data so crucial in manufacturing? 

Data helps enhance manufacturing quality control. Manufacturers can better understand their company's performance and make changes by collecting data. Data-driven manufacturing helps management to track production and labor time, improve maintenance and quality, and reduce business and safety concerns.


What is Predictive Manufacturing?

Predictive manufacturing uses descriptive analytics and data visualization to offer a real-time perspective of asset health and dependability performance. In addition, it helps factories spot quality issues and takes remedial action quicker by eliminating the waste and the cost associated with it. 

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OTHER ARTICLES
Industrial 4.0

Best Practices for Successful Digital Transformation in Industry 4.0

Article | September 21, 2023

Navigating the path to success by unveiling the best practices for thriving in Industry 4.0 through successful digital transformation. Embrace the data-driven decision-making and customer-centricity. The pursuit of successful digital transformation has evolved from a business strategy to a business necessity. It is a vital imperative for organizations striving to survive and thrive in an ever-competitive market. Within this paradigm shift, a journey unfolds that transcends the commonplace and ventures into the realms of strategic innovation. This best practices article is not just a standard guide but a roadmap to excellence. Explore the best practices that propel businesses into the forefront of Industry 4.0. Beyond the surface of technology adoption lies a deeper narrative, one of cultural transformation, stakeholder collaboration, and visionary leadership. Delve into the intricacies of data-driven decision-making, the agility that fuels progress, the relentless pursuit of knowledge, and the unwavering commitment to the customer experience. Each of these elements forms a crucial thread in the tapestry of successful digital transformation. Through compelling case studies and real-world examples, draw inspiration from industry leaders who have not merely embraced change but have harnessed it to redefine their future. 1. Make confident decisions with Digital Twin Combining the physical and digital realms enables seamless integration of the entire value chain, from design to production, while optimizing with continuous data flow. A digital enterprise can harness the limitless power of data by obtaining valuable insights to make quick and confident decisions and to produce best-in-class products through efficient production. The Digital Twin approach integrates the entire product lifecycle with the factory and plant lifecycles and performance data. The end result is a continuous, open cycle of product and production optimization. The digital twin is a comprehensive digital representation of a product or process throughout its entire lifecycle. By creating a digital twin, companies can achieve significant value, such as faster time-to-market for new products, improved operational efficiency, reduced defects, and exploring new business models to drive revenue growth. With the digital twin, companies solve physical issues more efficiently by detecting them early on and accurately predicting outcomes. It empowers them to design and build superior products and ultimately enhance customer satisfaction by better serving their needs. By adopting smart architecture design, companies can continuously realize iterative value and benefits at an accelerated pace. Manufacturing, automotive, aviation, and other industries have adopted digital twins to boost productivity and efficiency. By 2025, the manufacturing industry is predicted to reach a market size worth over six billion U.S. dollars. 2. Vertically Network Various Units in Enterprise Vertical integration in a Digital Enterprise involves the convergence of IT and OT (Information Technology and Operational Technology) to enable seamless data flow from the shop floor to the top floor. The vast amount of data generated by field devices and control units on the shop floor is vital in the context of Industry 4.0, where intelligent data utilization and communication are paramount. Vertical integration generates a comprehensive solution by integrating IT systems at various hierarchical manufacturing and production levels. These hierarchical levels include the field level (interface with the production process via sensors), the control level (machine and system regulation), the production process level (to be monitored and controlled), the operations level (production planning and quality management), and the enterprise planning level. Vertical integration allows for improved communication and collaboration across different departments within the organization. This leads to better coordination, streamlined operations, and increased efficiency across the entire manufacturing ecosystem. A study by the Boston Consulting Group found that companies with a high level of vertical integration were 16% more productive than those with a low level of vertical integration. 3. Horizontally Integrate the Processes in Lifecycle The concept of horizontal integration in a Digital Enterprise ensures smooth data flow throughout the entire value chain. This integrated approach enables the digitalization of the complete value chain, spanning from design and production to service and recycling. By establishing seamless horizontal integration, it eliminates information silos and creates connections that encompass all aspects, from product innovation and manufacturing to product usage and beyond. Horizontally integrated companies focus on their core strengths and partner to support the value chain. Horizontal integration helps information flow between plant-level Manufacturing Execution Systems (MESs) when a company's manufacturing sites are spread out. This allows production sites to quickly share manufacturing data, such as unexpected delays, breakdowns, and inventory levels. Automated cooperation is crucial to supply chain integration in both the upstream (production processes and downstream (the process of bringing the finished products to market) supply and logistics chains. The integration lets a corporation automatically switch production duties between locations. A study byMcKinseyfound that companies that engaged in horizontal integration in the digital age saw their market share increase by an average of 10%. 4. Upgrade Digitalization using Automation Automation catalyzes growth by streamlining operations, breaking down silos, and promoting cross-functional collaboration. With reduced errors and increased efficiency, businesses can scale their operations with fewer resources, fostering a climate of innovation. This increased productivity allows employees to focus on more creative and challenging tasks, leading to higher motivation and engagement. Furthermore, automation provides a better customer experience, essential in today's digital-savvy market. By automating processes, businesses ensure quick access to customer information, leading to increased satisfaction. Lowering costs and expenditures is another significance enabling businesses to eliminate waste, save time, and conserve resources by automating data entry, approval workflows, and financial procedures. Additionally, automation enables efficient management of decentralized global teams from a central hub, further contributing to cost savings. Automation aids in enhancing security measures and simplifying compliance procedures. Businesses proactively identify vulnerabilities and ensure compliance with ever-changing regulations by automating data-intensive tasks. 97% of IT managers feel process automation is necessary for digital transformation. 5. Implement Additive Manufacturing The adoption of Additive Manufacturing (AM)technologies has prompted the evolution of innovative business models that emphasize environmental perspectives. AM has emerged as a transformative solution within the smart manufacturing industry, offering numerous advantages, such as improved labor, energy, and material optimization, enabling companies to respond to changing market demands effectively. AM is particularly time-saving and cost-effective for small-batch complex geometries products, allowing for non-traditional mass customization and shortening the product development cycle. It encourages changes in sustainable business models, including integrating recycled materials, increasing component attributes, and enhancing product lifecycle. AM’s sustainable benefits have garnered significant attention, focusing on reducing waste, optimizing material consumption, and shortening supply chains. Using layer-by-layer production, AM is considered less wasteful than traditional subtractive methods. It also facilitates the creation of products with extended lifecycles through repair, refurbishment, and remanufacturing, promoting sustainability and environmental responsibility. Study data estimates that the cost savings that can be achieved with Industry 4.0 transformations is 50%. 6. Choose the Appropriate Technology The success of digital transformation endeavors hinges on the careful selection of technologies to invest in. Avoid investing in the latest technology just for the sake of digitization, and refrain from rushing into numerous significant changes simultaneously, which may overwhelm employees. Instead, opt to gradually replace legacy systems and synchronize technology with business objectives through the implementation of new procedures. Here’s what Airbus did. Case Study: Airbus Airbus is the market leader in aeronautics and aerospace products and services worldwide. The organization needed a user-centric digital transformation solution to optimize its data analytics, technology, and machine learning tools, but this proved difficult. Later, it embraced open-source technology and consolidated its 15 tools onto a user-friendly platform. In ten months, this helped produce 290,000 visits and 2,200,000 page views. Additionally, the company's service center is now managing 30% fewer incidents. 7. Adapt Company for a Change Digital transformation does not demand extensive technical expertise from management and employees; rather, it necessitates a shift in mindset. By embracing this new mindset and leveraging technology solutions to automate processes for both customers and employees, rapid growth can be achieved within the organization as well as in the external market. To facilitate this transformation, it is essential to identify areas that require change, enhance transparency, and foster a culture of collaboration within the organization. By taking these steps, an organization can effectively prepare for the changes brought about by digital transformation. Case Study: Honeywell The Fortune 100 manufacturer operates in industries such as aerospace and building technology. To improve product quality and make it easier to apply digital strategies, it cut its operations from eight markets to six. Early in its transformation journey, it established a digital transformation group in the company that led digital innovations like data-driven product offerings, IoT-connected devices, and advanced industrial process control. Honeywell Intelligent Wearables eliminated the need for expert site visits, empowered workers to continue learning, improved their performance, and effectively shared their knowledge with peers by connecting field workers with remote advice. In 2018, Honeywell's share price grew from $95 to $174, and revenue went from $40 billion to $43 billion. 8. Integrate Digital Transformation into Business Goals Establish a strong connection between the digital transformation journey and the company's goals. Define the specific achievements to accomplish through digitization efforts. By aligning digital transformation initiatives with business objectives, a company can enhance its effectiveness in completing tasks, retaining existing employees, attracting new talent, and successfully overhauling company culture. Case Study: Cummins Cummins, a manufacturer of diesel and alternative fuel engines and generators is an example of a company that has capitalized on the increased demand for environmentally friendly products. Microsoft cloud compliance opportunities boost data security and IP protection. Microsoft has been one of the world's foremost technology companies for decades. Satya Nadella shifted the company's revenue model away from desktops and accelerated the transition to cloud computing. Cummins now uses Microsoft 365 for information management and collaboration to create a new workplace culture. Cummins' 58000 employees work cross-functionally and globally to stay ahead. Cummins relies on Microsoft 365 for strict security, data management and delivery, and compliance. Final Thoughts Successful digital transformation in Industry 4.0 requires a strategic and holistic approach beyond technology adoption. It demands a cultural shift, stakeholder collaboration, and a clear vision of the desired outcomes. To achieve successful digital transformation in Industry 4.0, organizations must adopt best practices that encompass technological, cultural, and strategic dimensions. Data-driven decision-making, agility, continuous learning, and a customer-centric approach are key elements in this transformative journey. By prioritizing these practices, businesses can navigate the complexities of digital transformation, drive innovation, and stay competitive in the dynamic landscape of Industry 4.0.

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