Why Analytics Presents a Game-changing Opportunity for Players in the Manufacturing Industry
Today data and analytics solutions that leverage artificial intelligence, big data, and business intelligence are defining a C-level agenda that forms the basis for all digital transformation initiatives of players in the manufacturing industry. While manufacturers possess huge volumes of unstructured machine data, not all possess the ability to analyze and extract insights from it. [...]
Today data and analytics solutions that leverage artificial intelligence, big data, and business intelligence are defining a C-level agenda that forms the basis for all digital transformation initiatives of players in the manufacturing industry. While manufacturers possess huge volumes of unstructured machine data, not all possess the ability to analyze and extract insights from it. But with a powerful manufacturing analytics strategy, manufacturing industry players can tap into this information, converting data into actionable insights to improve decision-making and drive revenue.
Though the data deluge may seem like a challenge, it’s crucial to note that if analyzed properly, data has the potential to become an organization’s most valuable asset. Moreover, the evolution of data is poised to accelerating owing to the evolution of Industry 4.0 and the automation of manufacturing processes. Which means to gain control of this data, businesses that operate in the manufacturing industry must leverage new technologies and advanced manufacturing analytics solutions that turn data to insights. However, the manufacturing industry has traditionally been slow to adopt new technologies and many organizations may find it difficult to fully benefit from investments in digital technologies. This article highlights how manufacturing analytics can improve operational efficiency, productivity, and create a competitive advantage by offering new business opportunities within the manufacturing industry.
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Challenges Facing the Manufacturing Industry
Skyrocketing Operational Costs
Today, it would be right to say that the data deluge in the manufacturing industry has given rise to an increase in operational costs that must be addressed to avoid maverick spend. Adopting a comprehensive approach to cost management will help manufacturing companies to cut down on their operational costs while ensuring resource liquidity and productivity. Moreover, the machines used to streamline processes also contribute to the overall operational costs incurred by them.
Advent of New Technologies
The manufacturing industry is currently experiencing a resurrection of sorts owing to the advent of new technologies and lean manufacturing processes. New technologies that have paved their way into the manufacturing industry have helped streamline processes and make better use of data. However, it’s essential to note that this has turned out to be a major pain point for manufacturers as it has not only led to a rise in operational costs but has also resulted in a shortage of skilled workforce.
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Cyber-attacks and data breaches
Cyber-attacks are one among the most common challenges faced by palyers in the manufacturing industry. The use of outmoded data management systems that are incapable of addressing the complexities of today’s cyber threats have made manufacturers’ vulnerable to costly data breaches. To address this issue companies must deploy sophisticated methodologies to secure their networks from data breaches. Apart from leveraging the best practices in the manufacturing industry to thwart data breaches companies must also consider deploying a robust data management system with an enterprise-grade security layer that helps prevent the loss of data.
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Our manufacturing analytics solutions can help you tackle these challenges as it empowers you to analyze huge volumes of data sets to easily spot operational inefficiencies, new opportunities, and generates insights to bring about major transformations.
What is Manufacturing Analytics?
Manufacturing analytics is an advanced approach that leverage analytics methodologies and cognitive technologies to help companies within the manufacturing industry to analyze sensor-generated data to drive operational improvements and streamline processes. It also empowers businesses to identify reasons for machine breakdowns, unlock hidden capacity, and track process improvements.
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4 Key Analytics Use Cases in the Manufacturing Industry
Customer Experience Management
Manufacturing analytics helps companies to assess data from a variety of sources to obtain a single version of the truth and gauge user experience across touchpoints. The use of analytics in manufacturing can also help manufacturers to identify customer preferences, buying patterns, and engagement levels and offer personalized communications via customer touchpoints, such as account or service information.
Product Design & Development
Advanced manufacturing analytics can help businesses to design and develop new products based on the needs of their customers. It also enables manufacturing industry palyers to leverage natural language processing (NLP) to analyze unstructured information such as data from customer support conversations and social media channels, for key concepts, entities, and sentiment. This ‘voice of the customer’ can pinpoint trends or market changes and feed into the design of new products.
Operational Cost Management
Operational efficiency in the manufacturing industry focuses primarily on machine availability in the production process and matters relating to Health, Safety, and Environment (HSE). By leveraging manufacturing analytics businesses can minimize incidents, lost time, and environmental impact by autonomously analyzing critical sensor data. Manufacturers can also monitor the status of machines and their major components, forming the basis for a preventive maintenance regime that automatically reports breakdowns or incidents before they occur.
Automation and quality assurance
Manufacturing analytics has revolutionized every aspect of manufacturing including the back-end manufacturing processes that revolve around quality assurance. It empowers businesses to leverage machine learning techniques, RPA, AI, and rules-based software to interpret data and drive business outcomes.
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