Cloudera Edge To Cloud Analytics Services

Cloud Analytics

It’s difficult to think of a manufacturing process that digital change has not touched. According to McKinsey, industrial digital transformation activities will generate $11 trillion in economic value by 2025. The following main drivers are driving the wide adoption of linked industrial initiatives:

  • Improved Operations, including asset monitoring systems, OEE improvement, and proactive maintenance for process improvement and reduced downtime, thanks to product insights and factual information collected & shared in a constant loop for continuous process development.
  • Enhanced Agility by allowing manufacturers to quickly rotate production in response to shifting market needs or supply chain interruptions.

Manufacturing businesses are harnessing data from hundreds or even thousands of connected assets to produce never-before-seen efficiency and insight in both operations and IT, resulting in totally new value streams that have the potential to alter their connection with their markets.

Manufacturing’s Most Important Drivers

Manufacturers face many issues, most of which have focused on enhancing quality and avoiding unplanned downtime. They can attempting to shift quickly to fulfill market demands or procurement interruptions, which has added agility to the mix. Manufacturers are being pushed to implement IIoT solutions as soon as possible for the following reasons:


Quality control dates back to Industrialization, and manufacturers have consistently improved the quality of their products since then. Although manufacturers have made significant improvements in quality, rivalry for market share and the cost of quality, which can range from 5% to 40% of sales, keeps driving progress. In addition to underutilizing assets, increased scrap and rework charges, warranty costs, and missed sales, poor manufacturing quality imposes considerable downstream costs on practically every part of the organization.

Uptime of the Plant

Downtimes cost manufacturers a lot of money in terms of worker and machine idle time, which drives up the cost per obtaining a complete according to Deloitte, unplanned downtime costs manufacturers about $50 billion each year, and poor maintenance practices can limit plant capacity by 5 to 20%. Unsurprisingly, equipment and plant uptime are the main priority for C-level administrators. According to a survey of manufacturing executives conducted by IndustryWeek, the most compelling reason for investing in technology is to increase productivity by reducing downtime.

Plant Adaptability

Most businesses may instantly see benefits from an IIoT strategy in terms of performance & uptime; however, adaptability has distinguished the winners from the pack. Almost every aspect of the manufacturing sector has been affected by the pandemic. Companies concentrated on expanding capital expenditure and stock buybacks last fiscal year. Still, many are in survival mode, focusing on cash and cash flow, making or purchasing decisions, and enhancing ROI visibility across the board.

A few plants were able to rapidly transition from automotive parts producing to medical device machinery, for example, due to the changing market dynamics. In contrast, others enabled remote equipment tracking and process optimization to continue production operations from centralized control rooms and separation of control points. Others benefited from their digitalization efforts by being able to quickly scale down or up production capacity in response to shifting market demand and supply problems. Since real-time data It all and OT data powering their business, these organizations were able to pivot thanks to their integrated corporation supply chain with manufacturing operations intelligence.

Difficulty assessing the amount and complexity of IoT data:

Many factories use both current and legendary status assets but also devices from various providers, with various protocols but also data formats. Therefore, incorporating an IIoT remedy that can access but also controls Big Information in real for immediate business benefits must answer the underlying challenges. Although the controllers & devices may be linked to an OT system, they are rarely linked in such a way that data can be shared easily with IT systems. Manufacturers require a system that can accept all types of data structures and schemas from the edge, normalize the data, and then share that with any forms of data consumer, including Big Data applications, to support Linked Manufacturing and to deop IIoT use cases.

Handling the intricacy of real-time data:

A data management platform must support real-time analytics on streamed data to promote predictive analytics use cases. To give insights and act quickly, the platform must be able to receive, store effectively, and analyze flowing information in a real or close time.

Data liberation from separate silos:

Specialized operations (innovation systems, QMS, MES, and so on) reward fragmented data sources & data management portals. It helps in tailoring to distinct siloed solutions inside the Manufacturing value chain. These narrow solutions limit corporate value since they only provide a fraction of the knowledge that cross-enterprise data may provide. It also splits the firm and limiting collaboration options. A good platform should be able to ingest, retain, manage, analyze, and process data streams from all locations in the supply chain. You can also combine it with information from Data Historians, ERP, MES, and QMS and turn it into meaningful intelligence. Dashboards, reporting, and predictive analytics based on these insights will be used to promote high-value manufacturing use cases.

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