smart factories, decision-making, data analytics, data-driven, intelligent automation

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The Power of Data: Manufacturing Organizations turning into Smart Factories

[/custom_heading][share facebook=”true” twitter=”true” linkedin=”true” email=”true” size=”small” id=”” class=”” style=”margin-top: 10px;”][clear by=”15px” id=”” class=””][text]“The global smart manufacturing market is expected to grow from USD 277.81 billion in 2022 to USD 658.41 billion in 2029 at a CAGR of 13.1% during the 2022-2029 period.”

– Fortune business Insight

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Data to enable better Decision Making

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By digitally integrating an organizations’ current systems and harnessing data, companies are in a position to develop and refine areas like lean manufacturing and workforce management. It allows organizations to explore new ways of optimizing operations, driving higher productivity, and harnessing talent.

Understanding Managerial Decisions Amidst Uncertainty – Data analytics allows leaders to reinforce decisions through a process of iterative, evidence-based decisions. Educating executives can help reduce the knowledge gap by marrying traditional data analytics with management thinking and decision-making.

Once a data mindset is adopted, the next step is putting that insight into action and building the infrastructure in the organization to make good use of the available data. At its core, being exposed to data analytics, new analytics tools, and new ways to think about data can change how we approach problems and solutions.[/text]

Data to Understand Customer Needs and Product Quality

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The success of IR 4.0 and IoT technologies for the manufacturing industry depends on digitalization, Data + AI + Analytics technologies. It can enable decisions on logistics, risk estimation, cost structures, growth strategies, quality control and improvements, built-to-order and other sales models, and post-sales services.

Manufacturers use data analytics to forecast customers’ needs for products. User data collected includes demographics, purchase patterns, user preferences, search history, browsing history, and more.

While this is a relatively new method of analyzing information, big data can now be used to understand consumer behaviour, which in turn can help improve customer satisfaction. Consumer behaviour knowledge leads to better promotions and target products, increasing sales, improving, and optimizing customer experience.

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Data-driven quality control techniques make a stronger platform to manage quality efficiently. With sensors, RFIDs, and machine-based applications at their disposal, manufacturers can now collect product quality data, such as various parameters, including location, machining, tolerance, and geometrics.

Computer vision can perform all-round quality monitoring, detect defects early, and provide a quick diagnosis. The data gathered can also be used to identify the root cause of product production failures. Using the latest technology, defects can be detected, diagnosed, and addressed before shipment reducing returns/refunds. Using data mining and data integration issues with equipment and inefficient procedures can also be detected.

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Data to Increase Overall Equipment Efficiency (OEE)

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OEE, or Overall Equipment Efficiency, a measure of performance, can be broken into three parts: Quality, Productivity, and Availability. As the single most important measure of production measuring KPI: what proportion of the operational time a facility has been productive.

A smart factory uses cloud-based data analytics, AI/ML models, and Automation capabilities to speed up efficiency programs. With technologies such as, IoT, ML, and analytics, companies can build a hyper-connected production ecosystem powered by data to identify, predict, and avoid unplanned downtimes, from equipment malfunctions to quality issues.[/text]

Data to Enable Intelligent Automation

[/custom_heading][text]Predictions made by Gartner indicate, “By 2025, more than 90% of enterprises will have an automation architect, up from less than 20% today.”

According to McKinsey, “At its core, Intelligent Process Automation (IPA) is an emerging set of new technologies that combines fundamental process redesign with robotic process automation and machine learning. It is a suite of business-process improvements and next-generation tools that assists the knowledge worker by removing repetitive, replicable, and routine tasks. And it can radically improve customer journeys by simplifying interactions and speeding up processes.”

Manufacturing industries are facing myriad challenges — from reducing downtime and having to meet ever-increasing regulations, to supply chain complexities, and growing skills gaps. Staying ahead of evolving demands in manufacturing is a tall order.

All these challenges have presented an excellent opportunity for manufacturing companies that adopt AI and automation technologies to speed up their journey to digital transformation. AI, Automation, and advanced analytics is helping to present a realistic view of operational efficiencies, workforce costs, and allow better decision-making, thereby transforming the landscape of manufacturing unit.

With digital robots, manufacturers are processing massive amounts of data to optimize order fulfilment, sourcing, scheduling appointments, and alerting. With predictive analytics solutions, maintenance engineers can predict errors and be able to fix them before the equipment is significantly affected. This technology allows for deep inspections of quality, which are far more detailed and robust than those done by humans.[/text]

Conclusion:

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The move towards automated software, connected equipment, and data analytics are being mirrored by manufacturing companies looking to optimize their factory floors through recent technological advances. People + Process + Technology with data when brought together in the right mix can transform a manufacturing unit into a digital enterprise, making them a basis for robust smart factories. As manufacturing companies continue to embrace smart factory technologies, they will enjoy the benefits of maintaining predictive maintenance in their machines, better data stream management, and cost savings across a range of areas.

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data-driven organization, data-driven, data-driven culture, data analysis, insight driven

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5 Secrets of Successful Data Led Organizations

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“The benefits of a data-driven culture is to examine and organize the data with the goal of better serving one organization’s customers and consumers,” says Alan Duncan, Vice President Analyst, Gartner. “It also bolsters and speeds up business decision-making processes.”

Data-driven organizations are known to consistently outperform those that do not, making informed, efficient decisions that increase profitability and reduce costs.

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Creating a Data Driven Culture

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Every organization has different levels of expertise and people from widely different backgrounds but ultimately all should be united by the same goal. Even though there may be people who will not understand what the others are doing but they should know they are the same team, and that’s where the culture comes in.

Focus on strategy while building a data driven culture is the need of today’s time. We need to establish a set of practices that brings together data, talent and tools in such a way that data becomes a default backbone of company operations.

Many times, CEOs tackle data solely from the perspective of a business strategy, but implementing a successful strategy is not possible unless a company’s culture has already bought into the idea of being data-driven.

To help foster this culture, data needs to stay available, and employees need to feel encouraged to incorporate facts and statistics into their reports, status and information provided at all times.

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Data Accessibility to Everyone

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Next, is making data relevant and available for everyone. Sometimes even the best data-driven companies still reside in silos making the data hard to access.

Organizations cannot ignore the importance of data-driven culture in today’s accelerating and fast-paced world.

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Leaders Leading by Example

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Organizations that are data-driven have at the helm strong leadership. It is a leadership that inspires and promotes a data-driven culture. Such leaders make it their priority to provide data, tools, and training to their employees so that they can be data-driven. Leaders of data-driven organizations ensure to garner support from rest of the organization, and the senior management team to deliver results.

It is one thing to require that your people work with data, but it is quite another to show that you have the ability to drive decision-making and critical thinking using it. When CEOs and other corporate leaders set an example, they are also more likely to make the technology and human capital investments needed to power a data-driven organization.

Successful business leaders have quickly jumped on the bandwagon and have invested in training and showcasing how data is driving important business decisions.

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Creating a Strategy

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A thoughtful strategy is, of course, crucial for the success of almost every enterprise effort, and data initiatives and analytics are no different. However, there are still a number of companies that are yet to achieve their data and analytics goals, and an increasing proportion admits that a lack of a strategy in these areas is a major barrier to success.

To drive revenue in an organization it is imperative to be data driven as well as insight driven. Implementing a data-driven plan can help businesses make more informed decisions and turn customer insights into profits.

Businesses that are successfully driving data have identified creating a strategy as the most critical element to meeting their company’s objectives.

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Treating Data as asset that provides ROI

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Business leaders must see data as a raw material to power analytics and decisions. Data should be treated as an important company product that needs to be packaged and distributed among groups throughout the enterprise.

A product manager’s responsibility to consumers is to build a variety of revenue streams through channels, segments, and markets. In the same way, the owners of each data domain act as product managers for data, and their effectiveness is tied to revenues, satisfaction, quality, and other such measures.

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Conclusion:

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To be data-driven, organizations must rethink their approaches to using data in ways that go beyond technology. Becoming a data-driven organization also requires changes to the culture of the organization, the operating model, and the realization of real business value via use cases. Becoming a data-driven business requires having a coherent, holistic data strategy applied throughout an organization.

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