The Power of Data: Manufacturing Organizations turning into Smart Factories

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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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The power of Data, AI, automation and Analytics can enable manufacturers to increase productivity of their business by providing insightful predictions for better decision making and enabling intelligent automation.

Smart factories can monitor an entire organizations processes, from sales and product design down to the individual operators on the floor and support. Smart factories are powered to get a comprehensive view of all processes, which leads to faster response times, stronger decision-making capabilities, and fewer delays.

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Data is the key to ‘Smart Factories’

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When data is entered and managed manually, it is inconsistent and has missing value to support improvements in the manufacturing processes.

Sensor Data Gathering: Digital sensors installed on sensor-rich machines have enabled faster real-time data availability from factory floors. The availability of high-quality, high-value data enables the vision for “Smart factories of the future”, characterized by timely scheduling of tasks and pro-active actions. Process insights from manufacturing plant floors for all operations have opened doors for real-time surveillance, operational optimization, and adaptive controls.[/text]

Cloud Enablement for Data Storage and Computing: Advancements in computational infrastructure, represented by cloud and edge computing, have made it feasible to manage big data. This supports tasks of different temporal requirements, from process control to production schedules. The emergence of distributed production has transformed the traditionally centralized factories into a more service-oriented, individualized manufacturing resource.

McKinsey has predicted that data will play a central role in intelligent manufacturing, with key technologies including

  • Automated in-plant logistics
  • Data collection across supply chain
  • Data-driven predictive maintenance
  • Automation and human-machine collaboration
  • Digitalized quality system and process control,
  • Digital performance management
  • Smart planning and agile operations.
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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]

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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]

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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]

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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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    5 Secrets of Successful Data Led Organizations

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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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    Effective data analysis can provide companies with an enormous competitive advantage, as corporate managers are able to get fresh insights about trends and customer behaviors that may otherwise be impossible. Using business-critical, real-time insights to identify key business challenges that impact an organization, which must be addressed, becomes a lot easier when data is on hand.

    Gartner predicts – “By 2023, data literacy will become an explicit and necessary driver of business value, demonstrated by its formal inclusion in over 80% of data and analytics strategies and change management programs.”

    Here are some characteristics that today’s successful data-driven companies have adopted:

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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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      How can Organizations transform to be Data-Led and Insight driven

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      Digital Transformation, Data Led, Insight driven

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      How can Organizations transform to be Data-Led and Insight driven?

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      Business leaders of data-driven organizations realize the benefits of leaning from data and insights to make smart business moves. Data-driven organizations derive value from data analytics and the process of analyzing data to gain business insights. An insights-driven organization puts data and analytics front and center in its business strategy and across all levels, with each decision informed by insights gained from data and models.

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      Why data, AI, and analytics matter?

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      Over the past many years, business strategists have been obsessed with a data-driven mindset. But why does data matter so much? Well, the answer is straightforward: Businesses want to make decisions objectively, remove biases and identify inefficiencies. Data can reveal our habits and what our next action might be. It opens the door of opportunity and brings to the front and center, what’s possible. This idea of leading brings the culture to collect and analyze data for decision-making.

      According to McKinsey, 70% of digital transformation projects fail to meet the stated goals. It means most projects revolving around data are not getting the results they are looking for. More importantly, companies are flooded with mountains of data, with no growth in using this information to inform insights and strategies. There are organizations, though, getting it right.

      Here are a few core principles data-driven organizations are following. These principles are traits and behaviors thriving organizations are following.

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      Decide and Plan what to achieve

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      Often organizations are anxious to start using all the data they collect. They have gone through the process of ensuring the data is captured, so the next logical step is utilizing it. Companies are measuring everything, and since data is abundant, businesses end up with hundreds of analytics projects designed to measure or describe each facet of an organization.

      If a business challenge (problem) is decided based on business value it can unlock, then it is easy to come up with the questions to which you need to get an answer. The knowledge of datasets can make it easier to choose what data can help in getting an answer to the question.

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      Understanding the gaps and issues

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      Along the way, organizations will run into data gaps and quality issues. What is meant by data gaps and data quality deficiencies? This might occur when you have multiple manual processes for a task, or maybe you want to measure something less obvious.

      When gaps and quality issues are revealed, data-driven organizations use this as an opportunity to streamline processes. This can include going back to the source systems and forcing more stringent requirements on the inputs of the data. It could mean building new systems to capture data or defining more explicitly the transformational and rationalizing steps needed before the data becomes useful.

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      Data Governance

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      Once the components for understanding have been put in place, data-driven organizations take the time to determine roles and assign ownership or simply put, establish the Who. A lack of roles and ownership leads to scenarios in which no one knows where the truth lies.

      Data-driven organizations identify the different roles and most importantly, assign each one ownership.

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      Best Practices

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      Data-driven organizations see every new project as an opportunity to establish best practices, and to use it as a model for how the next project should be approached. Starting with best practices can accelerate future initiatives.

      Having the right data element is critical in making sure the data collected has insight for decision-making. Here are a few best practices:

      • Set clearly defined goals.
      • Enforce data collection from all sources identified.
      • Ensure data is a central point for the organization not just to be used by one department.
      • Inform and educate everyone on how to utilize data assets for decision-making.
      • Collaboration among all departments and groups is essential.

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      Measuring utilization and adoption

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      As much as companies believe in measuring factors that affect businesses, the importance of measuring analytics usage and adoption also must be considered. Measurement is the key to understanding user behavior, everything from consumption to creation.

      There are plenty of ways these aspects are measured, but these are the three main areas that need to be focused on.

      User Engagement: The goal here is to figure out how well analytics are adopted and how much engagement is happening with users.

      Utilization: The focus is on the insights rather than the platform itself. Here, the focus is on digging deeper to see what things people are looking at and what they are interested in learning and analyzing.

      Performance: It is the balancing act of making sure end users have the experiences they want, getting what they need when they need it. Of course, uptime comes into play here, making sure there is an uninterruptible service, making sure when data is required to make decisions, that data is there. And finally, using normal monitoring techniques for reviewing logs, parsing out alerts from systems, and fixing any hardware failures.[/text]

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      Conclusion:

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      The principles shared here fall into three stages of an analytics strategy. First is the foundation – the place where the framework is set of what is being measured, where it comes from, how it is going to be rationalized, and who will own, manage and use it. Second is the execution phase, starting from how data-driven organizations use tactics that leave audiences wanting more and backing that up with the knowledge of how to deliver. Finally, in the maintenance stage, where iteration is a mantra of every project, attention is paid to measuring uptake and managing platforms, champions built, and every victory celebrated.

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      In the global and fast paced world today where survival is a challenge, the role of intuitions and experience have change reduced from 75% to 25%. Now we need data and skills to drive insights from data to back our intuition and experience. Insights identify inefficiency and vulnerability in processes, operations and product development leading to better and informed decisions. Organizations today need to gather, organize, analyze and represent the data to increase efficiency, reduce cost, target right business problem to solve. We all have a hammer, we need data to know where we need to strike with the hammer.

      Sidharth Mittal

      VP, Account Management

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        5 Ways Digital Transformation benefits businesses

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        Digital Transformation

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        5 Ways Digital Transformation benefits businesses

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        Digital Transformation is no longer a thing of the future or a buzzword, it is essential for every organization that plans to grow and stay relevant in near future. Business leaders agree with the fact that Digital Transformation is vital to their business growth and to beat the competition.

        In simple terms Digital Transformation is about Improving current processes by removing repetitive manual steps, defining new automated operational processes, and leveraging technology advances using AI/ML. It represents a cultural shift to a more agile and intelligent way of doing business, powered by technology.[/text]

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        Here we are highlighting 5 Ways Digital Transformation benefits businesses:

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        1. Revenue Growth

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        • As per a recent SAP survey, leaders expect 23% higher revenue growth in the next two years for organizations that have adopted the culture of Digital Transformation.
        • Digital Transformation has enabled organizations to deliver more value to their existing and future customers without increasing the workforce and increased revenue. Efficient automated processes, better decision making.
        • Increased agility results of Digital Transformation are recipes for growth.

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        2. Cost Reduction

        [/custom_heading][text]One aspect of Digital Transformation is to automate repetitive manual business processes. It reduces the cost being spent on technology debt, legacy software, and systems. With the help of automation tools, AI/ ML/NLP, integrated systems, and processes organizations can save more than 20% in cost. According to SAP Digital Transformation and Executives study, 80% of leaders say the transformation has reduced cost, enabled them to run efficiently, and given them extra cash to invest for the future.[/text][image float=”center” lightbox=”” width=”” is_gallert_item=”” src=”14832″ alt=”” href=”” title=”” popup_content=”” id=”” class=”” style=””][text]Cost decreases from adopting Digital Transformation in global companies 2019, by function.[/text][custom_heading id=”” class=”” style=”margin-bottom: 0px;”]

        3. Customer Retention

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        • Better technology stack enables an organization with capabilities that can help in acquiring, retaining, and making customers’ buying decisions easier.  Same time reducing marketing and advertising spend.
        • Digital tools enable your customers’ lives easier and make you look better in front of your competitors.
        • Businesses that offer an outdated and clunky experience have trouble competing with those who innovate and stay updated with modern technology.
        • Digital Transformation also helps in improving Customer Experience that is the most important need of today’s time.

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        4. Enhanced Customer Experience

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        • The commitment to DT pays off. In a recent survey, 70% of leaders say that they are already seeing increased customer satisfaction.
        • One of the major objectives of DT is enhancing the customer experience using technology.
        • Increased focus on customers’ wants and needs, customer service is improved consistently throughout all touchpoints and channels.
        • As per a Gartner report, more than two-thirds of companies admit they are competing mostly on customer experience.
        • Customer Experience has become the new battlefront for organization growth.

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        5. Innovation in business/technology

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        • Digitalization creates opportunities for industries for new product development and service offerings that would have not been possible to create in past. DT allowed to innovate better and find new niches to expand the business.
        • It is not limited to big players like Amazon and Google, even legacy firms have begun to create new businesses that were beyond their core competencies in past. AI/ML adoption can help in creating new products and offerings by understanding market needs and customer behavior.

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          What AI and its adoption means to business?

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          Digital Transformation, Data + AI + Analytics

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          What Artificial Intelligence and its adoption means to business?

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          AI is no longer a dream for the future: it has become a reality across all industries and spheres of life, especially the business world. As with anything new, it can be a challenge to adopt AI within existing systems. So, how can you harness the power of AI?

          It all starts at the top, business leaders are the change agents, and game-changers, it falls upon them to enlighten their colleagues, employees, and others. They articulate the pros and cons of AI as well as how it can help accelerate business growth.[/text]

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          The Benefits of AI for Business

          [/custom_heading][text]Regardless of your niche, there’s a way that AI can help you. A few broad advantages you can expect with AI integration include:

          • Improved employee efficiency – Automate processes can save time and effort that employees can direct towards other goals instead of being bogged down with repetitive manual tasks.
          • Improve Quality – Using Machine learning for analysis and processes can bring accuracy. That delivers consistently better results and avoid room for error with manual work
          • Proactive than Reactive – AI and its adoption can provide you with predictive analytics allowing you to tackle unforeseen problems, meet customer demands with ease, and find better ways to grow your business.

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          How Do You Increase AI Adoption?

          [/custom_heading][text]Most businesses are well aware that AI could help them innovate but not many understand how exactly that happens. Change agents struggle on how to reap the benefits of AI via driving its adoption. To maximize benefits and increase adoption, we recommend a 2-pronged approach:[/text][custom_heading id=”” class=”” style=”margin-bottom: 0px;”]

          1. Enabling Workforce with data and Insights

          [/custom_heading][text]Our first recommendation is to initially focus on building the main KPI-based dashboards so that power is in the hands of employees and managers. The process of defining these KPIs and dashboards will help you refine your business goals and truly figure out what you need your AI system to accomplish.[/text][custom_heading id=”” class=”” style=”margin-bottom: 0px;”]

          2. Build on the success of the prior step using a four-step approach: discover, plan, act, and optimize.

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          • Discovery involves accessing the state of your data, the capabilities of your people, the feasibility of your system, etc. Assessing your data value is the single most important thing you can do to ensure the success of your AI system. Data is the lifeblood of any AI system and having structured, clean data can make or break AI initiatives.
          • Plan out your mission and vision for your AI solution by defining the obstacles in your business and converting insights into potential solutions.
          • Then we move on to the act – Implementation of the planned activities. We suggest choosing the right problem to solve based on its impact and adoption rate. AI use cases impact is calculated based on which business workflow it can be part of.
          • Lastly, you need to optimize systems, and processes. AI systems thrive on learning and constantly optimizing will ensure that insight is real and valid with changing business, and your system stay trustworthy.

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            Will Artificial Intelligence replace physicians?

            Business Leaders In AI Adoption
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            Artificial Intelligence, Digital Health

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            Will Artifical Intelligence replace physicians and face of healtcare?

            [/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]Imagine yourself walking into the hospital with a humanoid robot greeting you with a calming voice, asks you about your symptoms, and reassures you while giving you a prescription with a smile. While this may sound like complete science fiction, the question remains, “Will Artificial Intelligence replace your doctor in the future?” While we realize the advancement in technology especially in the field of neural networks has been remarkable, AI is enabling physicians with tools and decision-making power but not replacing them, at least not anytime soon.[/text]

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            [text]Here is our CEO, Deepak Mittal’s opinion about AI replacing physicians: Click Here[/text]
            [text]The same opinion other leaders have expressed in a poll conducted by NGI. Results were quite clear, 63% of the respondents believe that AI won’t replace physicians:[/text][text]Artificial Intelligence (AI) acts as an enabler to medical care. AI/ML shines the most when it is assisting physicians in making better medical decisions. More than accuracy, we as humans need human empathy from a physician along with effective treatment.[/text][text]Al/ML is bringing the power of object identification, classification along with question/answering, but a physician’s power lies in linking various pieces of information to make decision. Diagnosing a condition is an np-complete problem (specifically set cover: http://en.wikipedia.org/wiki/Set_cover_problem) and even with quantum computers, np-complete problems cannot be solved in polynomial time. The point can be well proven by the experiment of Waldo.[/text]
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            Waldo experiment:

            [/custom_heading][text]Waldo wears a stocking cap, even in the summer, is skinny, usually wears a striped shirt, needs a haircut, and always hangs out with lots of other characters. Can you find him in the image? And if you did, how long did it take?

            The viral video where a robot built with Google AI finds Waldo from a cluster of images within seconds which a human eye would normally take minutes. But if we change the question to who all needs a haircut in the picture then will the computer do that. That’s where in our opinion, a physician’s power lies. Or better question will be which haircut will look best on which person based on his liking/ethnicity etc. Please note that we are only highlighting two of the thousand parameters that might be going in a physician’s mind. Unfortunately, not all decisions are black and white in our human world.

            Even if we consider fully automated surgeries, we have history books to offer wisdom. The advent and progress in AI has been remarkable, and we have had our fair share of lessons from shortcomings and mistakes like Therac-25 in the past, where admittedly so we’ve realized there is a long, long way to go before AI can even dream of replacing surgeon.

            I would also like to point to the problem with malpractice insurance and FDA approval.  Think of a hospital having 1000 physicians and malpractice points to one physician vs malpractice. Pointing to AI that does the work of 1000 physicians. Imagine a situation where If we may run out of physicians overnight because of one blunder or mistake.

            In nutshell, we believe that it is unlikely we’ll get humanoid “robot physicians” for a long time to come. Though technology will replace some of the more routine aspects of medical care, or improve it but for now. The physicians are here to stay.[/text][clear by=”40px” id=”” class=””]

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              Four factors that hinder Digitization

              Four-factors-that-hinder-Digitization
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              Digital Transformation

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              Four primary factors that can hinder digital transformation

              [/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]Digitization isn’t something that happens overnight. It requires strategic planning and change in company processes and mindset. Your company will observe major changes on the way to digitization including a shift from siloed work to extensive cross-departmental collaboration and a shift towards data-driven decision making.

              These are fundamental shifts in how a company operates and so you may face many challenges along the way. How well you anticipate these bottlenecks and plan around them will go a long way in determining the success of your digitization efforts. Most companies face the following four major bottlenecks when attempting a digital transformation:[/text]

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              1. Acceptance of the status quo

              [/custom_heading][text]It’s easy to stay in your comfort zone. This acceptance of the status quo and a mindset of “this is how it’s always done” can be the first major bottleneck that managers and leaders may face when trying to push towards digitization. The best way to overcome this is to spend time educating your peers and employees on the benefits of digitization, why it needs to be implemented in your company, and how you can make it happen.[/text][clear by=”35px” id=”” class=””]

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              2. Lack of defined incentives (Incentives are a key tool to promote cultural change)

              [/custom_heading][text]The easiest way to motivate a person to pursue a goal is to define what incentives they can obtain by achieving this goal. If this is not done when making a shift towards digitization, it can leave employees feeling like they don’t know what they’re working towards and what will happen once that goal is completed. Clear communication with your team and setting regular milestones and incentives can help create a motivated and open mindset towards digitization.[/text][clear by=”35px” id=”” class=””]

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              3. Fear of change

              [/custom_heading][text]The fear of change is a constant, in most aspects of life, so it’s no surprise that this is one of the major roadblocks in the journey towards digitization. Over the last few decades, we’ve seen companies fail to confront this fear of change when faced with disruption (Blockbuster comes to mind, and traditional cab companies). Many people also fear job insecurity and job losses triggered by increasing automation along with the effort needed to upskill to stay competitive. This can be alleviated by reassuring your employees regarding their role in the company.  what support you can provide them to help them make the transition?[/text][clear by=”35px” id=”” class=””]

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              4. Risk of the unknown

              [/custom_heading][text]Stepping into the digitization mindset can feel like stepping into a whole new world. It can be daunting to face new technologies if you don’t have an understanding of them and how they work. Start tackling this bottleneck by setting up a clear strategy of what you want to achieve.

              How you will implement this strategy across different departments. Consult with industry experts who can help you chart your course towards digitization. Taking on experts is the best way to dispel the risk of the unknown.[/text][clear by=”40px” id=”” class=””]

              [text]All the above four factors are part of your organization’s culture and DNA. And, Digitization requires a cultural change and careful planning to make that happen.

              Digitization is no longer optional in many industries- this is the future of work. Don’t lose out on the power of digitization due to a fear of any of the bottlenecks we’ve outlined. With the right mindset, agile strategies, and the support of experts, you too can make the shift towards  digital future.[/text]

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                The Future of Digital Health Technology: Personalization is the Key

                Future of Digital Health
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                Digital Health, Digital Transformation

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                The Future of Digital Health Technology: Personalization is the Key

                [/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]Over the past decade, the healthcare industry has seen tremendous developments and innovations, and without a doubt, we have only scratched the surface. The inner workings of the healthcare industry are being improved and, by extension, transformed with the implementation of new innovations such as artificial intelligence and other digital health tools.

                For long, health care systems have been provider-centric, meaning that patients visited healthcare providers based on the provider’s schedule as opposed to the patient’s convenience. The new transformations, however, are leading the way into creating a healthcare domain that is more patient-centric and personalized.[/text]

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                What is Digital Healthcare and How it is Helping?

                [/custom_heading][text]Digital health has become an increasingly adapted technology and is functioning as a core component of personalization. It incorporates digital transformation into the healthcare sector by integrating software, hardware, and services. Components such as telehealth & telemedicine, AI-empowered medical devices, electronic health records, mobile health apps, and wearables are a few of the examples of digital transformation in healthcare. Such components are entirely reforming the way one interacts with health professionals. The abundance of data that is collected is shared among the providers to help make accurate decisions about treatment plans.

                The main characteristic of personalized health care is providing the patient with choices and options that address a person’s unique needs and life situations. This ensures that care is offered anytime, anywhere. In such a system, care providers communicate with patients to acquire their definition of health goals and preferences. This helps the care providers make informed health decisions, while also keeping in mind the patient’s wishes and goals. With that goal in mind, healthcare tools primarily manage health conditions and track progress digitally. They also accommodate virtual visits with a doctor or any such caregivers to further support the patients to actively manage their health.

                Mostly, the healthcare sector is divided into two parts, one is the emergency services and the other one is the chronic disease segment. Between the two sectors, chronic disease is responsible for a huge part of the total healthcare costs of individuals. Diseases such as diabetes, hypertension, or heart disease, require somewhat tailored care delivery to one’s personal health goals. To start with, chronic disease symptoms mostly are unique to every person and usually become a part of the patient’s life that requires regular monitoring and treatment.[/text][image lightbox=”” width=”2″ is_gallert_item=”” src=”14605″ alt=”Digital Healthcare” href=”” title=”” popup_content=”” id=”” class=”” style=”padding: 0px 80px 0px 80px;”][text]The entirety of 2020 and 2021 made the world stop and think about the ways of conventional living. When the whole world was restricted from leaving their homes, everyone decided to bring everything home. In 2020, people became accustomed to smart homes, online groceries, and work-from-home set-ups. But unfortunately, the healthcare industries were still lagging when it came to implementing digital strategies. During Covid-times, digital health became a sensation very fast. With the click of a button, one can make informed decisions about their health, track their health progress and connect to provider teams.[/text][clear by=”35px” id=”” class=””]

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                Let’s enlist the factors that make healthcare personalization a hit in the masses.

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                • Healthcare does not fall under one-size-fits-all. As discussed, the same disease can manifest itself very differently between two different people. Therefore, different patients might need different approaches to treatment for the same condition, in order to create their healthiest outcome. When a patient visits a doctor in the office, storing patient history becomes an elaborate exercise. A telehealth tool not only makes the visit easier but also helps collect and store much more data thereby making the data accessible in an instant for further decision making.
                • Have you wondered how Netflix comes up with suggested content that is tailored to your interests? Or Amazon exactly knows your preference in books. Well, by now, most of us are very familiar with the concept of Machine Learning that decodes the “behavioral phenotype”. Implementing this concept in healthcare can help determine how to tailor the data or information for a better-personalized experience. This is just one example of using data for making digital health solutions more personalized and effective. As more devices like wearables are becoming mainstream, the data will become more and more available and accessible. This data can then be used by providers through AI and Machine Learning to analyze and draw a better picture of each patient.

                 

                Now that we know personalized technology is all about a vast amount of data and its analysis, we can say that it has the power to revolutionize healthcare both for common as well as rare conditions. Similar to Big Data, where there is a data dump in an open platform for everyone to access, in the healthcare domain, the technology collects patient data from around the world. From the abundance of data, healthcare providers can have all the resources available. Consider the situation where a person with an incredibly rare condition is located in the US, and the care provider is clueless about the symptoms while a person with the same rare condition is located in Norway but receiving treatment. With a vast number of resources, the care provider can access their treatment data to decide the best course of action.

                The future of data analysis is mostly about risk or threat analysis. To look for ways to prevent loss rather than recover from it. The Healthcare domain is no exception, it is more about preventing disease than treating it. Healthcare providers are focussing more on preventative measures instead of preparing to perform risky or emergency procedures. Personalized healthcare is understanding each patient and their risk factors.[/text][clear by=”35px” id=”” class=””]

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                Conclusion

                [/custom_heading][text]For an industry like healthcare which is predominantly based on a myriad of data, the best and the most problematic part is humans. On one hand, it is often too difficult for humans to stay updated on data and treatment options. On the other hand, caregiving always needs the human touch to comfort an ailing patient. The future of healthcare personalization will eventually automate everything from data entry to medicine distribution to diagnosis and scientific analysis. This will provide the caregivers with everything they need to curate the best-personalized care. Not only that but, while machines take up all the redundant tasks, providers can also focus more on delivering quality care to each patient. Additionally, the machine is precise and beyond human errors, therefore reducing diagnostic errors and providing accurate outcomes.

                In summary, the future of digital healthcare is “personalization and efficiency” and it has already started. With strong and deep-rooted data at their disposal, health care providers will be able to treat patients for the best of outcomes and share their findings more readily and with greater ease. With the combination of digital transformations and the concept of personalization, healthcare will provide the best of both worlds, human touch, and technology.[/text]

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