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SMART MANUFACTURING IoT & data analytics – Catalyst of the smart factory

Dec 11, 2019

Today’s manufacturers must operate at breakneck speed to keep pace with the everchanging customer demands, market trends and global competitors in a rapidly evolving marketplace. Yet many traditional challenges, such as increasing costs, unplanned downtime, Quality Control (QC), evolving business requirements and aging infrastructure, continue to hamper progress. The article throws light on the equation of how IoT plus data analytics equals smart manufacturing.

The manufacturing sector is no stranger to change. Over the course of three industrial revolutions, companies have relied on everything from water and steam to electricity and information technology to power their operations, provide services and make products. But the evolution taking place with Industry 4.0, which McKinsey & Company defines as ‘the new wave of technological changes that will trigger a paradigm shift in manufacturing,’ may be the most significant change of all.

The catalyst for this shift, in which ‘smart factories’ can respond in real time to quality issues, equipment outages and other production challenges, is a combination of several interrelated technologies that are maturing at the same time, it being, the Internet of Things (IoT), advanced data analytics, Artificial Intelligence (AI), Machine-to-Machine learning (M2M) and cloud computing.

Improving production

Unplanned downtime from equipment breakdowns, unforeseen bottlenecks, supply chain disruptions, labour shortages or changing customer orders can idle assets, resulting in loss of production time and revenue.

Taming complexity

As the manufacturing sector moves from mass production to mass customisation, QC is becoming increasingly complex. Even the most minor variations in production can negatively affect the quality, leading to widespread recalls, costly lawsuits, consumer mistrust or exorbitant warranty expenses. Also, production line irregularities can produce enormous waste.

Finding flexibility

Legacy systems based on proprietary or outdated technologies can’t be integrated with new applications without costly customisations, thereby limiting their extended functionalities. In addition, legacy systems can be expensive to maintain and can divert resources from more-innovative initiatives. In response, manufacturers are turning to IoT technologies that combine manufacturing assets with embedded sensors, advanced analytics, AI and cognitive computing. The goal is to generate digital intelligence across the entire value stream.

Advanced analytics at the core

At the core of IoT and its potential to create value is advanced analytics. According to research firm Gartner, advanced analytic techniques include machine learning pattern matching, cluster analysis, multivariate statistics and a variety of other methods. Using capabilities of advanced analytics, manufacturers can aggregate, sort and analyse the vast volumes of information generated by the four Ms of manufacturing, blend structured and unstructured data and convert this data into actionable insights. These are some of the benefits:

  • Improving quality

    Inconsistent product or component quality can reduce yields, create costly waste and cripple production capacity. Consider, for example, a manufacturer whose polymer mixing process continues to produce output of inconsistent quality. The need to scrap poor batches results in huge costs and compromised production capacity. Worse yet, if left undetected, this material issue creates inferior product quality down the line and can lead to losses of millions of dollars in revenue.

    An advanced analytics platform that integrates a wide range of production and sensor data could visualise, analyse and diagnose the mixing process. As a result, the production engineering team can understand the correlations and can cause/effect, leading to inconsistent or poor-quality output from a wide range of variables.

    This type of advanced analytics eliminates poor-quality output. It helps manufacturers boost average yields and reduce operating costs as the focus shifts from scrapping poor batches to optimising manufacturing processes. By adding Machine Learning capabilities, the system can also adapt to changing conditions, such as new product designs, increased product variations and new or changing ingredients.

  • Maximise performance of critical assets

    Equipment failures can cost manufacturers millions of dollars in downtime and productivity loss. One survey of the auto industry manufacturing executives by Thomasnet.com showed that every minute of stopped production costs an average of $22,000. Advanced analytics and Machine Learning can help avoid production shutdowns by monitoring sensor, event and historical data to detect trouble spots. Using these types of prescriptive analytics, manufacturers can proactively identify machineries requiring attention and conduct necessary repairs under controlled conditions before it stops working.

    Consider air compressors, an essential part of most manufacturing and production processes – they are used to power equipment in 90% of all industrial companies. By deploying a condition-monitoring system that uses advanced analytics, manufacturers can track the machine’s performance in real time. The system will send alerts about conditions, such as excessive pressure or rising temperatures, that could lead to breakdowns and unscheduled production halts. Rather than simply reacting to production halts, maintenance becomes a proactive, integrated part of production.

    Detecting inaccuracies in the early stages of a production cycle can also offer a critical advantage. By pre-emptively detecting production line problems, such as nonconforming parts or improper calibration, manufacturers can decrease the likelihood of producing defective products, thereby improving throughput yield without having to commit time, money and resources into rectifying defects.

    Improving asset uptime can also significantly boost plant productivity. An IoT approach can result in a 20% to 25% increase in production volume and a 45% reduction in downtime. By eliminating unknown problems, such as equipment failure, manufacturers can optimise production schedules and shorten lead times, for improved quantity and quality of finished goods.

    A detailed understanding of equipment performance through analytics offers several benefits. Using analytics to monitor production line quality can help identify quality problems quickly and improve yield. Additionally, analysing historical equipment performance data and real-time data can reduce maintenance costs and increase equipment availability. Manufacturers can also more easily adhere to factory safety and regulatory compliance standards by taking the time to convert IoT-generated data into actionable insights, such as how to prevent system vulnerabilities.

The wisdom of a co-creation approach

In today’s highly competitive digital business world, manufacturers must act fast and pivot quickly to meet fluctuating market trends and consumer demands. IoT, combined with advanced analytics as part of a broader digital transformation strategy, can help manufacturers create new business models, improve operational efficiencies and drive innovation in products & services. Yet, nearly 70% of all digital transformation initiatives are considered unsuccessful, according to data from McKinsey.

Many manufacturers lack the multidomain experience and expertise required to create innovative solutions that will lead to successful digital transformation. Although they have in-depth manufacturing industry knowledge, it’s rarely accompanied by a deep understanding of IoT, computing and communication technologies, predictive analytics or AI. However, by partnering with a vendor that has both Information Technology (IT) and Operational Technology (OT) expertise in data analytics, a manufacturer can co-create solutions that cater to unique needs and business requirements. This innovative co-creation approach to IoT and advanced analytics includes four critical steps:

Step 1: Engage

The vendor and manufacturer work together to create a shared vision, understand pain points and goals, conduct discovery in reviews and workshops and analyse data. They discuss options and distil abstract concepts into a prioritised list of use cases to address.

Step 2: Build a model

A team of data scientists sifts through various data sets. Then they cleanse the data, standardise and enrich it and choose the right analytics techniques and algorithms to build an analytical model to fit a manufacturer’s use case.

Step 3: Create the solution

Solution developers deploy an analytics platform and tools. Then, they build the solution model code and algorithms, user-experience wire frames, user-interface design models and templates. Lastly, they integrate these with IT tools for analysis. The concepts of the solution are exposed to pipelines of real data and proven to deliver the expected results. Co-creative spaces help ensure effective workshop processes and simulation tools produce positive results.

Step 4: Test and validate

Delivery engineers integrate the solution with a customer’s Operational Technology (OT) and IT systems in an operating environment. Next, they integrate live data, deploy analytical pipelines, test robustness and scalability and validate business outcomes and Key Performance Indicator (KPI) results to ensure optimum value.

By drawing stakeholders directly into the innovation process and supporting them with an ecosystem of partners and expertise, a co-creation approach fosters a deep understanding of a manufacturer’s pain points. And it solves issues with a customised solution rather than an off-the-shelf tool. As a result, co-creation delivers the following benefits:

  • Offers a low-cost methodology for developing new products and services

  • Sparks innovation and advances complex projects

  • Reduces risks associated with implementing new digital initiatives

  • Helps speed up desired outcomes

Together, these benefits provide a solid foundation for business transformation.

Analytics for long-term productivity

Today’s manufacturers are under unprecedented pressure to produce high-quality products at record speed and with unmatched precision. To keep pace with fluctuating market trends and satisfy rising customer expectations, manufacturers must embrace IoT technologies and leverage advanced analytics to bring insights into asset health, output quality and operational processes.

However, just as no two companies are the same, no two IoT solutions are the same. For this reason, it’s critical for manufacturing firms to work with a partner that has both OT and IT expertise in data analytics to co-create solutions that address those unique requirements and specific needs. This type of partnership can help realise the promise of IoT and enable companies to enhance product quality, cut operational costs and optimise manufacturing operations for long-term rewards

Courtesy: CIO & Hitachi Vantara

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  • The 4 Ms of manufacturing

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