Smart Factories : Time to make Manufacturing smarter & Agile!
Manufacturing is directly proportional to a country’s economic growth, and it’s crucial.
The link between Manufacturing and economic growth is critical. Every dollar spent in Manufacturing adds to the economy; this is the most massive multiplier of any sector.
Investment in Manufacturing attracts jobs, opportunities & growth to the industry. That said, The industry is transforming and getting smarter every day.
We will have to understand Smart Factory when we are on the cusp of the Fourth Industrial Revolution.
Smart Manufacturing has different elements such as interventions of emerging technologies to mitigate the gaps, enhancing growth, bringing innovation to the entire manufacturing lifecycle, and increasing public, private partnerships (PPP).
Smart Factories, Smart Workers
New technologies such as Industrial robotics, Drones, IoT, 3D printing, Artificial Intelligence are on the verge of revolutionizing Manufacturing.
Most of the manufacturers, around 71%, are already adopting 3D printing in some way, while others, approximately 25 %, are expected to adopt in the future.
The growth of these emerging technologies is on the verge of becoming mainstream.
Manufacturing jobs are becoming increasingly high-tech, requiring a demand for more advanced skills, and as a result, many manufacturers see the talent shortage worsening in the coming years.
However, development in technology and innovation comes with more globally competitive industry, and a more robust economy and an increase in skilled jobs result in higher wages. Many manufacturers also believe that the adoption of advanced manufacturing technologies will result in hiring additional employees. The industry is seeing advanced Manufacturing as a job creator, not a job killer.
Technologies of Industry 4.0 in Smart Factory
The manufacturing practice adopted by smart factories — smart Manufacturing — is the most optimized application of technologies arising from the fourth industrial revolution known as Industry 4.0.
The smart factory is not about deploying one software across the entire shop floor and seeing immediate improvements in the production process. A combination of various Industry 4.0 technologies contributes to the optimization of intelligent Manufacturing. Here are the five most critical enabling technologies:
- Industrial IoT (IIoT)
IIoT refers to interconnected devices, machines, and processes linked by data communication systems to facilitate the exchange and the use of data between people and machines. Typically, these instruments have sensors that collect meaningful data points on a cloud or offline database to track and identify ways to improve Manufacturing. IIoT enables operational efficiency, control, and visibility into actionable vital metrics.
2. Sensors
Sensors attached to devices and machines help collect distinct data points at specific stages of the manufacturing process, providing instant visibility into various layers of the shop floor. For example, temperature sensors in a cleanroom can track and detect the climate in a lab and share that data through an IoT gateway. The data can then self-correct with AI (Artificial Intelligence) or alert relevant team members for review.
3. Cloud Computing
Cloud Computing allows smart factories to store, process and share data with greater flexibility at a lower cost than traditional on-premise alternatives. Interconnected devices and machines on the shop floor benefit from quickly uploading large amounts of data that can be distilled to provide feedback and make decisions near real-time.
4. Big Data Analytics
The accumulation of data over time can provide insights into how efficient the production process is, which key metrics to focus on, and what systems are underperforming. The sheer size of Big Data can spot error patterns and run predictive quality assurance with high accuracy. The presentation and the timing of big data analytics — being delivered the correct information at the right time — enables shop floors to improve optimally and quickly.
Benefits of Smart Factory
Smart factories optimize efficiency and productivity by extending the capabilities of both manufacturing devices and people. By focusing on creating an agile, iterative production process through data collection, intelligent factories can aid decision-making processes with more substantial evidence.
By continuously improving the productivity of manufacturing processes, intelligent factories can lower costs, reduce downtime and minimize waste. Identifying and reducing misplaced or underused production capacities mean opportunities for growth without investing in additional monetary and physical resources.
Smart Levels: Four Levels of Smart Factory
These four levels of data structure can help you evaluate where you are on the progress to becoming a smart factory and what steps you need to take to make advancements to the next level.
Level One: Connected Data
This is likely the current status of most factories. Data is available but not accessible. Sorting and analyzing data requires manual work and can be highly time-consuming, adding more inefficiencies to the product improvement process than intended or needed.
The first step to enabling a Smart Factory is connecting your data and integrating disparate sources into a single source of truth that continuously gathers and tracks production data. With the data in one location and always available, problem-solving becomes almost frictionless. When an issue occurs, operators and engineers can access the data in the system using data visualizations and dashboards — essentially leveraging the system as a query engine. With easy access to all the data, engineers can answer questions quickly, increasing plant productivity and agility in weather-changing environments.
In addition, a connected data infrastructure enables real-time monitoring and remote monitoring of the factory floor. This allows engineers to focus their time on addressing high-value issues such as process optimization, waste reduction or quality improvements. However, predictive analysis, which enables factories to improve before problems occur, still requires a significant amount of time, effort, and engagement from engineers. Manufacturers must leverage machine learning technologies that enable predictive and prescriptive analytics to move to the next level.
Level Two: Predictive Analytics
At this stage, data is presented in a more digestible form. Information is structurally organized and appropriately sorted in one location with additional systems that help visualize data and display dashboards. The factory can perform proactive analysis, although this may still require some time and effort.
The intelligent factory shifts manufacturing operations from reactive problem solving to proactive analysis and improvements at level two. Predictive analytics enables operators and engineers to take preventative action to avoid significant downtime or quality failures.
By building on the previous level’s data architecture and adding new capabilities such as machine learning and artificial intelligence, manufacturers can predict and prevent problems on the factory floor. Machine learning technologies typically require three to six months of historical data for accurate predictions but allow you to start generating insights almost immediately, depending on your product mix.
Predictive analytics, combined with a connected data infrastructure that aggregates all your production data, creates an intelligent system that quickly identifies insights and predicts failures more accurately. Real-time alerts deliver valuable information to the appropriate person allowing them to proactively take action. The main benefit of predictive analytics is that factory personnel do not have to query the system or perform manual process analysis to solve production issues.
Level Three: Prescriptive Analytics
Active data means data that can perform proactive analysis using machine learning and artificial intelligence to generate insights without much human supervision. The system can pin key issues and anomalies to predict failures with high accuracy and inform relevant people with valuable insights at the right time.
The third level of an intelligent factory takes production optimization one step further. Instead of predicting when failures might occur, machine learning technologies recommend settings through prescriptive analytics that allow you to optimize production. These setting recommendations not only allow you to replicate your most efficient runs more consistently but convert years of best practices from seasoned veterans into processes that new operators can follow.
By analyzing historical production data, prescriptive analytics isolate the variables and production settings that contribute to your most profitable and least profitable runs. These recommendations are sent to engineers who can review the insights and make process changes to maximize throughput without sacrificing product quality. By following the recommended settings, manufacturers can eliminate inefficiencies and waste throughout their production lines and increase contribution margins.
Level Four: AI-Driven Automation
At this stage, machine learning can generate actionable solutions to the identified issues in the earlier stages. The manufacturing machines and devices connected to this module or system can then execute those changes without human intervention. Collecting data, identifying issues, and generating solutions happen in sequence with little to no human input.
At level four, AI-driven automation deploys the recommendations identified by analyzing manufacturing data. For example, a machine learning model will identify an optimization, then generate and send the recommended settings in real-time to the machine, where it is automatically executed. In such a closed-loop artificial-intelligence-controlled production line, the time it takes to perform on an insight discovered by the system becomes minimal.
Achieving level four requires large datasets and enough validated cases to provide the information needed for the system to “know” the impacts of a production change. The time needed to move from level three to level four varies based on the time it takes to gather the necessary datasets.
True AI-driven automation is still the technology of the future. There will always be benefits to having a person reviewing and accepting machine recommendations. However, it has tremendous help when it comes to hazardous operations. Dangerous processes or production elements that have previously needed an operator can be automated with an operator supervising from the sidelines, significantly reducing the likelihood of a safety incident.
Building A Smart Factory
Approaching Industry 4.0 and moving to a smart factory as a journey rather than a single project makes you realize value much quicker. Building a proper foundation through the right data infrastructure and data acquisition processes will enable you to scale your Industrial IoT initiatives at a much faster rate. Jumping straight into advanced analytics while still siloed data results in data variability that quickly becomes mired in complexity.
Predictive and prescriptive analytics, along with AI-driven automation, requires both live and historical data to make accurate predictions. Additionally, machine learning technologies operate in a continuous loop as people, processes and data change. A step-by-step approach allows manufacturers to progress through natural evolution.
In the earlier levels, you learn more about data systems in general and the data they need for their specific applications. You’ll begin to amass the necessary datasets to enable machine learning and artificial intelligence applications as you progress. Enabling predictive and prescriptive analytics then allows you to identify and execute production-process improvements based on data. With this systematic approach, manufacturers will build an intelligent factory more quickly and with less frustration.
The Future Of Manufacturing
Manufacturers looking to capture growth and protect long-term profitability should embrace digital capabilities from corporate functions to the factory floor. Smart factories, including greenfield and brownfield investments for many manufacturers, are viewed as one of the keys to driving competitiveness.18 More organizations are making progress and seeing results from more connected, reliable, efficient, and predictive processes at the plant. In 2022, 45% of manufacturing executives surveyed expect further increases in operational efficiency from investments in industrial Internet of Things (IIoT) that connect machines and automate processes.
5 manufacturing industry trends to watch
One: Workforce shortage
Preparing for the future of work could be critical to resolving current talent scarcity
Record numbers of unfilled jobs are likely to limit higher productivity and growth in 2022, and last year we estimated a shortfall of 2.1 million skilled jobs by 2030. To attract and retain talent, manufacturers should pair strategies such as reskilling with a recasting of their employment brand. Shrinking the industry’s public perception gap by making manufacturing jobs a more desirable entry point could be critical to meeting hiring needs in 2022. Engagement with a wider talent ecosystem of partners to reach diverse, skilled talent pools can help offset the recent wave of retirements and voluntary exits.
Manufacturing executives may also need to balance goals for retention, culture, and innovation. As flexible work is taking root in offices, manufacturers should explore ways to add flexibility across their organization in order to attract and retain workers. Organizations that can manage through workforce shortages and a rapid pace of change today can come out ahead.
Two: Supply chain instability
Manufacturers are remaking supply chains for advantage beyond the next disruption
Supply chain challenges are acute and still unfolding. There’s no mistaking that manufacturers face near-continuous disruptions globally that add costs and test abilities to adapt. Purchasing manager reports continue to reveal systemwide complications from high demand, rising costs of raw materials and freight, and slow deliveries in the United States. Transportation challenges are likely to continue in 2022, including driver shortages in trucking and congestion at US container ports. As demand outpaces supply, higher costs are more likely to be passed on to customers.
Root causes for extended US supply chain instability may include overreliance on low inventories, rationalization of suppliers, and hollowing out of domestic capability. Supply chain strategies in 2022 are expected to be multipronged. Digital supply networks and data analytics can be powerful enablers for more flexible, multitiered responses to disruptions.
Three: Smart factory initiatives
Acceleration in digital technology adoption could bring operational efficiencies to scale
Manufacturers looking to capture growth and protect long-term profitability should embrace digital capabilities from corporate functions to the factory floor. Smart factories, including greenfield and brownfield investments for many manufacturers, are viewed as one of the keys to driving competitiveness. More organizations are making progress and seeing results from more connected, reliable, efficient, and predictive processes at the plant. Emerging and evolving use cases can continue to scale up from isolated in-house technology projects to full production lines or factories, given the right mix of vision and execution.
US manufacturers have room to run with advanced manufacturing compared to many competitors globally. Advanced global “lighthouse” factories showcase the art of the possible in bringing smart manufacturing to scale. Investment in robots, cobots, and artificial intelligence can continue to transform operations. Foundational technologies such as cloud computing enable computational power, visibility, scale, and speed. Industrial 5G deployment may also expand in 2022 along with advances in technology and use cases.
Four: Cybersecurity
Rising threats are leading the industry to new levels of preparedness
High-profile cyberattacks across industries and governments in the past year have elevated cybersecurity as a risk management essential for most executives and boards. Surging threats during the pandemic added to business risk for manufacturers in the crosshairs for ransomware. An expanding attack surface from the connection of operational technology (OT), information technology (IT), and external networks requires more controls. Legacy systems and technology weren’t purpose-fit for today’s sophisticated network challenges. Now, remote work vulnerabilities leave manufacturers even more susceptible to breaches.
Manufacturers should look not only at their cyber defenses, but also at the resiliency of their business in the event of a cyberattack. Cybercriminals can cause harm beyond intellectual property theft and financial losses, using malware that now ties in AI and cryptocurrencies. They can also shut down operations and disrupt entire supplier networks, compromising safety as well as productivity. A patchwork of regulations for different industries could be consolidated under the current administration’s “whole-of-nation” approach to protect critical infrastructure. The potential for additional oversight is likely to prompt more industrials to rethink preparedness for crisis response.
Five: ESG investment
Manufacturers are likely to bring more resources and rigor to advancing sustainability
The fast rise of environmental, social, and governance (ESG) factors is redefining and elevating sustainability in manufacturing as never before. Cost of capital can be tied to ratings on ESG, making it a priority for organizational financial health and competitiveness. Expectations for reporting on diversity, equity, and inclusion metrics in manufacturing will likely continue to rise. Board diversity, while progressing slowly, is also showing some momentum. To attract talent and appeal to workforce expectations, most manufacturers are making ESG efforts more visible.
Depending on a manufacturer’s end markets, environmental accountability is increasingly a focus. To develop and deliver against net-zero or carbon-neutral goals, more organizations are dedicating or redesigning sustainability roles and initiatives and quantifying efforts and results around energy consumption. And the fast-evolving ESG landscape may require close monitoring in 2022 for manufacturers. Many organizations are complying voluntarily within a complex network of reporting regulations, ratings, and disclosure frameworks. But regulators globally are also moving toward requiring disclosure for more nonfinancial metrics. Proactive approaches may help manufacturers stay ahead of the change and create competitive advantage
Gartner Identifies Top Five Business Trends in Manufacturing for 2021
Top Trends Include Digital and Product Experience, Data Monetization and Ecosystem Partnerships
Gartner, Inc. identified the top five business trends that will impact the global manufacturing sector in 2021. These trends will drive business disruption and widen opportunities for manufacturers.
“The first response to the global pandemic for many manufacturers was to go digital in their operations as fast as possible,” said Michelle Duerst, research vice president at Gartner. “While going digital is the right path, it is not enough. The trends identified by Gartner can help manufacturing CIOs prepare for similar future disruptions in the long run and mitigate challenges such as lack of customer touchpoints, entering new markets or product lines and financial distress.”
CIOs can use these five strategic business trends to improve top-line growth by providing better experiences.
Trend 1: Digital + Product Experience
Digital + Product experience is the combination of physical products and digital services for unique product offerings to B2B customers and consumers. The pandemic limited customer engagement touchpoints due to the severe lockdown restrictions globally. The combination of digital and product aims to address this challenge of providing an engaging platform that matches consumers’ expectations. This is not just about an additional service, but the ability to have a new digital business model where manufacturers can maintain a connection with the consumer past the sale of the product, by connecting more with the brand as well as with brand experts, other customers and advanced users
Gartner predicts that by 2025, the top 50 consumer goods manufacturers will have invested in a brand app using AI, embedded technology in the product, videos as a digital asset and/or integrated innovation with IT and R&D teams.
Trend 2: Total Experience
Total experience is about how CIOs can use technology and interactions to enhance, empower and embolden both customers and employees to improve their lifetime value. Using this approach, CIOs can identify the right platform that will connect customers, partners and employees. For example, an employee acting as a brand expert or customer service agent to a consumer, answering questions.
Gartner predicts that by 2024, organizations providing a total experience will outperform competitors by 25% in satisfaction metrics for both customer and employee experience.
Trends 3: Ecosystem Partnerships
Global organizations can utilize ecosystem partnerships as an opportunity to grow not only in mature markets, but in developing markets as well. In manufacturing, ecosystem partnerships can enable all types of initiatives, such as earth-friendly packaging, enablement of underdeveloped/underserved communities and emission reduction through remote work capability.
According to Gartner, by 2024, 75% of the top 20 global consumer goods companies will engage in an ecosystem partnership contributing to growth and sustainability goals.
Trend 4: Data Monetization
Data monetization gives manufacturing CIOs the ability to obtain revenue from digitizing their products and services. The rapid digitalization within manufacturing organizations creates large amounts of data. CIOs can share and monetize this data across the ecosystem. Using this approach, CIOs can use information as an asset, and create new services or enter new business models. This can ensure continuous revenue even when the business is disrupted by external factors, such as supply chain challenges or human resource shortage.
Gartner predicts that by the end of 2024, half of global heavy asset manufacturing organizations will have succeeded in monetizing their data.
Trend 5: Equipment as a Service (EaaS)
EaaS is a commercial model where businesses pay for operational assets through recurring operating charges rather than purchasing equipment. In this model, CIOs use embedded Internet of Things (IoT) technologies that leverage common IoT design patterns and industry frameworks to ensure asset effectiveness and find solutions to asset non-performance.
Gartner predicts that by 2023, 20% of industrial equipment manufacturers will support EaaS with remote Industrial IoT capabilities up from a current base of near zero.
It’s interesting to see the different compelling sets of trends for Manufacturing from both Deloitte and Gartner
Becoming a smart factory is a journey and not something that happens overnight, and it’s essential to begin taking steps towards digital transformation if you haven’t already started. Smart Manufacturing is the future of Manufacturing as it provides numerous benefits, including increased productivity and better throughput without sacrificing product quality. The benefits are clear, and the many resources available to provide insight and guidance make it easier and more accessible to start and continue your journey.
