Smart Factory 101 A Data, AI, Cloud and Workforce Revolution in the Making Shanawaz sheriff April 13, 2023

Smart Factory 101 A Data, AI, Cloud and Workforce Revolution in the Making

Smart Factory 101 AI Cloud featured image

Last year, a CPG giant introduced a smart factory across several global manufacturing units. Meant to be a step forward toward its lofty zero-waste goal, the implementation brought both fiscal and environmental benefits. 20% Improvement in OEE  and over 4000 per person-per-hour savings – per factory, per month. That’s no mean task! We’ll get to the story of this CPG giant in just a bit. But before that, let’s understand where the manufacturing world is with Smart Factory today and where this technological intervention is headed.

At its core, a smart factory enables a digital and interconnected production facility that uses state-of-the-art technologies like automation, AI, and the IoT to modernize shop floor operations for productivity and resilience. The smart factory is fast becoming a trusted solution among manufacturing and supply chain experts to achieve these goals. For example, 93% of supply chain and industrial experts want to prioritize the resilience of their manufacturing operations, and 70% agree that a smart factory is the best approach to get there. As with previous years, smart factory solutions are projected to contribute.  

When considering the financial and environmental implications of manufacturing, smart factories are indispensable. They simplify processes, reduce waste, and push for more energy-efficient manufacturing methods. Moreover, smart factory implementations are crucial to long-term sustainability since they encourage resource conservation and lessen environmental impact.

This looks like a dated metric…Can we look at some other one which is for 2025? [GU1]

Manufacturing automation: A story of evolution with smart factory the latest goal post

There have been many important discoveries and innovations along the path of factory automation. This includes the development of assembly lines and PLCs, among other automation solutions.

Productivity, labour costs, and product quality have benefited from the fixed automation and basic control systems used in traditional factory automation. In addition, the increased efficiency of these technologies has made it possible to keep up with rising consumer demands and shorten production timelines.

Traditional automation, however, has its drawbacks. For example, changes in product design, market demands, or production volume are more difficult to accommodate in these rigid legacy solutions. However, the development of technologies like RPA, AI, and the Internet of Things is making up for these constraints, making production and supply chains more agile and bringing manufacturing well and truly into the era of Industry 4.0. 

Smart factory: Making up for legacy factory automation limitations

First things first, a smart factory is a state-of-the-art manufacturing facility that uses Industry 4.0 technologies to build a fully connected and integrated industrial ecosystem.

This cutting-edge method of production has clear goals even before it is implemented:

  • Improved equipment and worker productivity (OEE)
  • Streamlined operations
  • Enhanced production and supply chain responsiveness to customer demands
  • Minimized production waste

And smart factories achieve these bold ambitions by combining the powers of more recent technologies, IoT, AI, and data analytics, among others.

With the help of IoT, equipment, devices, and systems may exchange and monitor data in real time through improved connectivity. Combined with machine learning and advanced analytics, AI allows smart factories to evaluate data, forecast outcomes and failures, control downtimes and maximize output.

Data, of course, plays a critical role in smart factory success because, without it, most outcomes will not be possible. Smart factories use data to predict staffing needs, production quantity and quality, equipment effectiveness and failures, efficiency and adaptability. AI and IoT interventions, too, need data to do their jobs effectively.

Safe to say that data is the lifeblood of a smart factory, fuelling every operation and driving every decision. It’s what puts the “smart” in a smart factory.

Smart factory: Transforming the nature of work with efficiency and effectiveness superpowers

Consider this. In a traditional manufacturing set-up, workers enter the shop floor and spend the first few hours of the day doing basic tasks like switching on the lights and equipment, testing assembly lines, and tinkering with equipment parts that fail without notice. Rinse, repeat!

Do you see how these tasks could be easily automated to minimize the time and effort wasted on inefficiencies that ideally shouldn’t exist at all? Smart factory opens new opportunities for organizations to upskill workers for higher-order tasks. Think improving product quality, keeping the assembly line agile to shifting demands, partnering with AGVs to keep an eye on factory operations without spending time on rounds, and taking predictive and prescriptive action based on triggers from the smart factory solution to both maintain equipment and stay compliant with environmental regulations.

Smart factories don’t just make factories more intelligent; they also make their human workers smarter. They are improving the manufacturing landscape by facilitating data-driven decision-making, increasing productivity, and reducing costs through sensors, embedded software, and robotics. Smart factories pave the path for a more agile and dynamic manufacturing sector by digitizing the whole production process, from product design through distribution.

Industrial Internet of Things (IIoT) systems, artificial intelligence (AI), and large data sets allow for continuous process monitoring and optimization, leading to less downtime, waste, and higher-quality products. Because of this, production facilities boost output while cutting expenses.

Smart factories influence the future of jobs and skill sets. The need for workers with AI knowledge and the ability to work with robotics and other data-driven automation tech is rising as smart manufacturing becomes the norm. While this change benefits those already training in these areas, it also highlights the importance of constantly upskilling and reskilling the workforce to keep up with the changing landscape. That’s an important area for smart factory’s early adopters in the industry to focus on.

The future of factories will be about robust collaboration between humans and machines, making each other more innovative and partnering on regular repetitive tasks and strategic problem-solving. And that future looks very bright from where we are standing. Let’s see why.

Data, AI, and ML: The new-age superpowers of smart factory operations

Data, AI, and ML are at the core of the smart factory transformation. With the help of these innovations, manufacturers streamline their operations, tighten their grip on quality, and apply predictive maintenance measures that add up to greater productivity, reduced costs, and lower environmental impact.

Here’s how these technologies add value to smart factory investments:

1. Quality Control

The manufacturing process relies heavily on quality control to guarantee that the final product will satisfy the needs of its end users. The following are some of the ways in which AI, ML, and Big Data help improve smart factories’ quality control procedures:

  •  Automated Inspection

Computer vision systems powered by artificial intelligence can automatically inspect and analyze product images to spot flaws, deviations, or anomalies. This ensures quality is consistent throughout production runs, cuts down on inspection time, and helps eliminate human error.

  • Real-time monitoring

Many production characteristics, including temperature, pressure, and humidity, can be monitored in real-time through IoT devices and sensors. AI and ML algorithms can examine this data to spot quality problems and initiate fixes, guaranteeing those final products always meet specifications.

  • Predictive QA

ML models can predict quality problems by looking at data from the past and spotting any trends or patterns that could indicate a problem. This promotes a proactive strategy for quality control by enabling manufacturers to foresee and respond to issues before they become problematic.

2. Process Optimization

With the help of AI, ML, and Big Data, production processes may be optimized, leading to greater efficiency with less cost. Examples of how these technologies can be used to improve operations are as follows:

  • Data-driven decision making

Big Data analytics paves the way for factories to sift through mountains of information gathered during the course of manufacturing. Decisions can be informed by the findings of this study, leading to more efficient and successful operations.

  • Adaptive production

To achieve adaptive production, AI and machine learning algorithms can analyse data from IoT devices and sensors in real time. Changes in demand or operational conditions may need to readjust machine settings, rearrange production schedules, or reallocate resources.

  • Energy saving

By evaluating data from sensors and other sources, AI and ML may improve energy use in smart factories by pinpointing inefficiencies and providing recommendations for reducing energy usage. This helps keep production costs down and makes for a greener factory overall.

3. Predictive Maintenance

Predictive maintenance is a crucial part of smart manufacturing operations because it helps manufacturers spot potential problems with equipment and fix them before they result in downtime or complete failure. Predictive maintenance solutions rely heavily on AI, ML, and Big Data in the following ways:

  •  Data collection and analysis

IoT gadgets and sensors can keep tabs on machinery around the clock, tracking metrics like temperature, vibration, and wear. Analyzing this data with AI and ML algorithms can identify patterns and trends that may signal problems.

  • Failure rate prediction

Predicting the likelihood of equipment failure using historical data and real-time information, ML models provide manufacturers with early warnings and enable them to schedule maintenance activities proactively.

  • Automated Maintenance Scheduling

Maintenance schedules that reduce downtime and increase equipment life can be recommended by AI and ML algorithms by examining equipment data and identifying the relationship between various factors.

Smart factory implementations: Roadblocks along the way

Now that we know how smart factory implementations benefit manufacturers, let’s also understand that building one is more challenging than it might sound.

This transformation requires businesses to adjust to novel technological, operational, and human resource demands. Among the most significant considerations are:

1. Tech integration

IoT, AI, ML, and Big Data are just some technologies that must work together harmoniously to create a smart factory. Unfortunately, incorporating new innovations into pre-existing infrastructures and procedures is often difficult, time-consuming, and expensive. To overcome this obstacle, companies need a plan for adopting new technologies, a list of priority areas that need transformation, and investments in solutions that can scale as their demands do.

2. Data security

As smart factories become increasingly reliant on data-driven processes, they become more vulnerable to cyber threats and data breaches, which can cause serious harm to their finances and reputations. Encryption, permissions, and routine audits are just some of the data security measures companies should take to address this threat.

3. Change management

Transitioning to a smart factory requires extensive alterations to established routines, procedures, and staff structures. Organisations must properly manage this change to ensure a smooth and successful rollout. This can be accomplished through staff participation in decision-making, open dialogue, and assistance with the adoption of new technology and methods. In addition, investing in an experienced technology stack partner can help ease the transition from a traditional plant to a smart factory.

4. Skill gap

The transition to smart factory calls for new skill sets for workers, including AI, ML, and data analytics knowledge. Businesses must engage in upskilling and reskilling programs to ensure their employees have the skillsets necessary to fill this gap.

The only antidote to these challenges is partnering with a technology provider who understands these challenges and has a successful track record of implementing smart factory solutions.

Smart factory done right: A success story

Let’s finally return to the story of the global British CPG major that transitioned to smart factories with Acuvate.

The company started its smart factory initiative to achieve sustainability by digitizing its supply chain and manufacturing processes and scaling towards its zero-waste goal. They also needed to optimize the equipment throughput utilization, reduce production timelines, and improve the Overall Equipment Effectiveness (OEE) metric.

Acuvate Smart Factory solution at work

The CPG major worked with Acuvate to implement end-to-end data solutions with Power BI-enabled reporting tools for its smart factory. This allowed every unit’s and overall organisation’s stakeholders to have real-time insights into workforce productivity and utilisation, OEE, and output reliability.

In addition, ML models were deployed to calculate material wastage. Appropriate remediation measures ensured that this wastage was minimised rapidly and across plants.

And the results spoke for themselves

The company made giant strides in its zero-waste and sustainability agenda while aligning with regulatory and environment-related compliance requirements.

Other improvements included:

  • 8% output reliability gains
  • 20% improved OEE metrics
  • 4000+ hrs/month with automated reporting and prompt action
  • Significant hard-dollar savings in OPEX due to robust and reliable processes

Acuvate Smart Factory solution: Ready for Industry 4.0 and beyond

Acuvate’s Smart Factory solution aggregates data from different sources and enables real-time monitoring and reporting to improve OEE metrics and suggest corrective measures for predicted service outages.

The root cause analytics and failure prediction features allow for corrective actions that are 50-70% more accurate than other comparable solutions. The data-sharing mechanisms include centralized governance for self-service machine learning and analytics.

Acuvate Smart Factory also improves OEE metrics by 75%, saving approximately $100M per implementation due to waste reduction, helping companies attain their zero-waste and sustainability goals while staying frugal in their manufacturing processes.

In addition, Acuvate’s decades-old experience with manufacturing units across CPG and other sectors, with a portfolio of brands like Unilever and AB InBev, reinforces domain expertise in implementing smart factory solutions.

The transition and deployment of Acuvate’s smart factory solution take as little as 12-16 weeks due to reusable solutions and components. This timeline goes beyond the plant and includes cubicle-level dashboards for real-time reporting and actionable insights across the board.

Ready to embrace the future of work and manufacturing?

Smart factory solutions are critical for businesses that aspire to excel in the competitive Industry 4.0 landscape.

By leveraging advanced technologies such as AI, ML, and Big Data, Acuvate’s solutions enhance efficiency, quality control, and predictive maintenance, ensuring that your manufacturing and supply chain workstreams stay ahead of the competition.

Don’t let your competitors outpace you in the race to Industry 4.0. Take the first step towards transforming your manufacturing operations. Request a demo!