Manufacturing process optimization is ever-critical for today’s industrials transitioning into the digital era. These are unquestionably times of adversity for manufacturers. Global-economic volatility, supply chain disruptions, vital sustainability mandates, technical talent shortages, evolving customer demands, and other pressures are testing industrial resilience and agility.

But with adversity comes great opportunity.

In the eye of the storm, manufacturing process optimization plays a more crucial role than ever. Done right, it maintains competitiveness and profitability while perfecting increasingly sustainable production. More than this, strategic manufacturing process optimization can accelerate a plant’s digital transformation roadmap holistically, integrating teams as connected workers. This feature makes it even more relevant against the background of the rapid transition towards Industry 4.0, during which new skills such as algorithmic business thinking highlight a mindset shift that leading factories exemplify.

For manufacturers, digitalization means the speedy integration of data-to-value solutions that seamlessly embed themselves within existing Manufacturing Excellence regimes and the IT/OT landscape of their plants, extending to the level of factory fleets. Manufacturing process optimization technologies and techniques should be evaluated on the basis of their impact and contribution to bringing entire operations into the digital era.

Why is aligning strategic digital vision with 4IR manufacturing process optimization technologies crucial in the current era?

Industrial Adversity for Manufacturers in 2023

The industry is evolving in fundamental ways and industrials must evolve their practices to adapt. An important article on this subject explores the interplay between the current structure of an industry, the conduct of competitors, and current (versus potential) performance. It reveals the SCP model as especially relevant to manufacturing’s current great industrial inflection point.

How so?

McKinsey identifies a rapid surge in digital adoption among manufacturers today in industries such as automotive and electronics. Reports suggest that, of the current Lighthouse manufacturers, 60% have adopted AI and are gaining a competitive edge through faster and more sustainable change.

In this light, the SCP model helps make sense of why the strategic integration of digital transformation technologies is a matter of great urgency in 2023. Manufacturers who invest wisely in new-era innovation will ensure they are not disrupted out of relevance by their faster moving competitors — in a sector that needs to transition to digital fast and with deliberate execution at every step.

Let’s get specific. For effective digital era manufacturing process optimization that drives value, manufacturers need to adopt a more connected, automated, and data-driven approach. This is actualized through the integration of Industry 4.0 technologies, including artificial intelligence (AI), the Internet of Things (IoT), big data, and cloud computing. 

By leveraging these technologies, manufacturers can enhance their ability to respond quickly and effectively to production inefficiencies and ever-changing market conditions. 

Conversely, a reactive production paradigm forestalls momentum.

Reactive Process Optimization in Manufacturing

Without Industry 4.0 data-to-value initiatives successfully embedded, manufacturing process optimization techniques put manufacturers on the back foot. A backlog of missed KPIs with either known or unknown failures piles up. This traps siloed quality and engineering teams in firefighting mode. 

Such reactive production loop event chains are common in Industry 3.0 factories. 

This approach to root cause analysis poses many limitations. Firstly, it is time-consuming and does not sufficiently reduce the number of defective units produced. It also requires expert guidance built up over years of on-floor experience. And while it may be ultimately effective for a discrete production issue, it often fails to solve the problem in its entirety. 

The growing mass and complexity of industrial data available to manufacturers today calls for a new relationship between humans and machines. In-house experts undisputedly provide plenty of essential wisdom. However, even the smartest specialist can only disentangle the hardest production problems from the complexity of their interdependencies up to a point. As a result, big data exposes knowledge surrounding production process anomalies as incomplete. 

Without proactive data-driven solutions to manufacturing process optimization, recurring problems continue to grow in adverse conditions. This results in higher opportunity costs and a negatively impacted bottom line. 

In the current climate, a reactive paradigm exerts undue cost, execution, and human resource pressures that demand extraordinary agility and resilience, as well as faster and more intelligent decision-making. They underpin the urgency for value-driven digital transformation. 

Let’s dig a little deeper into the stressors alluded to above.

Challenges Manufacturers Face in Times of Adversity with a Reactive Paradigm


Supply chain disruptions and global economic volatility continue to impact the manufacturing sector. Common issues include a global shortage of materials, high energy costs, and distribution delays. At the level of “make” (in the buy-make-deliver value chain), these issues have exposed vulnerabilities in so-called ‘real-time’ manufacturing to improve efficiencies at the next level. In ‘real-time’ production loops, the need for data-driven process optimization improvements is clear. Namely, current plant data delivers potentially useful information in real time about suboptimal production runs, but does so after the KPI has fallen over. Meanwhile, no corrective solution is delivered.


Additionally, organizations are required to comply with various sustainability mandates. Many regulations have been implemented globally to help navigate a greener future for manufacturing. As such, failure to achieve certain metrics (e.g., regarding carbon emissions, energy usage, etc.) is often met with penalties. 

Critically, stakeholders, including regulators, investors, customers, suppliers, and employees, are beginning to associate sustainability with long-term value. For corporations, there is a growing shift from profit maximization to a new growth approach that gives more weight to the betterment of society and the planet. Moreover, consumers and corporate purchasers are placing greater importance on organizations’ carbon footprints when making purchase and investment decisions, according to McKinsey

In a reactive paradigm, manufacturers are too busy trying to troubleshoot recurring production anomalies to focus on holistic efficiencies — they need proactive data-to-value insights that optimize multiple metrics and drive sustainable manufacturing.


At the level of process control technology, the increased mass and complexity of industrial data can hold back sustainability and industrial process optimization initiatives. For effective decision-making, having timely, relevant data is crucial. However, many manufacturers struggle to get the relevant data from the hundreds of parameters impacting their processes into one place such that it is valuable and ready for use. Additionally, they rarely have the data science expertise in-house to convert their data into actionable value levers.

Read on to dive deeper into data-driven manufacturing process optimization, and learn how digital step change technologies can help plants increase their resilience and maintain a competitive edge.

What is Process Optimization in Manufacturing Today?

For the current era, we can usefully define manufacturing process optimization like this: 

  1. Digitally connected plant teams learning and implementing data-driven strategies that impact their manufacturing processes to minimize cost and maximize production toward peak operational efficiency.
  2. Using data-to-value technologies that integrate seamlessly with their legacy systems and progressively automate an end-to-end, continuous improvement, production loop — freeing manufacturers from a reactive troubleshooting paradigm so they can layer in further innovations toward the smart factory.

In factories, process optimization focus areas generally include production machinery, control loops, quality monitoring, maintenance scheduling, and other variables. Through the optimization of such processes, manufacturers aim to achieve:

  • the greatest amount of output; 
  • at the highest possible quality; 
  • with the greatest energy efficiency;
  • and at the lowest possible input cost.

This has been a clear objective for manufacturers throughout the previous industrial revolutions, including Industry 2.0 (electricity and assembly lines) and Industry 3.0 (computers). Over the past decade, Industry 4.0 has brought about many new technologies, including the Internet of Things (IoT), cloud computing and analytics, and AI and ML. And yet, despite ever-growing capabilities, many manufacturers globally find themselves stuck in Industry 3.0—and some in Industry 2.0. 

For this reason, it is important for manufacturers to embrace the digital era so that inefficiencies can be identified ahead of real time.

Process Optimization in the Digital Era of Manufacturing

What does the optimization of manufacturing processes look like in Industry 4.0?


It begins with data. Data is the fuel that powers smart factories and empowers the personnel who run them. To gain a unified view of the production process and quality outcomes in data, an IIoT platform is used to collect raw feeds from the Edge from a variety of data sources. This includes everything from sensors, programmable logic controllers (PLCs), supervisory control and data acquisitions (SCADAs), and historians to quality data recorded in the quality management system (QMS), manufacturing execution system (MES), spreadsheets, and other files. 

Before feeding the data into the AI model, the IIoT platform works to render the optimization-critical data ready for use. For predictions and prescriptions to be accurate, it is essential for AI models to be trained on contextualized and complete datasets. Using the primed data, the IIoT platform is able to create a dynamically unified view of the global plant state (based on a period of historical production and the associated quality outcomes as against the current plant state).

This unified view ensures manufacturers log, trace, and visualize all relevant production process parameters in real-time and in one easily accessible location — subsequently empowering them to see where they are as well as define the optimal target.


From here, the AI model is deployed. Deep learning algorithms are the perfect tool to leverage massive amounts of process and quality data from the unified view to generate insights into the material flow of the relevant industrial process.

Applied as prescriptive analytics, the models work to optimize the production process preemptively and holistically. This is achieved by the Expert Execution System (EES), which continually correlates historical production data and quality outcomes with the latest plant state and provides actionable prescriptions created to take the production line to its most optimal operating region.


Through the above process, machine learning workflows are able to solve current generation data-readiness and production process optimization issues while future-proofing operations. By easing cost pressures and driving up revenue via data-driven production efficiencies (and with increasingly data-mature plant personnel), the C-suite is free to develop strategies with innovation managers. Together, they can combat the broader external challenges experienced by many manufacturers today.

How Does AI-driven Manufacturing Process Optimization Solve These Challenges?

Now that we understand how manufacturing process optimization 4.0 works, we can dive deeper into how it solves the challenges mentioned above.


AI-driven solutions are ideally provided as a service and digitally align manufacturing subject matter experts (SMEs) with data scientists, operators, and other stakeholders. 

This collaboration helps upskill key factory personnel so that they can make practical use of their production data. An increased understanding of algorithmic business thinking will then encourage plants to update their standard operating procedures to facilitate continuous improvement — for targeted metrics and holistically.


In addition to upskilling, manufacturing process optimization 4.0 helps plants effectively use and store production data. As mentioned earlier, an AI-ready IIoT solution collects industrial data from multiple sources and transforms it into data that can yield actionable insights that drive value. With a dynamic, unified view of the plant and accurate prescriptions, manufacturers can proceed to optimize production processes against their critical key performance indicators (KPIs). 

Among these KPIs are sustainability metrics, such as carbon emissions, energy consumption, and water consumption. By proactively identifying patterns and anomalies present in the production process, AI models can target optimization critical parameters (and supply missing ones from the edge) that ensure continual improvements in the targeted metrics. Simultaneously, machine learning workflows can be embedded that improve overall equipment effectiveness (OEE). Combined, this moves the plant to peak operational efficiency. Plant managers are able to meet their sustainability goals, avoid costly penalties, and support the global effort toward net zero. 

Broadly speaking, value-driven digital transformation technologies ensure that organizations are proactive and resilient amidst ongoing supply chain disruptions and other stressors. Dynamic AI solutions increase the flexibility and agility of plant operations and reduce the impact of external shocks.

Maximizing Manufacturing Process Performance with AI — Success Stories

To fully appreciate the capabilities of manufacturing process optimization 4.0, let’s look at some real-world successes brought about through deep learning applied as prescriptive analytics.

Recently, a globally-renowned manufacturer of light-alloy wheels deployed our AI as a Service (AIaaS) solution to reduce its scrap rate and attain Manufacturing Excellence 4.0 through effective digital transformation. This positively impacted production in a number of ways. Firstly, digitally automating the logging of data meant less paperwork and more consistent and centralized production record keeping, which is ideal for plant managers. It also helped engineers by gleaning the most intractable production problems in real time. Each of these led to more efficient production process optimization. 

Another use case is Condals Group—a foundry that produces over 43,000 tons of high-quality iron castings each year for the automotive industry. Condals implemented our AIaaS solution, which tracked data from over 700 process parameters and loaded it into the Expert Execution System. Harnessing deep learning applied as prescriptive analytics, the AI-driven solution provided prescriptions that Condal’s plant personnel actioned to proactively ensure an optimal state of production. The result—a 45% scrap reduction and measurable improvements in iron cast quality.

Achieve Operational Excellence with Data-driven Manufacturing Optimization

In sum, Industry 4.0 manufacturing process optimization plays a vital role in achieving manufacturing and operational excellence, particularly in times of adversity. 

In partnership, advanced technologies, including IIoT, AI, and ML, greatly improve manufacturers’ flexibility and responsiveness to rapidly changing market conditions and external pressures — by normalizing measurable, next-level continual improvements at a time when smart factory step change is a matter of survival.