As digital transformation has become a necessity rather than a choice across industries in recent years, the question of how traditionally labor- and machinery-intensive industries can leverage the latest technology has been raised as well.
Where does manufacturing stand when it comes to the new wave of digitalization? With smart factories expected to grow at a CAGR of 9.6 % to be valued at approximately $244.8 billion by 2024,  the answer—rapid strides in technological adoption—seems clear. However, although we’ve seen an increased adoption of manufacturing technologies such as IoT and sensors across assembly lines, this growth has not been uniform, or consistent. With the increased expectation of agile supply chains, supply on demand, and diversified methods of production as part of the emerging Manufacturing 5.0, how can manufacturers sustainably leverage evolving technology to scale and optimize operations?
Here are the top 7 areas businesses need to look out for:
With the demand for high-quality products, reduced turnaround times and costs, and personalized products as key differentiators for manufacturing, quality control has arguably become the greatest contributor to, and determinant of, high performance and ROI. This is the case not just for industries including transportation, medicine, O&G, construction, and aeronautics, where comprehensive and rigorous QC processes are critical, but also across assembly line types, sizes, and complexities.
assembly line types, sizes, and complexities. We’re also seeing quality control processes in assembly lines beginning to leverage the sheer wealth of data generated by factories every day. For instance, an optical products company created an advanced Asset Defect Recognition technology to improve the accuracy and competitiveness of testing mechanisms at its factories, using X-rays to scan parts’ structures and soundness.  In the same vein, a telecommunications giant deployed video analytics-based applications to monitor assembly lines in its factory, with real-time, automated alerts sent to operators with any challenges or faults arising. 
In fact, tasks such as quality checks, inspections, completeness checks, and audits are perhaps most suited to automation: aiding humans, who can be prone to error, fatigue, and/or boredom, as well as playing the role of first respondents in the case of risks or safety hazards. With AI-driven computer vision technology, the data generated from quality checks can be rapidly and seamlessly analyzed to drive automated defect identification. Factors ranging from inconsistent textures, color, and alignment to minute structural defects, dimensions, and measurements can be identified and addressed automatically to maintain product quality. Simultaneously, ML algorithms can help generate instant notifications for QC and engineering specialists, as well as teams across the globe, helping maintain high output quality, optimal assembly line performance, and regulatory compliance in critical industries including FMCG and Pharma.
AI&ML can help drive root-cause analyses, helping managers, engineers, and other stakeholders optimize manufacturing processes with greater speeds and efficiency than ever before. In addition to providing real-time alerts during instances of defect detection, allowing teams to quickly respond to incidents, data-driven applications can proactively predict defects to prevent quality issues. With data from machines, sensors, manufacturing execution systems (MESs), on-site monitoring mechanisms and more, a comprehensive picture of assembly line health can be created, with historical patterns analyzed to accurately predict the possibilities of defects, malfunctions, and quality non-conformance.
Further, intelligent applications are being increasingly used to reduce scrap volume in manufacturing. With real-time and predictive AI, first-pass yields can be enhanced, scrap rates can be kept to a minimum through production monitoring, and alerts can be sent out in case thresholds are about to be surpassed or quality issues are foreseen, helping not only reduce scrap but also maximize material use and ensure compliance.
We’ve seen a distinct move away from reactive to predictive maintenance strategies in assembly lines in recent years, with advanced technology being leveraged to analyze parts’ and machinery’s lifecycle and address faults before they can even materialize. According to a report, 29.4% of respondents from the packaging and processing industry were considering implementing predictive maintenance, with 21.6% already piloting it and approximately 23.5% having already deployed it.  Given the estimated $50 billion loss  resulting from unplanned downtime in manufacturing each year, predictive maintenance is set to become a pre-requisite for any assembly line; not only can it reduce losses but also eliminate the need for annual maintenance closures and scheduled downtimes entirely.
According to research, an automotive manufacturer faces losses of approximately $22,000 every minute due to unexpected downtime, which translates to a figure of $1.3 million per month. However, predictive maintenance solutions have been found to reduce unplanned downtime by 60% and financial impacts by 36%.  With the various components of predictive maintenance solutions—infrared analysis for temperature monitoring, vibration analysis for equipment malfunction/imbalance detection, and audio analytics to analyze machinery sounds and indicate deviations, for example—AI can help significantly improve uptime for assembly lines. This is corroborated by recent research, which finds that predictive maintenance can reduce costs by 12%, extend asset lifetime by 20% and cut safety, quality, and health risks by approximately 14% across the board. 
With reduced turnaround times and innovation being pre-requisites to product performance, generative design has, for a long time, played a decisive role in improving speed to market, assembly line efficiency, and productivity. However, newer technologies including AI & ML have given generative design a boost, shortening the time taken for testing, prototyping, and iterations from days to mere minutes. Drawing from varied sources including market research, real-time factory data, and even customer preferences, AI can drive new types of design across products. For instance, an American vehicle manufacturer developed ML algorithms for generative design that helped fine-tune the strength, weight, and components of their seatbelts, streamlining their 3D printing process.  Further, factors ranging from budget and raw material availability to production processes and labor availability can be quickly accounted for using advanced AI to indicate the most suitable courses of action. This has far-reaching implications for assembly lines, where rapid and flexible changes will be required to meet the mandate of constant innovation.
Digital twin technology has seen widespread industry adoption in recent years – according to research, by 2021, 50% of large industrial companies will be employing digital twins to see a 10% increase in effectiveness.  With digital twins integrating sensor, IoT, and other kinds of data to create virtual versions of highly complex plants, AI & ML can drive knowledge of granular factors such as raw material availability, power levels, and assembly line movements.
Each component of an assembly line can therefore be analyzed in detail to enable decision-making on process improvements, machinery replacements, supply chain streamlining, labor optimization, and so on. Further, operation modifications on other parts of the assembly line, as well as means by which component assembly and human labor can be optimized, can be examined and implemented without halting or disrupting operations.
For example, a major aircraft manufacturer deployed digital twin technology to manage 13,000 aircraft engines, with sensors providing valuable information on performance, maintenance needs, fuel requirements, and so on,  while ensuring minimal disruptions to workflows and uptime. With the digital twin market expected to grow from $3.8 billion in 2019 to $35.8 billion by 2025, across industries including healthcare, energy, and retail, 11] its adoption in manufacturing and assembly lines is set to strengthen, offering reduced go-to-market times, intelligent monitoring capabilities, and a rich stream of data.
The use of robots for warehousing and a variety of mechanical processes in the manufacturing space has become increasingly prominent over the last few years, with cutting-edge technology helping increase productivity, creating “dark factories” that can run 24/7, and bringing a greater level of precision to assembly lines. With robots being a potent source of real-time information on processes ranging from pallet loading and unloading to unit production and part repair, advanced algorithms can tap into this data to not only feed into digital twin technology but also streamline workflows, helping robots communicate effectively with each other as well as collaborate with humans.
With such technology becoming increasingly sophisticated in recent years, and the costs of robots reducing progressively, the opportunity to deploy more robots in assembly lines is ripe. For instance, a major Chinese communications company has established a network of AI-driven robots for manufacturing processes across the globe. With technologies including IoT, AI, and deep learning employed as part of the interconnected system, robots across factories can now learn from each other to enable enhanced manufacturing.  Alongside reducing assembly cycle times and downtimes, such technology is also poised to enable better robot-human collaboration, for instance, alerting humans to emergencies, or handing over decision-making to them for certain critical tasks.
In this regard, the use of collaborative robots in assisting humans as well as undertaking higher-order tasks can be highlighted. The cobot market is expected to reach a valuation of $12 billion by 2030,  and with the use of AI & ML, cobots will be able to draw on historical data to respond to complex situations, independently identify problems and address them, as well as assist humans at assembly lines through quality control, advanced perception, sorting, welding, and transporting objects, among others.
With assembly cycle time being greatly impacted by factors such as machinery downtime, raw material unavailability, labor unpredictability, and so on, potential and real-time bottlenecks become immediate concerns for manufacturers. With machine learning speeding up intelligent learning, real-time reporting, and considering factors ranging from performance to quality across operational areas and shifts, ways of improving assembly line efficiency can be easily identified. This becomes especially important in the move towards mass customization in production, where the traditional challenges of unexpected breakdowns are met by the more recent challenges of raw material needs and personalization-driven process modifications. The latter bring challenges of their own: the need to effectively source raw material and equipment, anticipate customer needs, and transition to an entirely new way of manufacturing.
In the quest to achieve more modular production processes, we’re seeing manufacturers lean towards a greater use of mixed-model assembly lines, which enable variations to products without increasing costs. According to research, “The mixed-model assembly line has several advantages, such as quick response to customer demands, fewer expenses to produce the new models of a given base model by using the same facilities, and more production flexibility. In other words, the mixed-model assembly line is an essential factor for today’s manufacturers to overcome the recent challenges associated with operating in the Industry 4.0 environment, particularly regarding the diversity of products, as well as the rapid response to ever-changing customer expectations and demands.”  AI here can help, through digital twin technology and data-driven research, implement such changes to assembly lines, ensuring enough inventory levels as well as providing specialists and engineers insights into demand trends that are likely to emerge in the short- and long-term.
In the transition from Manufacturing 4.0 to 5.0, where technological advancements as well as the interactions between technology and humans are poised to take the center stage, the role of AI & ML in ushering in change is significant. From predicting downtime and improving robot-human collaboration to reinventing manufacturing processes for personalization, the value of data in driving flexibility and agility for assembly lines is immense. From traditional manual processes to streamlined automation, from time-consuming processes to instant iterations, manufacturers can now tap into the rapid pace of the future with AI.
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