Industrial Automation Magazine India

The essence of smart manufacturing lies in pre-emptive actions

Published on : Sunday 05-03-2023

Niju Vijayan, Partner, Avanteum Advisors.

Which are the three new technologies which would be interesting for factories to acquire and adopt? Why would it be attractive?
Digital Transformation is expected to bring about convergence like never before and different technologies will have interplay to accrue maximum benefit to the user community. While each of the technologies will find an immediate use case, its new applications are developed in a short period of time, thereby increasing their attractiveness. Attractiveness and feasibility lead to further development and adoption by adjacent industries too. There are numerous technologies that will positively impact manufacturing. Selection of 3 key technologies may not do justice to others but can throw light on the long-term impact.
 
Artificial Intelligence/Machine Learning (AI/ ML): The manufacturing sector has traditionally been challenged by uncertainties arising out of supply chain, quality, reliability, accuracy, lack of compliance, etc. Manufacturing practitioners have relied on experience, best practices, benchmarks, empirical data, machine builders, and event occurrence among others as feed into their decision making; none of which individually provide a high level of confidence in achieving desired results.
 
AI is a computer based program that can teach itself, train, understand, analyse, act or provide advice, based on the relevant data fed to it. ML can train the devices and processes to act in ways that can greatly minimise inefficiencies. AI acts as a force multiplier on account of its prowess in accurately planning demand, energy needs, inventory, logistics, production, quality, and associated costs. Businesses acquire the ability for risk taking as AI helps in minimising uncertainties and offer collaborative opportunities. In a highly competitive business world, manufacturing needn’t be constrained by an inflexible production structure but can become agile and responsive to customer needs, through adoption of AI/ML. The essence of smart manufacturing lies in pre-emptive actions to enhance manufacturing competitiveness.
 
Additive Manufacturing: Also known as 3D printing, is gaining currency among manufacturers on account of strong advantages offered by the technology. Whether it is quicker prototypes, design iterations, optimisation of expensive material inputs, quicker production – the manufacturing industry couldn’t have asked for anything more path-breaking.

Manufacturers are enamoured by the fact that distributed manufacturing can become a reality resulting in lower cost of production and faster time to market. Key investment areas like tooling will be vastly curtailed while mass customisation will enhance customer stickiness.
 
Augmented Reality/Virtual Reality: in an era where productivity is a key metric for customer value enhancement, AR/VR offers tremendous diagnostics power in the service support. Users focused on low inventory and JIT are greatly served by efficient handling of service issues, possible through AR/VR systems. These systems provide physical views, possible malfunction information, accurate locations – all these even from remote locations. This technology is a boon for product and service training. 
 
There are two work areas – bringing raw materials into the factory, and movement of work-in-progress inside the factory – where there is much scope for automation. Which technologies are relevant in this area for different types of factories?
Both areas are equally important as without the requisite quality or quantity of raw materials, manufacturers can’t achieve their output targets. Since RM specifications are set by the users in advance, it falls in the ambit of the RM suppliers to meet the specifications. RM supplied as processed inputs calls for a high level of automation and is determined by the user industry. For example, steel requirements for automotive need to meet stringent quality norms and hence steel makers consider automation as integral to their production.
 
Core manufacturing process is witnessing a significant increase in automation as new technologies like robots and AI either add new layers or automate existing processes. All essential demands in manufacturing – quality, efficiency, costs, sustainability, safety, data driven decisions are sought to be addressed by higher automation.
 
Inspection and quality is a very important topic. It is no longer just good enough to execute these functions rigorously, now it is a necessity to show off that it is being done. In other words customers might wish to view that inspection and quality check are being executed.
Quality being a vital parameter is no longer confined to the producer’s database but today travels across the value chain to provide data and inspire confidence across the elements in the chain. Quality practices followed are getting increasingly digitised and available through traceability solutions, designed as part of the system. It is in the interest of the producer and user to have access to process quality data in order to correct anomalies and minimise defects. Regulatory requirements in pharmaceuticals and food demand digitised records pertaining to quality. Transparency in practices acts as a confidence boosting measure in long term relationships. Large number of recalls in the automotive industry in recent years sticks out as sore examples of process compromises being made.
 
Robots are going to be a presence in the factory. But importantly, which functions are going to get robotised? For instance, would cleaning the shopfloor be an application to use a mobile robot?
Robots have become popular on account of 3 reasons – Substitution of human workforce engaged in repetitive/nonvalue added activities, jobs which are considered unsafe and where human precision falls short.
 
Considering the above, the areas where robots are expected to dominate are material handling, quality inspection and process functions. Load movement between points and within the manufacturing line comprises heavy loads and delicate components, ruling out human intervention. If they are driven by precision or process safety in industries like electronics, pharma, food while hazardous applications involving workforce safety demand robotic usage.
 
As industrial manufacturing moves to non-urban locations, comparatively lower social infrastructure poses a challenge to availability of quality manpower. This is sought to be overcome through usage of robots that can minimise this industry wide concern. Moreover, the premium on time leads many manufacturers to opt for robots that do not require expensive hiring and training programs.
 
It is interesting to note that countries like India where manufacturers are automating through robots are seeking to augment human skills with robots, thus providing a boost to Collaborative Robots (Cobots). Cobots provide the best of both attributes – human skills and precision handling – in the desired proportion. Cobots are emerging as a preferred mode of automation across manufacturing as they align well with the need to reskill existing workforce while achieving automation.
 
Robotic Process Automation – RPA is an exciting productivity tool. How many factories use this? Why don't others use it?
RPA is expected to gain acceptability with increased adoption of AI/ML. Generation of large volumes of data through IoT enabled devices/systems calls for interfaces that need to “talk” to computers and receive continuous instructions. Such manual tasks are not only repetitive but also prone to generating errors. RPA ensures that such repetitive tasks are managed with an extremely high level of accuracy and skill manpower can be productively deployed.
 
RPA today is prominent in the service sector but has tremendous potential in manufacturing. Areas like logistics with a multitude of destinations, carriers, transportation modes, compliances and protection are hugely benefited. Similarly supply chain complexity can be efficiently managed through RPA where a plethora of vendors, variants, geographies, fitment, cost and compatibility challenge the manufacturers regularly. Inventory management is all about cost and RPA can act as a saviour through data collection, reporting, report generation and sharing with stakeholders. Routine activities like invoice generation have fallen into the realm of RPA.
 
While RPA is picking up pace across the manufacturing industry, the speed of digital transformation taking place within each organisation is a key determinant in RPA adoption.
 
Niju Vijayan is a Partner with Avanteum Advisors. He has more than 2 decades of experience in helping clients formulate strategies and actions in rapidly disrupting markets and industries across multiple geographies. His core focus is on the manufacturing sector helping clients strategise for growth and sustainable success. He has also been associated with industry bodies, actively assisting in formulation of policies and action plans for industry development.
 
(The views expressed in interviews are personal, not necessarily of the organisations represented)