SMEs can leverage AI/ML in multiple ways despite their limited resources
Published on : Tuesday 08-02-2022
Namrita Mahindro, Chief Digital Officer, Aditya Birla Group.
Which forthcoming advances in technology will impact industrial automation?
The shift from operational excellence to operational resilience has become a key priority for businesses as business risks continue to multiply be it the pandemic, cybersecurity threats, additional regulatory changes or enhanced safety requirements in the wake of Covid over the past two years.
Industry 4.0 technologies like IIoT, AI/ML, Advanced Analytics, AR/VR, Robots, Additive Manufacturing, Blockchain, Cloud, and 5G are at the heart of the current industrial automation.
The vision is to shift from a hybrid automated plant to a self-optimising, connected, autonomous plant which can change the competitiveness of companies and nations by increasing productivity and fostering greater growth. This will be enabled by gathering and analysing data across connected machines with an integrated production line, enabling faster, more flexible, and more efficient processes to produce higher-quality goods at reduced costs and lower error rates.
With this high degree of automation and digitalisation another aspect of technology that has gained prominence is the convergence of IT/OT. This includes the data flow and connectivity within the OT environment as well as across IT/OT. With data flow of men, machines and materials, cybersecurity of these environments and across the ecosystem is a key consideration and priority.
Plant workers on site working along with remotely placed colleagues has become the norm even in manufacturing sites. Be it for remote maintenance, training, management, quality inspections by customers, safety and for many other use cases a combination of Robotics, AR/VR, Computer Vision and AI is being used extensively.
How are manufacturing industries leveraging AI/ML? How do automation controllers provide the necessary platform?
AI/ML is re-defining/disrupting the human-to-machine and machine-to-machine interaction across the value chain. Be it use of AI/ML for reducing the new product development time by simulating and experimenting with different chemistries or product designs in virtual labs and collaborative marketplaces and open source platforms or predicting the procurement reliability across the supplier ecosystem and variance in raw material/commodity pricing to better hedge organisational risk.
At the manufacturing plant, it is helping in delivering greater quality consistency and productivity improvement through digital twins; reducing equipment failure and thereby enhancing maintenance reliability and saving variable costs especially for energy. It is also enabling near real time decision making with edge computing and augmenting plant workers existing knowledge and skills so that they can perform higher order, more complex decision making rather than just repetitive tasks
Currently, there is a hybrid environment with on the one hand different OEMs having different platforms supporting their specific equipment; and on the other hand horizontal platforms where the data from different equipment can be collated from PLCs, DCSs, etc., and fed into the IIoT platforms for greater insights and intelligence for better and quicker decision making
AI and ML need massive amounts of data to be gathered by IoT devices. What strategies do industry plan to collaborate in data collection?
In the manufacturing sector historically a lot of data is being captured at plants. A lot of this has now been digitised. More importantly all these disparate systems are now being connected to speak with each other.
The self-optimising connected plant will have digital twins at the heart of the operations to enhance productivity and a shift from scheduled to predictive maintenance.
The data centralisation and central asset repository will allow users to have access to the data and assets they require ‘on demand’ across plants to ensure that there is a single view of any form of development and reuse to existing assets and data is maximised. This can subsequently be opened to the ecosystem of partners (customers and suppliers). There is also an opportunity for creating industry level platforms with private and public sector partnerships which can be a win-win for all stakeholders
From an IT/OT security perspective the data flow will be managed within each of the environments and flood gates for two-way data exchange will be enabled only in specific use cases
How can AI and ML help companies create predictive models, analyse operations, make accurate forecasts and automate supply chains?
Integrated business planning becomes key to better decision making, revenue and margin maximisation and cost optimisation. The demand forecasting models can be very useful for sales and marketing teams to get insights and intelligence that may have been difficult to decipher otherwise. Scenario planning has become key for greater agility and flexibility given the dynamic times we are living in with rapid and significant shifts in markets. From a production planning perspective, AI/ML models can help decide which plant to produce a product based on variable manufacturing costs, inventory availability and what mode of logistics will enable the best margin for the business keeping all the constraints in mind. AI/ML models are also very useful in enabling logistics and inventory cost optimisation.
The Control Tower provides a single view of all the data flow from different sources generating insights and intelligence which will be way harder to discern across disparate systems.
Be it supplier performance, risk management or spend analytics, in each of these cases AI/ML models can result in significant cost optimisation, quality consistency, ecosystem upliftment and improved customer satisfaction.
The full potential of AI and ML is realised only when the scale of operations is big enough. How can the average SME benefit with their limited resources?
I have had the opportunity to work with SMEs both in the enterprise and start-up ecosystem. My experience has been that entrepreneurs and SMEs can leverage AI/ML in multiple ways despite their limited resources:
Open Source and Low Cost Platforms: There is a lot of AI/ML work which is happening in the open source world that SMEs can leverage from AI chatbots to personalisation of products and services for improved customer experience. Again, from a manufacturing perspective there are low cost solutions for predictive maintenance or marketplaces and shared platforms that can be leveraged for logistics and inventory cost optimisation.
Partner ecosystems: Every organisation doesn’t have to re-invent the wheel and set up their own AI/ML platforms. SMEs and start-ups can collaborate across partner ecosystems to leverage AI/ML capabilities. For example, a typical supplier ecosystem will have large enterprise and SME partners. SMEs can benefit from the customer integrated business planning or supplier ecosystems with AI/ML capabilities to better understand global benchmarks on quality, performance, sustainability and risk management.
Automating manual work and better decision making: SMEs can use AI/ML to automate a lot of the manual work and with data available on their customers, suppliers and other ecosystem partners they can do better decision making with these models.
The human element remains critical in deployment of new technologies. How is skill development to be planned in a scenario of not yet mature technological advances?
Over the last decade that I have spent in the transformation space, the focus has been predominantly on following a ‘Build Operate Transfer’ model. Whilst a specialist team initiates the transformation journey the key to transforming the DNA of the organisation lies in ensuring that the existing business teams are re-skilled and upskilled to embrace the new environment and champion the new way of working. This happens through a three pronged approach:
Re-skill: Here the re-skilling and upskilling is linked to specific projects that the team is working on. Therefore, they get the chance to apply the new skills as they are learning them to solve a live business problem. This ensures better understanding and higher adoption as seen from my experience across organisations
Re-imagine: Once an employee has experienced the benefit of doing things differently which leads to enhanced productivity they become influencers, champions and ambassadors for the new way of working. With a little bit more support from the organisation they also start re-imagining what their potential future roles may look like. This happens across both white and blue collared workers. As an example, in my previous organisation a person in the automotive after sales service function did a project on reducing customer churn and learnt analytics on the job. Post the success of this program he re-imagined his role and along with his functional expertise in after sales service he also chose to become the data and analytics champion for the after sales service function across the business
Re-Invent: The millennial generation are already digital natives but for middle and senior management employees to embrace new roles which are digital first they often look for safety nets. At one of the organisations where I was driving the transformation agenda, we offered a select set of employees the opportunity to do a one year ‘baptism by fire’ by becoming part of the ‘digital garage’ we had created. They had the opportunity to learn and implement emerging technologies to solve business problems working alongside the best in the industry globally. Post the first year they were given a choice to continue with the ‘digital garage’ or head back to their original roles. This ‘safety net’ enabled a lot of curious minds who were performance focused and were open to career pivots to take a leap of faith and a year later 90% of them stayed with their new roles.
(The views expressed in interviews are personal, not necessarily of the organisations represented)
Namrita is a senior strategic executive with CXO level success in leading organisations transform their businesses leveraging digital and technology. Currently, she is the Chief Digital Officer of the Chemicals, Filaments and Insulators sector at Aditya Birla Group. A digital evangelist, her forte lies in creating competitive advantage for organisations by re-defining business models, re-imagining customer experiences, re-engineering business processes, building people capabilities and orchestrating shifts in mindsets and organisation culture.
With a career spanning two decades, Namrita has been both an entrepreneur and worked with MNCs and Indian conglomerates across sectors – chemicals, agriculture, automotive manufacturing and retail, logistics, travel and hospitality dotcom and technology in USA, UK and India. An award winning business leader, Namrita also serves on the Board of an Indian automotive ecommerce company as an Independent Director and is an Angel Investor across multiple technology led businesses. Besides being a Thought Leader and hands on expert in Industry 4.0 technologies, Namrita is a Certified Customer Experience Professional and leverages these capabilities to advise businesses and boards on digital, technology and customer experience
You can follow her on Twitter (@namritamahindro) or connect with her on LinkedIn (www.linkedin.com/in/namritasehgal).