Machine learning & predictive analytics: Reshaping manufacturing
Published: 04 February, 2020
Vivek Shah, Syncron, takes a look at how machine learning and predictive analytics are reshaping manufacturing.
Regardless of industry or vertical, companies around the world are encountering a new generation of customers with ever-evolving expectations, and these new demands are forcing brands to redefine the way they do business.
Today’s customers overwhelmingly favour the simplicity of subscription models, where they pay a flat monthly fee for access to a product in the form of a service. For example, consumers no longer buy or rent DVDs, they subscribe to Netflix; more of them prefer services from Uber or Lyft over traditional car ownership. As these digital-age consumers enter the industrial workforce in large numbers, they are causing a redefinition of business models, leading to an increased trend towards subscription-based services in manufacturing industries.
This shift towards a subscription-based business model in the manufacturing industry is being referred to as servitisation, where manufacturers no longer strictly sell new products, but instead sell access to and the outcome those products deliver. This new way of delivering services is forcing manufacturers to shift to subscription-based pricing models, where Product-as-a-Service (PaaS) becomes the norm. Manufacturers of complex industrial equipment, because they operate in a B2B environment, can attribute this shift to another major cause: their customers are also dealing with complex operating paradigms, forcing them to improve productivity and capital utilisation. In these scenarios, customers are looking to simplify their businesses, and increasingly favour suppliers who can help minimise their risks in operating a profitable business.
The full realisation of servitisation – which could take up to 15 years – especially impacts manufacturers’ after-sales service organisations (the service delivered after the initial sale of a product). In today’s current reactive, break-fix service model, the responsibility of maintenance and ensuring equipment availability falls on the customer. In a servitisation model, however, manufacturers will own the responsibility of maintenance and repairs and will need to focus on maximising product uptime – since revenue can only be earned when their products are available to generate output in the field.
Below, I outline where and how manufacturers will need to use machine learning and predictive analytics, plus how it differs from common instances in consumer-facing markets.
Machine learning and predictive analytics
The use cases of machine learning and predictive analytics are as varied as the industries within manufacturing. However, there are a few common use cases that apply to most manufacturing verticals – typically grouped under terms like Smart Manufacturing, Industry 4.0 or Industrial Internet of Things.
Predictive maintenance: Predictive maintenance is the most well understood and varied use case in most manufacturing industries. Here, data from process monitoring sensors like temperatures, pressures, flows, vibrations and more are captured in real-time and used in pattern recognition software to detect the earliest symptoms of wear and tear that are predictive of eventual functional failures. Early detection and prediction can help to prevent failures or at least help to plan for eventual corrective actions leading to minimised downtime. Downtime – especially unplanned downtime – can be a very expensive event, possibly leading to millions of dollars in losses. Some analysts estimate that unplanned downtime in certain industries is worth $20 billion.
Process optimisation: Process optimisation, where existing processes are updated and optimised based on historical data, is a critical use case, especially in industries such as power generation, oil and gas refining, petrochemicals and chemicals. In this instance, sensor data feeds machine learning algorithms for yield and quality optimisation of output components for different combinations and quality of input raw material feedstocks. This also helps with energy efficiency, thus improving sustainability and profitability for these process manufacturers. In the global airline fleet, for example, a one per cent fuel saving would save $30 billion over the next 15 years.
Supply chain and inventory management: High levels of raw material, work-in-process and finished goods (e.g. replacement service parts) inventory are one of the highest contributors to inefficient capital utilisation for discrete manufacturing industries. Using machine learning to improve raw material and demand forecasts, while meeting dynamically changing production goals, helps improve capital utilisation while supporting lean and just-in-time manufacturing production goals.
In today’s customer-driven world, manufacturers can no longer rely on selling expensive spare parts and service since these become costs of supporting a single-price subscription. This shift will require manufacturers to completely re-think how they operate – new organisation structures and skilled resources, new incentive models, new KPIs to measure success and new processes replacing ones developed over decades and centuries. They will have to become data-driven organisations, investing in technologies to connect and track products, collect data and efficiently analyse these massive amounts of operational and service data, using technologies like IoT, machine learning and predictive analytics. This will strain manufacturers’ existing organisations and IT infrastructures, necessitating investment in highly scalable cloud-based solutions to lay the foundation for a successful future.
Manufacturers that embrace these changes will be the winners, while others will struggle to stay relevant. In fact, the ones that can successfully adapt to these paradigm shifts will be able to gain significant advantage over their competition.
