February 2020

Focus on:Maintenance 4.0 Maintenance Matters February 2020 www.pwemag.co.uk Plant & Works Engineering | 13 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 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 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. ONLY BUY FROM AN APPROVED HOSE ASSEMBLY SCHEME MEMBER FIND YOUR LOCAL APPROVED PROVIDER www.hydraulichosesafety.co.uk 01608 647900 IMPROVED QUALITY BFPA AUDITED WILL NOT RE-END OR MIX AND MATCH COMMITTED TO HEALTH AND SAFETY OPERATORS TRAINED FULLY COMPLIANT WITH INDUSTRY STANDARDS