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Predictive analytics is the use of past data to extrapolate possible future scenarios and is made possible by big data. The larger and more varied the dataset, the more accurate the prediction will be; hence the necessity for massive computing power. Before human analysis, the data is usually processed using AI and/or machine learning techniques to spot patterns, trends, and markers that may imply certain events or trajectories. These results are then packaged into a format designed for human consumption for further analysis—ideally yielding actionable insights. What predictive analytics means in practice Manufacturers, for example, can use predictive analytics to prevent machine faults from occurring. They would do so by collecting real-time streams of machine health data—a combination of heat detectors, vibration detectors, ultrasound, acoustic sensors and so on. This data can be analyzed for changes to normal operation. Move Keyboard Shortcuts The frequency emitted by a fast-rotatin

Corporate strategy, defined broadly as providing meaningful and actionable strategic analysis, planning, and development to management teams, is critical to company direction and growth. To build a balanced portfolio and drive sustainable value, companies must calibrate their mix of assets, capabilities, and processes across resource allocation, performance improvement, M&A, and divestitures. But the fact of the matter is that corporate strategy faces insurmountable challenges when it comes to strategic execution. Harvard Business Review notes that strategy executives are often pulled toward internal issues: resolving conflicts, reconciling budgets, and managing performance. Making corporate strategy challenges infinitely more complex is the COVID-19 crisis. The impact of the global pandemic is far-reaching, affecting businesses across all sectors and resulting in extreme supply chain disruption. Global businesses are quickly adapting to the “new normal” as executive leaders look f

How does quality impact your manufacturing costs? In the past, many businesses thought of this as a strictly linear equation: it costs more to produce higher quality goods. The more sophisticated “cost of quality” methodology, however, shows that this massively oversimplifies the issue. In fact, quality is intertwined with costs in complex and sometimes surprising ways. In this article, we’ll explain how the methodology works, the factors it considers, and how digital manufacturing affects and improves the cost of quality. The relationship between cost and quality The relationship between cost and quality isn’t so much a correlation as a delta. It’s not as straightforward as saying “in order to improve quality, we need to spend more”. Rather, if you imagine different measurements of quality and cost as multiple bell-curves, there is a substantial cross-section in which you can bring down manufacturing costs and minimize waste while still achieving a high standard of product and good le

Lean manufacturing is a methodology for reducing waste in the manufacturing process. First introduced by Toyota in 1930, the term ‘lean’ was coined in 1988. It has since become common practice in the industry. An increasing number of manufacturers have been adopting lean methodology in recent years. But what do we mean by lean? And why is it important? What is lean manufacturing? Lean manufacturing is the continual pursuit of waste reduction in the manufacturing process. Waste is anything that uses resources without adding value to the customer. The seven wastes defined in the original Toyota Production System are: Unnecessary transportation Excess inventory Unnecessary motion of people, equipment, or machinery Waiting—inactive employees, or idle equipment Over-production of product Over-processing—putting in more resources than a customer needs Defects—which require resources to correct By following the lean methodology, practitioners aim to reduce or entirely remove these seven waste

As the number one early morning financial program in the UK, "Wake Up to Money" on BBC Radio is a great start to the business day, with topical content, debate and a string of interviews with leading executives. Recently, PTC’s Paul Haimes, European VP of Technical Sales, was invited on to the show to talk about augmented reality (AR) and the critical role it plays in the VentilatorChallengeUK Consortium. Led by High Value Manufacturing Catapult CEO Dick Elsy, the Consortium has risen to the "call-to-action" by the British government for manufacturers and technology companies to come together and increase the quantity of ventilators needed in the treatment of COVID-19. With effective collaboration at its heart, great British manufacturing and technology companies, such as PTC, GKN Aerospace, McLaren, Ford Motor Company, Meggit and Siemens UK, had started to work with ventilator manufacturers Penlon and Smiths Group. They shared a common goal – to work at unprecedent

Production planning is critical for the effective running of any manufacturing plant. Without planned production, you will not be able to forecast what, when and how you will produce anything. A long-term view of the production process is necessary for a fully optimized workflow. The Industrial Internet of Things (IIoT) has provided a revolutionary way to understand your manufacturing plant, giving you access to instant data that would have previously been inaccessible. The Pros of Planned Production On-time delivery By having a clear view of your resources, your available machines, and manpower, you can accurately map the amount of time it will take to turn your raw materials into a valuable product. This means you can always ensure the timely delivery of your product. Failing to meet your planned production demands and deadline can lead to wasted inventory and can lead to costly damage to your business’s reputation. Clear raw material procurement Procuring too much or too little raw