The manufacturing industry has always thrived, thanks to the right technology and tools.
During the 1800s, the industry thrived due to the development of machinery. Now, as the fourth industrial age takes over, we are seeing the advent of new technology that can boost productivity and remove traditional obstacles preventing the industry from reaching its full potential.
The main challenge, today, is the absence of a platform that can analyse data and break it down into useful insights that optimise the manufacturing process. Fortunately, big data analytics platforms in manufacturing can optimise your production and cut down on costs.
While I have explored how data analytics can improve manufacturing in the past, in this post, I focus on how we can boost productivity and operating costs.
Here are some of the ways big data analytics in manufacturing can add value to your operations.
While custom manufacturing is a consumer-friendly practice, there are certain industries where consistency in quality is key; for example, pharmaceuticals. In this industry, the product is often made in a series of batch processes, which can lead to inconsistencies in product quality.
By using data analytics, organisations can conduct multivariate data analysis. The intent, here, is to better define quality control parameters and use continuous process verification to ensure consistency in product quality.
Furthermore, real-time manufacturing analytics can provide manufacturers with the insight they need to reduce batch variability. They can use the information to adjust process parameters during manufacturing to reduce deviations that might compromise product quality.
Advanced data analytics uses high-level methods to conduct sophisticated multivariate data analysis (MVDA).
This new level of analysis allows manufacturers to analyse data using several variables at the same time. What this means is that manufacturers can perform “what-if” calculations, allowing them to conjure multiple scenarios. The ability to project the future, based on different variables, can help your company future-proof business strategies and determine where your processes could go wrong.
This makes it easier to make long-term structural adjustments that optimise production.
It’s somewhat common to encounter bottlenecks when it comes to production. These bottlenecks could be a result of poor equipment efficiency because when equipment breaks down, it can halt the entire production line, driving up operating costs.
By using analytics in manufacturing, you can collect data from different sources, like ERP, the environment, and maintenance. This helps you determine when your equipment requires maintenance and prevent breakdowns during production.
Advanced analytics in manufacturing can help organisations in the industry make unexpected discoveries through experimentation. For example, by using big data analytics in manufacturing, manufacturers can experiment with different inputs like carbon dioxide, temperature, and coolant pressure.
This calculated experimentation allows you to make unexpected discoveries in your industry.
Big data analytics platforms in manufacturing can make product customisation easier to execute. Customisation in manufacturing is becoming a pivotal part of the industry. But, while it sounds awesome in theory, it is very difficult to pull off in practice. This is because making the shift from mass production to custom production is not possible without the right equipment.
Data analytics platforms make this a more practical feat. Big data analytics tools give manufacturers the insight they need to analyse the different factors that determine custom manufacturing, like customer preferences and production processes, making it much easier to customise manufacturing.
Removing complexities in the supply chain
In a global, connected environment, supply chains can be long and complex.
Analytics in manufacturing can help streamline the supply chain, reducing operating costs and optimising production. With this level of insight, it allows manufacturers to examine the ins and outs of the supply chain and pinpoint existing weaknesses.
Big data analytics platforms in manufacturing help organisations maintain the right balance of production processes and variable inputs to optimise production and reduce operating expenses.
Manufacturing analytics can help optimise business processes either by reducing the variable inputs needed or by discovering new methods to improve production.
With the high rate of adoption of sensors and connected devices, there has been a massive increase in the data points generated in the manufacturing industry. These data points can be of various types. Data types range from a metric detailing the time taken for a material to pass through one process cycle to a more complex one, like calculating the material stress capability in the automotive industry.
With this surge in data available, there is no wonder why big data analytics in manufacturing is a hot topic.
Manufacturing remains a critically important part of the world’s economic engine, but the role it plays in advanced and developing economies has shifted dramatically. The manufacturing industry market was valued at $904.65 million in 2019 and is expected to reach $4.55 billion in 2025.
Big data is essential in achieving productivity, improving efficiency gains and uncovering new insights to drive innovation. With big data analytics in manufacturing, manufacturers can discover new information and identify patterns that enable them to improve processes, increase supply chain efficiency and identify variables that affect production.
Leaders in manufacturing enterprises understand the importance of process – KRC research study found that 67 per cent of manufacturing executives planned to invest in data analytics, even in the face of pressure, to reduce costs in this volatile climate.
To understand big data analytics in manufacturing and its impact, let us dive into how it’s intervention helps streamline operations.
Since manufacturing profits rely heavily on maximising the value of assets, asset performance gains can lead to big productivity improvements. By the same token, a reduction in asset breakdown can reduce inefficiencies and prevent losses. For these reasons, manufacturers focus on maintenance and continuously optimise asset performance.
Machine logs contain data on asset performance. This data is potentially of great value to manufacturers, but many are overwhelmed by the sheer volume of incoming information. Data analytics can help them capture, cleanse and analyse machine data to reveal insights that can help them improve performance.
In addition to enabling historical data analysis, data can drive predictive analytics, which manufacturers can use to schedule predictive maintenance. This allows manufacturers to prevent costly asset breakdown and avoid unexpected downtime. A study found that big data analytics can reduce breakdowns by up to 26 per cent and cut unscheduled downtime by nearly a quarter.
In an increasingly global and interconnected environment, manufacturing processes and supply chains are long and complex. Efforts to streamline processes and optimise supply chains must be supported by the ability to examine every process component and supply chain link in granular detail. Data analytics gives manufacturers this ability.
With the right analytics platform, manufacturers can zero in on every segment of the production process and examine supply chains in minute detail, accounting for individual activities and tasks. This ability to narrow the focus allows manufacturers to identify bottlenecks and reveal underperforming processes and components. Analytics also reveal dependencies, enabling manufacturers to enhance production processes and create alternative plans to address potential pitfalls.
Traditionally, manufacturing focused on production-at-scale and left product customisation to enterprises serving the niche market. In the past, it didn’t make sense to customise because of the time and effort involved to appeal to a smaller group of customers.
Data analytics is changing that by making it possible to accurately predict the demand for customised products. By detecting changes in customer behaviour, data analytics can give manufacturers more lead time, providing the opportunity to produce customised products almost as efficiently as goods produced at a greater scale. Innovative capabilities include tools that allow product engineers to gather, analyse and visualise customer feedback in near-real time.
According to a Deloitte review of the rise of mass personalisation, the ability to postpone production gives manufacturers new flexibility that allows them to take on made-to-order requests. The ability to postpone production can reduce inventory levels and improve plant efficiency. A streamlined manufacturing process is not only beneficial – it gives manufacturers a way to maintain efficiency while customising manufactured goods.
Big data analytics in manufacturing presents many promising and differentiating opportunities and challenges.
According to a McKinsey report, worldwide consumption will nearly double to $64 trillion. In such a scenario, data analytics provide manufacturers with a huge opportunity to predict, innovate and implement their approaches.
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Big data analytics has helped improve the productivity of many industries such as the manufacturing industry. Your organisation can also enjoy the benefits of leveraging your data analytics with the Selerity analytics desktop.