The manufacturing sector faces a plethora of opportunities by effectively using Big Data. Paul Leahy examines how data can be used to achieve responsible production, augment product engineering through smart automation, drive predictive maintenance, and adapt to the industry’s changing demands?
The benefits of technologies such as Big Data and artificial intelligence (AI) are increasingly apparent; when deployed appropriately there’s no denying their potential impact on an organisation’s bottom line. According to a study by Partners, businesses are reaping the rewards of Big Data and AI, with successful business outcomes almost doubling in the past half decade from 49% to 96%. More and more businesses across sectors are increasing their use of data, with 99% of businesses surveyed by NewVantage shown to have invested in data initiatives. The value of investments in Big Data has also increased 10-fold between 2011 and 2021, and this number is expected to reach US$100bn by 2026.
The implication is clear: businesses across sectors are aware that optimal data-harnessing can deliver great value. To achieve this, however, they will need to tap into the right technological framework.
Conserving natural resources has emerged as a non-negotiable priority for businesses in the face of the growing climate crisis. According to experts, we will cross the point of no return within the next two decades if necessary measures are not implemented on a large scale. As such, manufacturers across the world are switching to responsible production by adopting sustainable practices. Companies are exploring greener alternatives to generate energy, while focusing on resource and energy conservation. For instance, Australian supermarket Coles has reduced its coal power consumption by switching to 70% clean, renewable energy, and has signed multiple wind and solar power purchase agreement (PPAs) to fulfil its commitment to becoming 100% renewable electricity by 2025.
This is where something like Qlik’s Active Intelligence Platform comes in, as it helps create in-the-moment awareness of the business through real-time information to trigger immediate actions. Manufacturers can use this framework to maximise the efficiency of their sustainability initiatives. Active Intelligence analyses up-to-date information sourced from an extensive Internet of Things (IoT)-driven framework that can help companies accurately calculate the rate of resource usage and depletion. It can also help them forecast and determine the quantity of raw materials required to achieve the delivery targets that are subject to change in a volatile market landscape, especially in the post-pandemic era. The seamless workflows enabled by Active Intelligence improve the interactions and flow of data between manufacturers, distributors and retailers for more accurate and efficient decision-making and demand projection.
The insights generated can also be used to minimise wastage while sourcing materials from the primary sector. These insights can empower manufacturers to enhance the intermediate processes, ensuring optimal utilisation of non-renewable resources. Therefore, the intricate analysis performed via Active Intelligence can help companies come up with innovative strategies that reduce and recycle materials to create alternative sources of revenue – boosting overall profitability.
Traditional design procedures involve many levels of scrutiny and fault testing that are not only difficult to maintain manually but can also lead to human errors. Top manufacturers leverage the power of automation and analytics to overcome this challenge while optimising the efficiency of product engineering generative design.
A vast amount of data is involved in the process – and the greater the volume, the more efficient the algorithm. Active Intelligence can help companies leverage dynamic content and logic through AI and machine learning (ML) to drive innovation while minimising costs and time as well as eliminating errors from the production process.
To give an example, a company designing a three-legged chair can feed a detailed design brief into generative design software. The Active Intelligence-powered AI/ML algorithms then come up with all possible configurations and, aligned with ergonomic constraints fed by engineers, produce the best set of solutions. Following the testing phase, the system finalises the optimal solution.
Machine breakdowns are costly affairs – not only because of the cost incurred to replace/repair the machine but also in terms of hours lost due to the operational disruption. What compounds the challenge is that, in the absence of the right technology, predicting equipment failure is difficult. As per a 2018 study, unplanned downtime, or part maintenance, upgrades or repairs cost manufacturers US$50bn a year.
Active Intelligence can empower manufacturers to achieve this. Industrial machines generate huge volumes of real-time and historical data – which can be analysed with AI/ML to predict and trigger proactive maintenance. In doing so, manufacturers can ensure peak performance and business continuity. Top companies such as Mitsubishi Electric are already using intelligent, interconnected systems to drive predictive maintenance, minimising costs and downtime while augmenting productivity and responsiveness.
While the COVID-19 pandemic is a one-off event, disruptions in the supply chain are a regular occurrence. Armed with the power of Active Intelligence, manufacturers can determine with precision how much further they should advance a delivery date in line with the delay in procuring the raw material. Besides calculating how disruption will affect their operations, both in terms of time and finances, Active Intelligence can help businesses to determine the regions/markets where they can divert their supplies to – or source their raw materials from – to mitigate the negative effects on their unit economics.
For instance, Active Intelligence empowered Qlik customer Multipack, an Australian packaging services provider, to strategically scale up and down its operations in line with fluctuating demand during the pandemic. By reducing decision-making time from days to minutes, the platform helped Multipack take just-in-time decisions to efficiently streamline its production processes.
Using systems like Active Intelligence to eliminate operational bottlenecks and optimising processes, industries can start to gain superior financial benefits. Utilising technology to collate, combine and analyse incredible volumes of information will allow enterprises across sectors to make optimal business decisions that ensure they effectively navigate the post-pandemic, increasingly digital-first business ecosystem.
Paul Leahy is Country Manager – ANZ at Qlik.