Big data in manufacturing: a compass for growth

Infor Global Solutions (ANZ) Pty Ltd

By Jo-Anne Ruhl, General Manager, Pacific, Infor
Wednesday, 12 August, 2015



Big data in manufacturing: a compass for growth

Embracing the power of big data can help manufacturers be more competitive and improve efficiencies.

Data has long been the essential lifeblood of manufacturing, driving efficiency improvements, reductions in waste and incremental profit gains. But today a new breed of big data analytics is taking over manufacturing and providing a totally new dimension to the value of research and trend analysis. Now, data is no longer being used for reporting past activities; it’s helping manufacturers predict future events, foresee risk, understand their extended value chain and enhance the customer experience they deliver. Data has grown up, with new multidimensional capabilities and broader horizons. It’s like a compass, pointing the way for manufacturing growth.

What’s driving big data?

Big data is quickly becoming an important element of the fourth generation of ERP technology. Today, fourth-generation ERPs are transforming outdated manufacturing facilities into highly automated, efficient powerhouses. Big data’s ability to engage data, people and processes is helping to create a new era for manufacturing. Whether you call this new era the ‘factory of the future’ or the ‘connected enterprise’, there are two elements driving the transformation: innovative mindsets and data.

Manufacturers now have access to more resources for data capture and tracking than ever before. Data is coming from traditional sources, like the classic customer survey, as well as more innovative applications, like smart sensors and the use of the Internet of Things (IoT) to capture machine readings. Managers throughout an organisation can access real-time data for every aspect of the products being manufactured, from warranty claims to cycle times and inventory counts. In fact, the volume of data available is so vast that it can quickly become overwhelming and cause data paralysis.

In spite of this, manufacturers are beginning to realise the value of big data. According to a recent report by Pierfrancesco Manenti, The Digital Factory: Game-Changing Technologies That Will Transform Manufacturing Industry, 47% of manufacturers expect big data analytics to have a major impact on company performance — making it core to the future of digital factories — while 49% expect advanced analytics to reduce operational costs and utilise assets efficiently.

Additionally, manufacturers are putting their investment dollars on the line for big data, too. According to the TechTarget 2015 IT Priorities Survey, 31% of the 2212 respondents worldwide said their organisations plan deployments of business intelligence, analytics or data warehousing tools in 2015. A quarter of respondents expect to invest in big data analytics and 21% expect to invest in big data processing and management.

Where is all the data coming from?

Manufacturers today have more resources for data capture and tracking than ever before. The overabundance of data can be intimidating and cause manufacturers to struggle to understand how to use the data available and how to harness its power.

Data can come from both internal and external sources or be generated by machine-to-machine interaction. Together, these sources can provide manufacturers with the information they need about their customers, products, processes, people and equipment.

  • External sources: Manufacturers can turn to external sources, such as user groups, social media, focus groups or surveys to build customer data. Third-party surveys, portals and call centres add an impartial layer to the data collection that is often less threatening to the customer. The promise of anonymity can also generate higher response rates. This fact finding can be used to build accurate profiles of customers and prospects, including subjective or ‘soft’ characteristics, like colour and design preferences, common buying triggers or evaluation criteria.
  • Internal sources: Manufacturers can also turn to their own systems for data capture and analysis. A modern, integrated ERP system can provide data on products, processes and people at all levels and departments in the organisation. Data collected through an ERP system offers benefits, such as real-time reporting with up-to-the-minute accuracy, a common database that provides one version of the truth, the ability to drill down into details for historical depth and relational data with context and relevance.
  • Machine-to-machine: Smart sensors and the IoT can now collect data directly from machines and equipment and send it on to an ERP system, EAM system or other enterprise application. Built-in, low-cost sensors can detect a wide range of attributes, including location, weight, temperature, vibration, flow rate, humidity and balance. These conditions can then be monitored in order to identify and predict performance issues that require service, repair or replacement. This allows manufacturers to get an early warning of impending issues, and hopefully intervene before there’s a catastrophic interruption to processes and performance.

Machine data provides valuable insights about how equipment functions in use, whether it is the machinery on the factory floor or the product in the consumer’s home. Detailed product lifecycle analysis can point engineers to future design improvements and performance enhancements. This data also gives manufacturers the ability to predict opportunities to sell replacements and upgrades. Predicting future needs can also help with sales forecasting and inventory management, so the organisation can prepare for changing demands.

What can manufacturers do with all that data?

As mentioned, in this new paradigm of manufacturing data the focus is no longer primarily on reporting on past events; data today is also being used to predict trends and anticipate needs. Big data acts as the gateway to the future.

Manufacturers certainly understand the value of predictive abilities. Anticipating consumer trends, stocking necessary inventory and maintaining adequate resources to meet customer orders have been high priorities for manufacturers for decades. As speed of delivery and just-in-time inventory strategies gained importance, the ability to accurately forecast needs also grew. Manufacturers learned — sometimes the hard way — the importance of choosing the right influencing factors or the right combination of factors. When attempting to predict the future, one data source is seldom sufficient.

Today, predictive analytics has become a valuable science and tool for manufacturers. It turns data collected from numerous sources into a blueprint for future actions. Modern business intelligence solutions now have the ability to project trends with a high degree of accuracy. As in any data initiative though, the output is only as good as the input. Manufacturers must take care to choose reliable data sources and to continue to refine which influencing factors provide the best signposts for future activities.

Predictive capabilities offer many benefits to manufacturers, including:

  • Staffing readiness: When manufacturers have a reliable forecast of product sales, departments throughout the organisation can plan personnel staffing accordingly, hiring personnel as needed and allowing adequate time for team training.
  • Raw resources in stock: Procurement teams can use accurate predictive forecasts to better plan just-in-time inventory levels of raw materials, preventing delays due to stock outs.
  • Spare parts inventory: An accurate understanding of the product lifecycle translates to being better prepared for necessary maintenance, including having the consumables and parts that require regular replacement in stock.

Anticipating consumer trends also provides a much-needed competitive head start, allowing the timely manufacturer to be first to market with a product innovation or first to introduce a breakthrough concept to an emerging niche market. Companies that are early arrivers often maintain valuable ownership of the market.

Successful product innovation largely relies on an accurate reading of the market’s preferences and needs. Design engineers need to understand the consumer’s pains in order to determine the potential value of new products and help prioritise allocation of R&D dollars. Big data makes this possible.

How can big data fuel growth?

How can big data provide significant return on investment (ROI) and lead to manufacturing growth? This is the question manufacturers must answer if they want to take full advantage of big data’s potential.

Big data acts as a compass — it provides a guide, but it’s not magically going to start generating greater sales and more customers. Collecting data — whether from machines through the IoT or from customers through online portals — is not the end. That data must be translated into action. This is the step that requires careful attention to detail and a thorough understanding of the relevance of the data. This is where many manufacturers fall short in their big data initiatives.

However, with careful analysis, data can be used to identify, analyse and foster growth opportunities by helping manufacturers:

  • Identify new geographic regions to target: Manufacturers can match demographics of current customers with profiles of prospects in other regions or countries. Global expansion becomes easier when manufacturers know what prospect characteristics to target.
  • Expand into niche/micro-markets: Manufacturers can use data to spot pools of untapped opportunity. They can identify micro-markets that are currently under-served or that need specialised make-to-order (MTO) products. By being the first to reach a new market, manufacturers can become trusted advisors and build a market.
  • Tap into a customer base: Data can help manufacturers identify opportunities to upsell, cross-sell and resell to their current customers. They can predict their customers’ needs and the performance gaps in their current products. Data from successful customers can help manufacturers reinforce their message and demonstrate the value of upsell and cross-sell products.
  • Foster customer intimacy: When manufacturers understand their customer pains, they can provide better out-of-the-box solutions. Data is the common language between manufacturers and customers; it helps manufacturers better understand and serve their customers, and forge strong bonds with them.
  • Innovate: Manufacturers can use data to accurately predict the impact of design and engineering refinements, and speed product innovations and launches of breakthrough solutions. With the right data and analysis tools, they can accurately forecast the sales impact of a new product — as well as its risks.
  • Improve product lifecycles: Data can help manufacturers identify design flaws and weak elements of mechanical design. They can use this data to refine product features. Plus, this data can help identify suppliers and subcontractors who are meeting expectations, and eliminate subcontractors who are performing poorly.
  • Increase value-add: Data can help manufacturers extend their offerings to enhance the customer experience and the value-add. Services like consulting, installation, aftermarket service, extended warranties and ongoing maintenance contracts offer possible new sources of revenue. Data can help manufacturers manage these service-related offerings with greater efficiency.
  • Improve profit margins: Manufacturers can use data to optimise their lean initiatives to reduce waste, improve productivity and stretch already thin margins.

It’s time for big data

It’s becoming clear that manufacturers need to embrace big data in order to remain competitive. Manufacturers that make the most of the customer, product and equipment data they capture stand to improve their ability to innovate, please their customers and bring more profitable products and services to market more quickly.

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