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Cold Foods Industry NewsSupply Chain Logistics for Refrigerated & Frozen Food

Study: 75% of manufacturers still employ manual data collection procedures

The findings revealed that 75% of respondents are still employing manual approaches, 53% use spreadsheets to collect data and 47% still rely on pencil and paper.

March 2, 2017

Despite digital transformation being seen as a critical issue at board level, research from InfinityQS International, Fairfax, Va., reveals that organizations are failing to automate their quality management and data capture methods. In fact, many manufacturers still rely on time-consuming and labor-intensive manual procedures to collect information.

InfinityQS conducted a survey with 260 manufacturers—including some of the world’s largest manufacturing organizations—to gain insight into their needs, processes and uses for data collection. When it comes to managing data collection procedures, the findings revealed that 75% of respondents are still employing manual approaches, 53% use spreadsheets to collect data and 47% still rely on pencil and paper.

However, 77% of these manufacturers want to adopt automated data collection. This demonstrates the respondents’ awareness surrounding the shortcomings of maintaining a manual approach to data collection – a key challenge they face in managing and processing data.

InfinityQS’ findings match those of a report from the Boston Consulting Group (BCG), “Sprinting to Value in Industry 4.0,” which uncovered manufacturers’ desires to adopt advanced digital industrial technology. However, the Boston, Mass.-based management consulting firm reported that manufacturers face a host of barriers to adoption, including defining an effective implementation strategy and challenging cultural change within an organization.

According to an InfinityQS spokesperson, a first step of Industry 4.0 adoption is technology to automate their data collection methods.

“The manufacturing sector is facing intense pressure to improve the way they manage product and engineering information from employees, customers, suppliers and industry collaborators. Traditionally, a lot of information has lived in discrete systems with limited or restricted access. As a result, information can be difficult to find and cumbersome to digest, often supported with labor-intensive, manually operated data collection procedures. Additionally, these data-capture methods are increasingly at risk from human error, and often detract from the real business of innovation, revenue growth and customer satisfaction due to their time-consuming nature. For many manufacturers, this fact is not lost on them, and that is why digital transformation is becoming such a critical conversation.

“Industry research from the Cloud Industry Forum (CIF), UK, suggests that 71% of firms either already have a digital transformation strategy in place or are in the process of implementing one – this is the same for the manufacturing industry. Changing employee expectations means staff needs access to increasingly flexible IT infrastructures. This is particularly apparent in their demand for greater automation of data collection methods.

“The ability to gather and compare quality-related data from multiple sources is vital to making real-time decisions in manufacturing environments. This is something we’ve looked to address with our data management system (DMS), which automates information collection by standardizing data from multiple sources and then writing that data into a centralized database. In doing so, the risk of variances within the quality management and data collection procedures is dramatically reduced, and therefore, the risk of recalls is also diminished.

“Looking to the future, as manufacturing firms start to reassess their current technological capabilities, the ability to automate critical actions will pave the way for other recognizable benefits such as increasing operational efficiency, improving product quality and achieving significant cost savings. It’s imperative that organizations look to address this now or risk being left behind by their more tech-savvy counterparts.”

KEYWORDS: automation big data cloud-based solutions data collection systems internet of things

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