Gap Between C- Suite and Factory Floor: Data-Driven Decision-Making

September 22, 2020

Industry 4.0 initiatives are changing process management for managers, bringing in more data and new challenges with it. Currently only about 50% of business decisions are based on data analytics. The rest is based on experience or opinion. Why isn’t there more data-driven decision-making when the amounts of data in the manufacturing industry is ever increasing?

Gaps in the Collected Data

Although the data is collected, the digitalisation strategies are still being planned and implemented, while many are battling with sticking to a comprehensive strategy, change management within the organisation and gaps in the organisational structure. Creating a situation where management can have access to some of the data, but the quality or quantity of the data aren’t enough for reliable decision-making.

Even when there are data based KPIs generated for a process, they largely cater to the people on the factory floor instead of creating a comprehensive overview to the management. For example, they focus on the amount of downtime instead of the reasons behind each downtime occurrence. Therefore, helping to plan rather than making improvement decisions.

C-Suite Being the Catalyst for Change

The support for becoming more data-driven indecision-making needs to come from the top. Although it’s a tough battle between focusing on daily production and planning for the future, C-suite needs to lead by example to create a company-wide change. At the end of the day, the shift in thinking is going to drive the improvements in the future.

According to IDC by2024, enterprises with intelligent and collaborative work environments will see30% lower staff turnover, 30% higher productivity, and 30% higher revenue per employee than their peers. Whether the solution is hiring a CDO or investing more in digitally transforming business sectors, the data efforts need to be centrally managed and the need must be recognised by all C-level participants.  

Data Improvement Technologies

Currently data scientists spend 80% of their time preparing data to be used for analysis.Considering the scarcity of data scientists in manufacturing, the factories are underserved and the amount of KPIs that can be tracked accurately is quite limited.

To combat this issue, there is a variety of data governance tools on the market, allowing either manual supervision or using machine learning to detect corrupt and inaccurate records. As the data is constantly changing and the amounts of data keep growing, it’s important to focus on data management asa crucial discipline for manufacturing. It’s also clear that scaling data management by hiring doesn’t work in the long run – the solutions must be technological to enable manufacturers to focus on their core business.

Data can’t be an After Thought

While investments in data are growing, 90,9%companies still feel that people and process changes are the biggest barriers to becoming data-driven organisations. This is the perfect place for theC-level to establish a data-driven culture by abiding by it themselves and requesting it from others. Consistent efforts need to be made to change the historic working processes and implement data-driven decision-making. In the long term, that will be the measure of competitiveness of an organisation, both in the eyes of their partners and customers.

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