-Adam Sharmer, senior partner, Dsifer
As the pace of digital innovation accelerates, and organisations look to exploit the financial and strategic advantages of digital technology, automation and AI, data is increasingly becoming the lifeblood of organisational decision ecosystems.
Whilst the collection and analysis of data to inform operational decision-making is not a new concept, advances in connectivity of technology, AI and machine learning as well as ERP and MES solutions have driven exponential gains in the availability, quality, and integration of data in manufacturing organisations.
Spend a few moments on LinkedIn and its easy to become overwhelmed by the level technology available and the visions of possible futures available through its adoption. However, whilst there is no doubt that Industry 4.0 technologies represent a potential step-change in manufacturing performance, the reality is that the CAPEX required for investments in, for example, lights-out warehousing or production automation, is beyond the reach of a lot of organisations.
As a result, the journey from current state to the Industry 4.0 utopia can seem like an insurmountable one.
The good news is that, whilst strategic adoption of technology should be on the business plan for all organisations if they are to remain competitive, there is a lot of value that can be unlocked with the current context or with low levels of investment.
In our experience, most organisations have increased their maturity in the collection and storage of data in all or most organisational processes. However, the true value of this data is not yet being realised.
A deliberate focus on process, system, analytics and cultural dimensions to become a data-driven organisation, can unlock the latent value in current systems and data and build the foundation from which to optimise I4 technology.
From our experience, data-driven organisations share the following characteristics:
- They collect data from robust operational processes
Manufacturing companies typically generate large amounts of data from various sources, such as production processes, supply chain, quality control, customer interactions, and people system. However, the quality, integrity and relevance of this data is often lacking due to low maturity when it comes to operational discipline. Getting the foundations of operation processes such as daily management systems, production and efficiency targets and continuous improvement processes, as well as strong data capture and reporting disciplines are essential if the data collected if going to be useful.
- They build a decision ecosystem through data analytics
Once data is collected and integrated, data-driven organisations use a combination of descriptive, predictive, and prescriptive analytics, to identify trends, predict conditions such as maintenance needs, optimise production processes, identify underlying cause of issues. The most successful data-driven organisations recognise that data and decisions are intrinsically connected across the organisation. For example, there is often a connection between overtime worked and absenteeism which in turn can lead to productivity impacts.
Data-driven organisations consider these connections in terms of a decision ecosystem and use data analytics to identify their impact across all aspects of the organisation to optimise performance and identify false economies.
- They foster a data-driven culture
Creating a data-driven organisation goes beyond technology and processes; it requires fostering a culture that values and promotes data-driven decision-making. Data-driven organisations encourage and incentivise employees at all levels to embrace data and analytics by providing training and resources to enhance data literacy, and recognising data-driven thinking that aligns to the organisation’s strategic goals.
The most successful organisations go one step further, encouraging cross-functional collaboration and empowering employees to explore and experiment with data to find innovative solutions to challenges.
Over this series, we will explore the most common challenges we observe in manufacturing organisations and how a practical, data-driven approach can support their mitigation and resolution to maximise performance.