4 steps to become a data-driven manufacturer

Over the last few years, in my interactions with manufacturing clients worldwide, one thing has stood out clearly. Manufacturers that use data to gain strategic and operational insights are pulling ahead of their peers in tangible ways, both in competitive advantage and more effective operations. They’re also more resilient in the face of emerging market realities and agile enough to adapt to change.

Today, manufacturers want to transform the way they operate to sustain growth, reduce costs, improve product quality and achieve operational excellence—all in a manner that supports their sustainability goals and transition to net zero. But achieving this is grounded in the ability to make informed decisions, at the right time, based on the right data.

What does it mean to be data-driven? Is there a blueprint for becoming a data-driven organization? In fact, is it even achievable?

The answer is yes.

While there is no one-size-fits-all mantra, below I share the 4 key steps to advance your data journey.

1. Harness the full potential of data with a robust and holistic end-to-end data strategy that underpins the entire data journey—from collecting and storing data to putting it into action.

Building a comprehensive data strategy starts with your strategic imperatives. In other words, identifying and understanding your priorities, the positive changes you seek, and what is possible. For a data strategy to be effective, it must always be tied to the desired business value that an organization intends to achieve.

In the case of manufacturing, there are several data use cases:

• On the shop floor, the most well-known use of data is for the predictive maintenance of machinery and quality analytics of raw materials or parts. Similarly, using machine learning can support process improvement.
• Digital twins is another use case. For instance, in product design, digital replicas can be tested and improved before spending money on production. Digital models can also be used to build and track performance over the entire life cycle of the product.  
• Lastly, using relevant and actionable data to achieve sustainability and carbon neutrality targets and address stakeholder expectations is a key topic for the industry. Lately, data use cases on energy consumption have increased due to the energy crisis.

In short, developing a data strategy starts with asking (and answering) the overarching question: What can data do for my organization?

2.   The second step in the data journey is data management, or how data is treated across its entire lifecycle, including access rights and user management, quality, security and integrity.

Taking a holistic, enterprise-wide data management approach that includes a semantic model to create clean data sets, structure, and elicit meaning is critical to realizing value from data. It is also a prerequisite to creating digital twins.

The holy grail for a data-driven company is creating a “digital continuum,” or seamless and integrated data flows across business units, processes and systems. Adopting industry-specific data standards helps significantly in reaching this interoperability.

Yet, data quality is not tied to standards alone. Ensuring data is fit for purpose requires an informed approach across the entire data life cycle, which includes:

• Collecting and capturing relevant data from assets and across the business value chains
• Understanding and defining data ownership, such as when data can and needs to be used and the different levels of access and rights within your company (Of course, data security, integrity and classification are critical when documenting data ownership, especially for those organizations dealing with enhanced safety protocols, such as those within the aerospace industry.)
• Building a clear plan for data’s end-of-life and developing protocols for archiving and destroying data

3. As organizations evolve, it becomes increasingly important to leverage enterprise intelligence to take data to the next level.

Enterprise intelligence (EI) is your organization’s ability to turn context-relevant data into actionable insights that drive business value. Once harnessed, you can scale data up the knowledge pyramid—moving from data to wisdom by employing increasingly complex analytics and AI.
While every organization’s path is different—both in terms of pace and legacy constraints—there are some fundamental stages to climbing this knowledge pyramid:

• Deploy basic reporting.
• Build in basic automation and simple logic to interact with your data
• Employ intelligent process automation or digital twins to blend the boundaries between the digital and real world
• Apply cognitive computing, such as analytics, machine learning and pattern recognition, to enable machines to sense and infer
• Embrace artificial intelligence so technologies such as neural networks or genetic algorithms can be incorporated into processes

4. Equally important (and some might even argue most important) is managing the human side of change effectively. When it comes to becoming a data-driven manufacturing organization, organizational readiness is paramount.

A data-first mindset is critical to building trust and ensuring ROI on your data investment. Human change does not happen overnight; it requires patience and enduring willingness. Transforming into a data-driven organization calls for a shared vision and roadmap that is clearly communicated to all members of the organization. Conducting skill gap assessments for the entire organization and specific departments can help evolve from the status quo to the desired future state.

In addition, successful transformation requires champions of change and a clear methodology. And, at the helm, a committed leadership team must accompany change and constantly reflect and adapt.

Keen to move forward with your data-driven transformation? Get in touch with me.