In today’s competitive marketplace, it is more important than ever for manufacturing enterprises to be data driven. Manufacturers can identify trends, improve efficiency, reduce costs, increase quality, and make fool proof, data-backed decisions by collecting and analyzing data. Are you a CMO looking for the key steps to become a data-driven manufacturer? Then this blog is for you.
A recent study by Harvard Business Review found that 91% of enterprises believe that being data-driven is critical to thriving a business. That apart, haven’t we seen some of the world’s leading manufacturing giants losing steam over time? Kodak, Nokia, and Sears are some instances where these companies’ inefficiencies to keep up with trends, collecting and analysing data effectively have led to their downfall. However, if you look at the case studies of multinational conglomerates such as Siemens, General Electric, and Honeywell it is amazing how these manufacturing enterprises have embraced data and, with the help of data analytics have stayed ahead in their game.
Why and how is data necessary for manufacturers?
When it comes to competitive advantage and more efficient operations, manufacturers who use data to derive strategic and operational insights are steadily edging out their competitors. Additionally, they are more adaptable and robust in changing market realities.
Manufacturers today aspire to fundamentally alter how they do business to achieve operational excellence, sustain growth, cut costs, and improve product quality, all in a way that supports sustainability goals also. But accomplishing this depends on having the capacity to decide wisely, at the appropriate moment, and using the proper data.
Let us observe the case study of Toyota- a leading car manufacturing enterprise that uses data-driven analytics to improve its cars’ reliability. By collecting data from its cars as they are being driven, Toyota is able to identify potential problems before they occur. This has led to a significant reduction in the number of recalls that Toyota has issued.
What are the steps to becoming a data-driven manufacturing enterprise?
Is there a guide for transforming a manufacturing enterprise to one that is data-driven? Is it even possible, really? Yes, that is the answer.
Although no universal maxim exists, we give the four fundamental steps to enhance your data journey in the paragraphs below.
Develop a comprehensive data strategy to leverage the full potential of data
The first step involves leveraging the full potential of data by developing a solid, comprehensive end-to-end data strategy that guides the complete data journey, from gathering and storing data to using it.
Your strategic imperatives form the basis of a thorough data strategy. A data strategy must always be connected to the targeted business value that an organization hopes to accomplish to be effective. Put another way, figuring out what matters most to you, what improvements you want to see, and what is feasible.
There are various data use cases for manufacturing:
- The most well-known applications of data on the shop floor are quality analytics of raw materials or parts and predictive maintenance of machinery. Machine learning can similarly assist process improvement.
- Digital twins are yet another application. Digital copies, for instance, can be tested and improved in product design before investing money in manufacturing. Additionally, performance across the course of the product’s whole life cycle can be built and tracked using digital models.
- Finally, a crucial area of discussion for the sector is how to use pertinent and valuable data to meet sustainability and carbon neutrality targets and satisfy stakeholder expectations. The recent energy crisis has led to a rise in data use cases related to energy use.
In other words, asking (and responding to) the broad question – What can data accomplish for my organization? This is the first step in developing a data strategy.
Managing data throughout its entire lifecycle
Data management is the second step in the data journey. It refers to how data is handled throughout its full lifecycle, including user management, access rights management, quality management, security management, and integrity management.
Realizing value from data requires a comprehensive, enterprise-wide strategy for data management that incorporates a semantic model to produce clean data sets, arrange them, and elicit meaning. Additionally, it is a requirement for developing digital twins.
The creation of a “digital continuum,” or seamless and integrated data flows between business units, processes, and technologies, is the holy grail for a data-driven (manufacturing) organization. Adopting data standards specific to the industry greatly aids in achieving this interoperability.
However, standards are not the only factor in data quality. Throughout the entire data life cycle, an informed strategy is necessary to ensure the data is fit for purpose. This includes:
- Gathering and collecting pertinent data from assets and throughout the business value chains
- Recognizing and defining data ownership, including the various levels of access and rights inside your organization, and when data can and must be utilized. (Of course, when establishing data ownership, data security, integrity, and categorization are essential, especially for organizations dealing with higher safety regulations, like those in the aircraft sector.)
- Creating defined end-of-life plans for data and creating protocols for data preservation and destruction
Using enterprise intelligence to advance data
Harnessing enterprise intelligence to advance data becomes increasingly crucial as organizations change.
The capacity of your organization to transform context-relevant data into useful insights that increase company value is known as enterprise intelligence (EI). By using increasingly sophisticated analytics and AI, you can grow data up the knowledge pyramid from data to wisdom.
There are several essential steps to climbing this knowledge pyramid, even if every organization’s route is unique—both in terms of pace and legacy constraints:
- Implement simple reporting.
- Implement fundamental automation and straightforward logic to interact with your data.
- To blur the lines between the digital and physical worlds, use intelligent process automation or digital twins.
- Use cognitive computing to give machines the ability to sense and infer, including analytics, machine learning, and pattern recognition.
- Adopt artificial intelligence so processes can use technology like neural networks or genetic algorithms.
Organizational preparedness- key to becoming a data-driven manufacturing enterprise
Effectively handling the human component of change is crucial; some might even say critically crucial.
Adopting a data-first approach is essential to establishing credibility and guarantee a return on your data investment. Human change takes time. Therefore, one must be patient and persistently willing. Once you have a defined strategy and shared vision, clearly communicating that to all organization members is a key step to transforming into a data-driven manufacturing organization. Performing skill gap analyses for the entire organization and particular departments can assist in moving from the current situation to the desired future state.
A transparent approach and change champions are also essential for successful transformation. Furthermore, you must accompany change by a dedicated leadership team that continuously evaluates and adjusts.
Data can lead the product roadmap. Businesses must first have a solid understanding of their data before developing data-driven goods. They also require the capacity to gather, purify, connect, and analyse data efficiently. A commitment to leveraging data is necessary for a manufacturing company to become data-driven. Companies must take advantage of the data they acquire to enhance experiences throughout the organization. Although making this commitment can be challenging, it’s essential if companies want to remain competitive.
Are you eager to start advancing with your data-driven transformation? We offer various services, such as InsightBox, an end-to-end managed BI service offering, and data analytics consulting services. You can check out our services at Saxon.ai and book a consultation.