As global competitive pressures continue to drive changes and higher expectations for your organization, industrial analytics has a magnified role in addressing increasing market demands.
Actionable insights from the analytics are the fuel that can drive the optimization of industrial operations and production, the creation of new revenue streams and commercialization of new business models.
Given that enterprise digital transformation (DX) projects take significant time and effort to implement and define the future direction of the business, it’s paramount that DX leaders do their due diligence to understand the unique capabilities of industrial analytics, the challenges of specific industry domains, and the landscape of available solutions in the market before settling on a solution.
Foundational Pillars
While solutions vary, every solid industrial analytics solution should:
1. Offer a cohesive analytics solutions stack
Using modular, integrated technical capabilities, your enterprise must be able to benefit from a full-service customer journey—from setting up an industrial data management foundation to single pane-of-glass views of operations; collaborative data mashups to building and deploying self-service machine learning or analytical models at the controller, edge or in the cloud.
2. Enable versatile use cases and business outcomes at scale
No matter how small or simple the use case, you want to apply the analytics solution right away for immediate return on investment yet be extendible to meet future use cases. Use cases can be very specific per industry, and a solid analytics solution set should address the nuances of the industry, discrete and process manufacturing.
3. Empower OT/IT and executive personas
Manufacturing operations is run by a wide range of personas—control or process engineers, line operators, data scientists, IT analysts, plant managers, executives—who have varying, and sometimes, conflicting needs. A mature analytics solution set should empower people to achieve their operational or business goals, without elongating the learning curve.
4. Include industrial fit-for-purpose applications
Implementing any solution in an enterprise setting is an exercise in melding process, people, and technology—and takes significant effort. But the deployment-to-value realization curve can be significantly accelerated if targeted solutions templates or analytics applications are available off-the-shelf. Therefore, a mature industrial analytics solution must have pre-built fit-for-purpose solutions that can deliver value right off the bat.
5. Offer end-to-end consulting and implementation services
Pulling off a successful implementation, ensuring adoption, and demonstrating clear ROI is not easy – and that’s why you need a trusted partner who can offer end-to-end consulting and delivery services by leveraging a proven, iterative methodology for successful deployment.
The implementation methodology must keep continuous improvement in mind and incorporate the experiential learnings from earlier analytics. And, the solutions provider should have an impressive portfolio of successful analytics implementation for customers across industries for a variety of use cases.
Industrial analytics can put your manufacturing performance on an upward trajectory, but a lot needs to happen before that. Use our Industrial Analytics resources to filter out the noise and choose the right solution and partner.
