A Practical Guide to Assessing Your Industrial Digital Transformation Needs
This white paper introduces the Smart Manufacturing Kaizen Level (SMKL) method developed by Mitsubishi Electric, a structured framework designed to help industrial manufacturing and infrastructure organizations assess their digital transformation needs and identify the most appropriate digital solutions. By evaluating two key dimensions — scope of work and digital maturity stage — the SMKL matrix guides organizations from basic data collection through visualization, analysis, and full process optimization. The guide walks through real-world customer examples and a transportation use case to demonstrate how the method is applied in practice. Organizations gain a clear baseline of where they currently stand, where they want to go, and what hardware and software solutions can bridge the gap. Ultimately, the SMKL method empowers businesses to pursue continuous improvement and operational excellence with confidence and clarity.
In this guide, you'll explore
- The SMKL matrix maps two critical dimensions — scope of work (single machine to full facility) and digital maturity stage (collect, visualize, analyze, optimize) — to pinpoint where an organization stands and where it needs to go.
- Three core questions drive the SMKL assessment: Where are you currently in your digital transformation? Where do you want to be? And what are your goals and targets?
- Real-world customer examples (Customer A and Customer B) plus a transportation use case illustrate how the SMKL method translates into actionable, tailored digital solution recommendations.
- Key industrial digital transformation concepts — including IIoT, Edge Analytics, Digital Twin, Smart Manufacturing, AI, and Big Data Analytics — are clearly defined to help organizations build the foundational knowledge needed to make informed decisions.
- The SMKL method is rooted in the Kaizen philosophy of continuous improvement (Plan, Do, Check, Act), ensuring that digital transformation is not a one-time event but an ongoing journey toward automating optimization.