Lean Compliance Startup Model™
Improvement roadmap to effective compliance
What you have now
Assortment of separate parts, some working, some not, and most not working together as a whole.
What you need
All essesential parts working together as a whole to fulfill its purpose.
When it comes to implementing compliance systems many take a phased: component first-approach. This comes from years of prescriptive obligations and a focus on implementing "shall statements" in order to pass certifications and audits. When the focus is on compliance to prescriptive "shall statements" rather than improving outcomes you will find these familiar steps promoted:
Understand the elements of the regulation or standard.
Map existing practices to the elements.
Identify where current practices do not meet the standard.
Engage these deficiencies in a Plan-Do-Check-Act (PDCA) cycle.
Target these deficiencies for compliance with the standard.
While this approach is familiar (although PDCA maybe new to some) it is not without limitations the most significant being that it often fails to deliver operational systems fast enough or at all. Companies usually run out time, money, and motivation to move beyond implementing the parts of a system to implementing the interactions which is essential for a system is to be considered operational.
To support performance and outcome-based obligations another approach is needed than the traditional component-first approach, one that:
creates and sustains system properties over time, and
achieves operational status faster
We know from systems theory that systems are never the sum of its parts but rather the product of its interactions. It is these interactions that cause emergent properties to be produced. For compliance systems these are the outcomes we are targeting: zero incidents, zero violations, zero fatalities, zero emissions, and so on.
We developed The Lean Compliance Startup™ methodology based on the work by Eric Ries (Lean Startup) to emphasize system interactions and achieve operational status faster than traditional approaches. By doing so companies can collect the maximum amount of validated learning about outcome performance with the least amount of effort right from the start and through every version of the system. This accelerates performance improvement as well as the advancement of outcomes which together significantly increase the probability of overall mission success.