Value Validation and Adoption Framework

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Applies to DecisioWorks v1.4.0

Value Validation and Adoption Framework

A production-decision initiative should first prove usefulness in a bounded operating context. Validation compares a baseline and an adjusted run with the same data and agreed evaluation rules, then uses the evidence to judge practical value.

Minimum validation package

Item	Minimum requirement
Scope	one workshop, order family, bottleneck, or planning horizon
Business data	orders, products, routes, resources, time, and required material data
Structural constraints	applicable capacity, calendar, shifts, maintenance, BOM, and readiness rules
Decision objectives	a small set covering delivery, load, waiting, changeover, inventory, or stability
Measures	name, unit, granularity, baseline, threshold, and owner
Review roles	planning, operations, data/IT, and accountable management

Six-step flow

1. Define the decision problem, scope, and evaluation period.
2. Freeze the baseline data, current plan, rules, manual corrections, and actual outcome.
3. Build computable input and complete relationship and SolverReady checks.
4. Run the baseline and retain configuration, status, results, and evidence.
5. Change only agreed objectives, rules, or resource parameters, then rerun.
6. Review executability, explainability, and reusable decision knowledge.

Measure more than an aggregate score

Retain business outcomes, run quality, and adoption quality. Every metric should state its unit and granularity. Load, for example, must identify whether it is minutes, hours, or a ratio and remain traceable to a work center or planning time unit.

Go, Adjust, or Stop

• Go when relationships are stable, key results can be traced to business objects, and users support expansion.
• Adjust when the chain runs but data, objectives, or rules need another iteration.
• Stop when scope, critical data, or evaluation ownership cannot be established.

Stopping is valid evidence: it prevents a larger commitment before prerequisites exist.

Evidence in DecisioWorks

Scenario Lab retains data, relationships, baseline and adjusted configurations, results, and comparisons. Objective-material audits check direction and semantics. Analysis links aggregate measures to object-level details, while run records retain error codes and diagnostics.

Continue with End-to-End Scenario Walkthrough and Results, Evidence, and Feedback.