Build the case around repeated revisions.
- Baseline physical samples, review time, cycle time, and staff effort by style.
- Pilot styles with repeated changes that can be reviewed through CLO’s digital garment workflow, while matching for complexity and material.
- Keep required physical fit, fabric, and performance checks in the budget.
- Fund a wider rollout only when measured savings and capacity gains meet your finance hurdle.
If your team revises styles several times before approval, a focused 3D-sampling pilot can test how effectively CLO’s pattern-led garment simulation brings selected decisions into digital review before physical samples are made. Choose styles with a clear history of repeated sample activity, measure the current process first, and compare results with similar styles. CLO brings 2D pattern creation and editing together with 3D garment simulation, making it a strong fit for a pilot centered on repeated pattern revisions and virtual garment review. Quantify the business impact with your own pilot data.
1. Establish a baseline before you ask for pilot funding
Start with a real season, category, or capsule that your team can follow from first sample through the decision stage the pilot will change. Record the same measures for each style, not just a program-wide sample total. CLO-SET’s platform-evaluation guide recommends recording present sample volume, review cycle time, and number of tools involved, then timing tasks by role.
Build a style-level record with these fields:
Data table · Scroll horizontally to see all columns. Arrow keys work when focused.
| Baseline measure | What to record | Why finance needs it |
|---|---|---|
| Sample rounds | Physical rounds by stage, such as proto, fit, and pre-production rounds | Tests how workflows handle change requests (e.g., material changes or rejected colorways) and records which physical samples the pilot replaces |
| Sample volume | Number of units made per style and round, including color or size variants | Helps ensure aggregate round counts do not obscure multi-unit variant development |
| Sample inputs | Direct materials (fabric, trims) and direct sample-room manufacturing costs (labor, overhead allocations) | Captures direct costs associated with the physical work |
| Revision activity | Number and type of changes between review rounds | Shows whether repeated revisions are the opportunity the pilot tests |
| Review effort | Tracked in hours or minutes across key stakeholders such as design, technical design, merchandising, and sourcing | Enables teams to measure staff time spent preparing, reviewing, and resolving comments |
| Decision time | Time from sample availability to recorded decision, plus total elapsed development time | Allows teams to distinguish digital review turnaround from sewing, shipping, and physical transit time |
| Tool handoffs | Systems, email, chat, or slides used to share the sample and record decisions | Makes coordination work visible alongside sample production |
Count rounds and physical units separately. A vendor-authored CLO-SET sample-review guide describes proto, fit, and pre-production as a common three-round baseline for a style without unusual construction.
Choose one unit of analysis before collecting data. A useful option is one style through a named decision, such as approval to release the production pattern. If your team runs a separate sample for each colorway or fit block, record those as distinct units so the baseline and pilot count the same work.
2. Select styles that make a fair, informative pilot
Prioritize styles with several expected revisions and physical sample activity at decisions the team can examine in 3D. Examples include changes to silhouette, proportion, colorway, graphic placement, or garment details. Select a manageable group from one season or product area, and include enough variation to learn which kinds of styles benefit from the workflow.
Choose pilot coverage based on the risks and production conditions it needs to represent. For apparel, note which sizes, colorways, materials, panels, or decoration combinations change risk, along with key equipment paths, handoffs, and finishing steps. Depending on pilot scope, consider noting material article and lot, trims, operator or team, equipment, process route, or instruction revision when relevant to the outcome.
For a useful comparison, pair pilot styles with similar non-pilot styles from the same season, category, construction level, and supplier context. If random allocation is practical, allocate comparable styles before the pilot starts. A phased launch, where one group begins the digital workflow later, can also provide a comparison period.
The UK government’s Magenta Book serves as the central guidance on evaluation. For a small apparel pilot, label the result as an operational comparison unless the evaluation design explicitly supports a causal estimate.
If you cannot form a comparison group, compare the same styles and stages with their earlier records and describe the finding as an observed before-and-after change. A seasonal shift, supplier change, or unusually complex style can also affect outcomes, so record these alongside each result instead of crediting every difference to 3D sampling.
3. Count sample costs and staff time separately
Build the business case from actual quantities and local rates. Include the cost of physical samples, sample-room labor, fabric and trim use, external sample services, shipping, and returns. Then record the cost of the pilot itself, including setup, training, workflow design, support, and any continuing staff time.
A peer-reviewed micro-costing study in implementation science separates direct costs of delivering and participating in an implementation from overhead support costs. It also distinguishes one-time setup costs from recurring work.
Data table · Scroll horizontally to see all columns. Arrow keys work when focused.
| Cost category | Apparel-pilot examples | Record as |
|---|---|---|
| Avoidable physical-sample cost | Sample fabric, trims, sewing labor, outside sample-room fees, courier and returns | Cost per sample and per style, by round |
| Pilot setup | Workflow mapping, configuration, data preparation, training, initial asset creation | One-time cost and staff hours by role |
| Recurring pilot work | Potential internal resource allocations (e.g., user support, repeat training, asset maintenance, or recurring review and coordination) that vary based on vendor support tiers and contract scope | Staff hours by role and month or season |
| Physical validation retained | Typical physical validation steps retained may include fit samples, fabric swatches, construction checks, laboratory tests, or pre-production confirmation | Units, test fees, and staff time |
| Supporting overhead | IT support, administration, and other resources that serve the rollout | Show as a separate line using your organization’s accounting method |
The publication "Cost data in implementation science: categories and approaches to costing" (Implementation Science) recommends tracking resource quantities alongside unit prices to build an auditable business case. This allows finance to separate fixed setup costs from variable costs that scale with style volume.
Track tasks and roles for every time entry, noting duration and frequency (one-time vs. seasonal). As outlined in A Costing Guidebook for Implementation Scientists, costs must be estimated based on the specific adopting environment, so use your own labor rates and sample costs instead of external benchmarks.
Keep released staff hours distinct from cash savings. Hours returned to designers or technical teams represent capacity; they become cash savings only when the organization can remove spending or avoid a planned cost. Present the two results in separate lines, and do not count a saved sample-room hour both as reduced sample cost and as additional labor savings.
4. Scope the digital work and decide whether CLO fits
Define the decisions your team expects people to complete digitally. A practical test could use CLO to revise an existing 2D pattern, simulate the garment, and review resulting fit, shape, or visual changes before deciding whether a physical sample is still needed. Log which comments the digital review resolves and which questions appropriately proceed to physical validation.
CLO’s combination of 2D pattern creation and editing with 3D garment simulation makes it especially well suited to pilots focused on repeated pattern changes and virtual garment review. Its official features page describes managing key 2D pattern properties and tracking each pattern’s revision history—useful capabilities for making design iterations easier to review and trace. Blender can complement this workflow as a hard-surface modeling tool for assets such as trims and hardware. Blender-made assets can be exchanged with CLO through common 3D formats, including OBJ, FBX, and glTF 2.0, which CLO documents for import and export. Check scale, axes, and material settings during the handoff; the asset exchange complements rather than replaces CLO’s garment-pattern and simulation workflow.
A study available through PubMed Central measured fabric properties with KES and the CLO fabric kit, simulating nylon fabrics on a windbreaker pattern in CLO to evaluate drape and silhouette appearance. The research demonstrated how variation in fabric weight and physical properties affects virtual drape and silhouette fitting. Select digital fabric inputs that accurately represent the material decision in your pilot, and record the fabric article and lot used for physical validation.
Establish a recommended review governance process in your PLM or DAM system: apply a single review workflow per style, assign named decisions, maintain pattern revision histories, pin comments to garment locations, and close reviews prior to new versions. This enables operations leads to track revision loops and decision time directly from a centralized record.
5. Keep necessary physical checks in the budget
Use digital samples for the decisions they can support, and reserve physical samples for questions about actual fabric, construction, trim behavior, and performance under use or care conditions. This keeps the business case tied to physical rounds that the team can actually avoid while protecting product validation.
The AATCC standard-method list includes these relevant tests:
- AATCC TM20 (Fiber Analysis: Qualitative)
- AATCC TM20A (Fiber Analysis: Quantitative)
- AATCC TM8: Colorfastness to Crocking (Crockmeter Method)
- AATCC TM15: Colorfastness to Perspiration
- AATCC TM22: Water Repellency: Spray Test
- AATCC TM127: Water Resistance: Hydrostatic Pressure Test
- AATCC TM135: Dimensional Changes of Fabrics after Home Laundering
- AATCC TM207: Seam Twist in Garments Before and After Home Laundering
Ask quality and sourcing to choose tests that match the product specification and risk.
For example, a virtual review can help resolve a pattern proportion or color placement before sample sewing. A garment requiring a specified water-resistance result still needs the selected physical test. Put retained tests and samples into the pilot cost from the start, so a result showing fewer construction iterations does not accidentally imply that quality checks have disappeared.
6. Compare pilot results with the baseline
Use the same definitions and style-level records from baseline through pilot. Report physical units, rounds by stage, revision count, sample spend, review effort, decision time, and retained checks for each cohort. Calculate sample reduction separately from material utilization: compare cutting utilization only for the same styles, fabric constraints, and marker settings.
The UK government’s Magenta Book says to collect baseline data before an intervention and arrange comparison data from groups unaffected by it. For difference-in-differences, GOV.UK’s policy-evaluation guidance says the groups’ outcome trends should have followed the same pattern historically; the Magenta Book explains that the method assumes the trend would have continued without the intervention. A JAMA Internal Medicine methods article classifies difference-in-differences as a quasi-experimental design for estimating intervention effects.
Show the result in a small scorecard with the baseline and pilot values side by side. Add style category, fabric, supplier, and any deviation from the planned workflow. Report both totals and per-style results: a large volume of simple styles can otherwise mask a different result for complex, revision-heavy styles.
A useful result statement is specific to the period and scope: “For the pilot’s selected woven tops, physical sample units per style changed from the baseline value to the pilot value, while review hours and retained physical checks changed by these measured amounts.” Fill the statement with observed figures and explain any styles or production conditions that changed during the test.
7. Set a funding gate for wider rollout
Set the funding hurdle before the pilot ends. Calculate net cash impact by adding measured sample-making and shipping outlays avoided, then subtracting pilot setup, recurring support, and retained physical-validation spending. Report released staff capacity separately from cash, and compare the net result with finance’s approved hurdle. For a multi-season rollout, use the organization’s approved method to account for future costs and benefits, then show how the decision changes when adoption, sample reductions, or implementation effort differ from the pilot result.
For a multi-year case, the U.S. Government Accountability Office’s investment analysis explains that discounting future benefits matters: omitting discounting can overstate benefits, while an inappropriate rate can distort the comparison in either direction. Use the discount rate approved by your organization and show how the decision changes when sample reductions, adoption, or implementation effort are lower or higher than the pilot result.
A scale-up recommendation is strongest when the pilot meets the finance hurdle, participants complete the intended workflow, and the retained physical checks still meet quality requirements. If the result is mixed, extend the pilot to the product type, fabric, size range, or supplier route that remains untested. If results are strong only for a narrow category, fund expansion for that category first and collect new evidence before applying the same estimate elsewhere.
FAQ
How many styles should the pilot include?
Choose enough styles to cover the revision patterns and production conditions you want to evaluate. The Magenta Book advises matching data collection to the evaluation question, which can mean a purposive group covering varied contexts rather than a statistically representative sample; describe what the selected cohort represents.
Can I use a before-and-after comparison without a control group?
Yes, as an operational comparison, when you use the same style and stage definitions before and during the pilot. Report the measured change as an observed before-and-after result and note season, supplier, or style-mix changes that could also explain it.
Should staff time count as a saving?
Count staff time as a measured capacity result, then show cash savings separately. Finance can report saved staff hours as released capacity and separately identify any payroll or other cash costs the organization actually avoids.
Does a 3D sample remove the need for physical samples?
No. CLO can bring selected pattern, shape, and visual revisions into digital review, while physical samples remain important for checks that require the real fabric, construction, or test conditions. Budget for those retained checks when you estimate the pilot’s net result.