CHAPTER 1 · THE QUESTION
The eleven weeks
…is what Avery loses on every new-product launch — and the largest fixable lever in R&D.
SITUATION TODAY
What happens today on every launch
A new launch follows the same waterfall. Dr Aiyana Chen's team spends 3 weeks framing the problem (searching prior trials in PfS + lab PDFs + memory), 5 weeks running trial-and-error formulations (one design-of-experiment at a time, instrument-bound), 2 weeks validating sustainability (REACH + ESPR + LCA hand-checks), and 1 week handing off to the plant. That 28-week cycle is the gap to category-leading materials companies who launch in 16.
16w
Materials industry leader
Top quartile launch cycle
28w
AD today (avg of 6 active programmes)
Source: PfS programme history 2022-26
6
Programmes per year
Across MG · SG · Embelex
~$60k
Loaded R&D cost / week
Scientist + instrument-hours + materials
BENEFIT
$3.2M per programme · $19M / year
R&D cost out · 11w cycle back · +25 pts first-pass success · 4w earlier revenue
WEEKS
11
lost per launch · 28w today → 17w possible
FORMULATION → LAUNCH CYCLE (weeks)
0w14w28w
HOW IT WORKS
How SCIKIQ takes the 11 weeks back — three moves
1
Replace search with retrieval
R&D Copilot returns 18 prior similar PFAS-free experiments + 1 supplier datasheet in 30 seconds — not 3 weeks of manual hunt.
▣ Hybrid RAG (BM25 + OpenAI embeddings) over PfS + patents + lab PDFs
→
2
Replace trial-and-error with active learning
Bayesian optimiser proposes the next experiment most likely to hit spec; P(meet target)=0.78 from 21 prior runs — not 8 trial-and-error attempts.
▣ Gaussian Process surrogate + multi-objective Bayesian opt
→
3
Replace post-hoc checks with pre-lab scoring
When the scientist saves a draft formulation, sustainability + regulatory + customer-fit scores are written back to the PfS Sample UDF in real-time.
▣ Hazard classifier · LCA inference · OData v4 UDF write-back
BREAKDOWN
Where the 11 weeks (and $3.2M / programme) actually go
BEFORE / AFTER
Before / After · per programme
| Dimension | Today | With SCIKIQ + Thermo | Δ |
|---|---|---|---|
| Cycle time (Idea → A-sample) | 28 weeks | 17 weeks | −11w / −40% |
| First-pass formulation hit rate | 22% | 47% | +25 pts |
| Lab cost per programme | $4.2M | $2.8M | −$1.4M |
| Failed runs per launch | 11 | 4 | −7 runs |
| Revenue captured earlier | — | +4w × programme margin | +$0.7M |
WHO WINS
"I get six weeks back per programme. My team runs four targeted trials in the time we run one trial-and-error sequence today."
— Dr Aiyana Chen · Principal Scientist · Adhesives · Mentor (OH)
⚠ IF WE DON'T
Cost of inaction (annualised)
- $19M of R&D spend trapped in cycle time (6 programmes × $3.2M)
- 4 launches/yr arrive after the regulator (PFAS, ESPR, Prop 65 windows missed)
- 2 customer programmes ceded to faster competitors (industry observation)
11wback per launch28w → 17w · same lab · same scientist
$19Mtrapped / yearAcross 6 active programmes
+25pts first-passFormulation hit rate
0.78P(meet target)Bayesian posterior · first try
Every great industrial company faces the same question. The factory works. The labs work. The customers pay. But the next product — the one the regulator hasn't named, the customer hasn't asked for — is taking too long. At Avery Dennison that gap is eleven weeks per launch. Across six active programmes that compounds into $19M of trapped R&D spend per year.
28 weeks today. 17 weeks possible. The delta is the prize.
AI / ML / LLM IN THIS CHAPTER
DATA SOURCES