Three sessions.
One vertical.

Settled: DTC channel expansion is associated with higher gross margin in CPG companies with strong brand equity (Nike, Levi's cases are well documented). Contested: whether margin improvement persists past the initial 18–24 months when customer acquisition costs are excluded from gross margin calculation — several DTC-first brands have reversed this assumption. Unknown: whether the margin advantage holds when DTC is an incremental channel added alongside wholesale, rather than a channel shift.

Calculation methodology concern: the 8–12 point margin improvement figure assumes customer acquisition costs (CAC) are classified as marketing expense, not COGS. If CAC is treated as a cost of sale — which is appropriate for subscription DTC models and is how several analysts now model it — the gross margin improvement is 3–5 points, not 8–12. The assumption is not flagged in the initiative brief.

“The DTC cases cited (Nike, Levi's) both involve brand repositioning that preceded the channel shift — the margin improvement was partially a function of product mix change, not channel economics alone. This company's product mix is not changing. Applying margin improvement data from brand repositioning cases to a channel-only change will produce a systematically optimistic forecast.”

Confidence: Probable that DTC expansion improves gross margin. Contested on the magnitude — 3–5 points is defensible; 8–12 points requires the CAC classification assumption and a brand repositioning effect that this initiative does not include. Recommendation: restate the initiative forecast with a 3–7 point range and make the CAC classification assumption explicit.

Settled: PMI synergy realisation rates are well studied — McKinsey and BCG research consistently shows 40–60% of deals fail to achieve stated synergies within the projected timeframe. Contested: whether technology sector acquisitions perform better or worse than the cross-sector average — evidence is mixed, with enterprise software showing higher realisation rates but consumer tech showing lower. Unknown: the specific talent retention risk in this deal, which drives a disproportionate share of synergy assumptions.

Synergy composition analysis: the $400M breaks down as $180M cost synergies (facilities, headcount) and $220M revenue synergies (cross-sell). Historical data: cost synergies realise at approximately 80% of target within 24 months; revenue synergies realise at approximately 35% of target in the same window. Applied to this deal: expected realisation = ($180M × 0.80) + ($220M × 0.35) = $221M. The gap to target is $179M.

“The base rate analysis is correct but the revenue synergy composition matters. The $220M includes $140M from product bundling (historically lower realisation) and $80M from geographic expansion (historically higher). Using disaggregated base rates: ($140M × 0.25) + ($80M × 0.55) = $79M revenue synergies. Total: $223M. The gap is the same, but the distribution of risk changes the integration priorities.”

Two integration priorities based on disaggregated analysis: (1) Front-load geographic expansion synergies — highest probability of realisation, least integration complexity. (2) Treat product bundling synergies as a 36-month target, not 24-month — revise board reporting accordingly to avoid miss-vs.-target framing in Q6–Q8.

Settled: route optimisation algorithms reduce average cost-per-delivery by 8–15% in controlled studies across multiple carriers. Contested: whether these gains are additive to existing logistics management systems or whether they partially overlap with efficiency gains already captured. Unknown: real-world performance in high-density urban environments with dynamic constraint changes (traffic, access restrictions, recipient availability).

14 citations reviewed. Three vendor white papers cited as independent evidence were produced by the same vendor group whose product is being evaluated. Flagged Critical — removed from evidence base. Without vendor white papers, independent peer-reviewed evidence supports 8–11% cost reduction, not the 15–22% in the proposal.

Additionality problem: the company has already deployed basic route optimisation (4 years prior). The vendor is claiming efficiency gains relative to an unoptimised baseline. The marginal improvement from state-of-the-art AI over existing optimisation is the relevant measure — literature on this specific comparison suggests 3–6% incremental improvement, not 8–15% from baseline.

“The methodologist is correct on additionality, but 3–6% incremental improvement on a $2.4B annual delivery cost base is $72–144M. At a 3-year payback the investment is still justifiable even with the corrected estimate. The problem is not whether to invest — it is that the vendor's claims were evaluated against the wrong baseline, and the procurement team needs to renegotiate performance guarantees against the marginal improvement standard.”