Selected work supporting high-stakes cost, pricing, and sourcing decisions

Project 1

Aggregate Cost Forecasting & Driver-Based Cost Ranging

Decision context

Leadership needed to understand where cost pressure was really coming from — and whether supplier price increases were economically justified or simply opportunistic.

Headline pricing trends were noisy, inconsistent, and difficult to defend in negotiations and internal approvals.

What we did

• Built an aggregate cost index using multiple public market indices (Federal Reserve Economic Data, Statistics Canada, and category-relevant public sources)

• Used category total system cost to identify which cost drivers had the greatest economic exposure and were most likely to influence overall cost movement

• Applied algorithmic weighting approach to combine market indices into an aggregate signal, rather than relying on spend proportions alone

• Validated index selection with business stakeholders to ensure no economically material cost drivers were missing — including indirect or embedded inputs (raw materials, fuels, utilities, storage, maintenance)

• Developed forward-looking cost range based on weighted market signals and scenario analysis, rather than supplier-quoted pricing

• Visualized results in a decision-ready format suitable for negotiations and approvals

Outcome

• Replaced negotiation-by-anecdote with evidence-backed cost ranges

• Enabled leadership to challenge blanket price increases using driver-level logic

• Established a defensible economic baseline for negotiations, approvals, and internal alignment — before commitments were made

Project 2

Supplier Pricing Prediction & Value Segmentation

Decision context

Procurement teams needed to understand why suppliers were pricing differently for similar assemblies — and whether observed price differences reflected real design complexity, supplier capability, or misalignment with business expectations.

The challenge was not ranking individual suppliers but understanding how different groups of suppliers trade off cost, performance, and commercial alignment when serving comparable assemblies.

What we did

• Built a pricing feature set using procurement transactions, historical supplier quotes, and assembly design attributes rather than predefined should-cost models

• Applied clustering and segmentation to group suppliers with similar cost-performance-alignment drivers

• Summarized each cluster as a profile across key business criteria (cost exposure, delivery, safety, rebates, preference, and recency), enabling comparison of trade-offs rather than point-by-point supplier debates

Why it mattered

• Shifted pricing discussions from quote-by-quote negotiation to pattern-based evidence at the portfolio level

• Enabled leadership to distinguish:

  • Pricing driven by real design and operational complexity

  • Versus pricing driven by supplier behavior, commercial structure, or misalignment

• Made visible which supplier profiles were:

  • Under-leveraged

  • Over-leveraged

  • Or representative of scalable best practice

Outcome

Procurement teams gained a clear, defensible view of how different supplier profiles performed relative to business needs, allowing them to benchmark pricing, prioritize supplier development or consolidation, and focus negotiations where the economic impact was highest.

Project 3

Pricing and Capacity Decisions Under Demand Uncertainty

Decision context

Pricing and availability decisions in short-term rental markets are rarely isolated. Changes made to one listing, location, or property type can influence demand and outcomes across a broader set of comparable listings.

Property owners and managers needed to understand where pricing, availability, and reinvestment decisions would have the greatest downstream impact, particularly in markets shaped by seasonality, substitution effects, and uneven demand concentration.

What we did

• Built demand and availability features using historical booking behavior, listing characteristics, location attributes, and temporal signals

• Used predictive models to estimate price and availability ranges under different demand scenarios, explicitly surfacing uncertainty rather than point estimates

• Modeled interdependencies between listings and market segments based on similarity, substitution, and shared demand patterns

• Represented these relationships as a network, where nodes reflected listings or segments and connections reflected demand overlap

• Used network structure to identify high-influence hubs—areas where pricing, availability, training, or renovation decisions would disproportionately affect outcomes beyond a single listing

How to read this network

This network visualizes the relationships between property owners, neighborhoods, and listing activity.

• Nodes represent entities (property owners, neighborhoods, listings).

• Node size reflects influence — larger nodes indicate higher activity, stronger connectivity, or greater impact on pricing and availability.

• Connections show relationships between owners and neighborhoods where listings are concentrated.

Diamond markers highlight dominant nodes:

• Orange diamonds indicate high-activity neighborhoods.

• Red diamonds indicate property owners with outsized influence (multiple listings, strong pricing power, or high availability concentration).

When both an owner and a neighborhood are highly connected, they appear together within dense clusters—signaling areas where pricing and availability are tightly coupled.

Why this matters

This structure makes it possible to see patterns that traditional tables and averages hide:

• Which owners disproportionately influence supply and pricing

• Which neighborhoods act as demand or pricing anchors

• Where market power is concentrated, creating risk or opportunity

How this was used

• Identified high-impact owners and neighborhoods requiring differentiated pricing, training, or policy treatment

• Prioritized where to focus expansion, renovation, and availability efforts

• Informed predictive models for rental pricing and availability by incorporating network influence rather than simple averages

Outcome

The network transformed a fragmented rental dataset into a decision-ready market map—allowing teams to focus attention where structural influence, not just volume, mattered most.

Related insight — Why defensible cost analysis breaks down in supplier negotiations

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