Benchmarking in Cost Estimation

If you benchmark projects on €/kWp, you miss the point. The real metric is €/MWh. In practice, I keep running into the same discussions: How do you compare Project A (say, in Eastern Europe) with Project B (say, in Southern Europe), when grid, construction, O&M or financing have totally different cost profiles? Instead of arguing over individual cost items, there’s a simpler way: look at LCOE (€/MWh). What really matters (short & clear): --> €/kWp = construction indicator, but not a success factor. --> LCOE (€/MWh) captures CAPEX, OPEX, performance (PR/degradation), financing & lifetime. --> A “more expensive” project can deliver cheaper power thanks to higher yield, longer lifetime, or better financing. --> Investors and banks already benchmark on €/MWh, not €/kWp. Number flavor (utility scale, all-in incl. EPC, development, financing): -->Typical Utility Scale DE/CEE (2024): ~560–600 €/kWp all-in -->Project A: 580 €/kWp, PR 80%, WACC 6%, 25 years -> ~49-52 €/MWh -->Project B: 640 €/kWp, PR 87%, WACC 5%, 30 years -> ~40-43 €/MWh --> Same installed capacity, different assumptions –> output beats input. Do you still benchmark projects on €/kWp? Or already on €/MWh? And which 3 variables move your LCOE the most: PR, WACC, O&M, degradation? #AndreasBach #LCOE #SolarPV #ProjectFinance #CleanEnergy

Executives and CFOs: Understanding AI is important for budgeting your companies existence! This benchmark comparison of leading AI models highlights a critical issue for strategic budgeting and resource allocation: the rapidly evolving landscape of AI capabilities and their associated costs. The data clearly shows significant variations in performance across different benchmarks (MMLU, MATH, reasoning, etc.) and, crucially, in pricing. While the raw performance numbers are impressive, the financial implications are even more impactful. The disparity between the cost of 1M input/output tokens across these models illustrates the need for a nuanced approach to AI investment. Simply choosing the most powerful model isn't always the most efficient strategy. Business goals must drive technology choices. For example, a company focused on precise mathematical calculations might find a model like Llama 3.3 70B to be more cost-effective than GPT-40 despite the latter's higher overall scores in other areas. This necessitates a thorough cost-benefit analysis, integrating pricing data with the specific performance requirements of each project. This analysis should be a core part of your budgeting process. Understanding the relative efficiency and cost of different AI models will be crucial in allocating resources to maximize return on investment. Ignoring this aspect could lead to wasted expenditure and lost opportunities. Companies need to actively integrate this type of data analysis into our future planning to leverage AI effectively and responsibly. A dedicated discussion on this topic is recommended for your next strategy meeting.

No supplier has the perfect cost structure But here’s how you can get closer to it… Every supplier is different Imagine 3 suppliers: A, B and C: A has the lowest material cost…but the highest waste. B has the lowest conversion cost…but the priciest packaging. C has the lowest overheads…but the most expensive materials. It makes you think… Wouldn’t it be great if one supplier was the lowest on every cost line? Alas – this supplier doesn’t exist! Or does it? 👉 Enter the concept of ‘cherry-picking’: → Benchmark the 3 suppliers → Add a fourth column for the ‘perfect supplier’ → Cherry-pick the lowest cost for each cost line …you’ve just created the perfect (fictitious) supplier! And whilst it doesn’t exist, it triggers the right questions: 💡How could A reduce their waste like B? 💡How could B reduce their overheads like C? 💡How could C buy their materials like A? You won’t optimise every variable But even a clutch of cross-pollinated ideas will drive value The insight? → Don’t assess suppliers in isolation → Look right across your pool of suppliers All insights on cherry-picking are welcome 👇 Frost Procurement Adventurer ♻️ Repost to help others maximise value 🔔 Follow Simon Frost for more top tips on cost modelling PS – Cherry-Picking has similarities to Could-Cost: Could Cost = a theoretical model (what it should cost if optimised) Cherry Picking = a market-benchmark model (what it can cost if we take the best of what’s out there)

Different Types of Cost Estimation Tools & Techniques : 💡 Ever wondered why two suppliers quote completely different prices for the same part? 👉 The answer lies in how we estimate cost. In the world of Cost Engineering, understanding the right Cost Estimation Tools & Techniques makes the difference between price guessing and fact-based costing. Over the years, I’ve explored multiple methods - from traditional spreadsheets to AI-driven cost models - and here’s a structured overview that every engineer, sourcing professional, and value analyst should know 👇 Cost estimation means predicting the true cost of a product, process, or project - by analyzing materials, labor, overheads, and profit. 💡 Why Cost Estimation Matters The goal? To bring cost transparency, control, and continuous improvement to every stage of product development. Having the right Cost Estimation Tools & Techniques helps teams make informed, data-driven decisions 👇 🔹 Should Costing (SC) – Breaks down cost by material, labor, and overheads. 👉 Purpose: Helps in supplier negotiations and identifies cost reduction opportunities. 🔹 Zero-Based Costing (ZBC) – Justifies every cost from the ground up, starting at zero. 👉 Purpose: Eliminates unnecessary expenses and focuses on essentials. 🔹 Activity-Based Costing (ABC) – Allocates indirect costs based on activities. 👉 Purpose: Highlights high-cost areas for precise cost control. 🔹 Best-in-Class Costing (BIC) – Benchmarks against industry best practices. 👉 Purpose: Closes cost gaps and boosts efficiency. 🔹 Target-Based Costing (TBC) – Aligns design and production cost with target market price. 👉 Purpose: Ensures profitability while meeting customer expectations. 🔹 Kaizen Costing – Drives gradual and continuous cost reduction. 👉 Purpose: Improves productivity and reduces waste over time. 🔹 Lean Costing – Integrates Lean principles to eliminate waste and enhance efficiency. 👉 Purpose: Enables continuous cost reduction through process optimization. 🔹 Benchmark Costing – Compares with global competitors to set cost targets. 👉 Purpose: Identifies regional or supplier-specific cost advantages. 🔹 Parametric Costing – Uses mathematical models to estimate cost. 👉 Purpose: Perfect for early-stage estimates during new product development. 🔹 Product Costing (PC) - Calculates total cost across the product lifecycle. 👉 Purpose: Provides a detailed view of cost distribution and lifecycle value. 🚀 Final Thought Each method serves a unique purpose - but together, they build one powerful outcome: Cost Transparency ➜ Profitability ➜ Sustainable Value. 💬 Which of these cost estimation techniques do you use most in your organization? Let’s exchange ideas and strengthen our global cost engineering community 👇 Join the following WhatsApp group for further learning. Should Costing Community 2 https://lnkd.in/gSiE-fxy ...more #CostEngineering #ShouldCosting

Should-cost methodology is emerging as one of the most reliable solutions to help #upstream players address their current challenges, providing the granular cost transparency needed to deal with the changing landscape. So how does it work? After breaking down the total cost of a project, product, or service into granular components and assessing the #cost drivers for each, companies can determine the reasonable should-cost of a service or product based on its constituent elements. Compared to traditional solutions (which limit the benchmark to a finite number of past projects), should-cost can estimate the costs associated with any combination of design, geographic footprint, and commercial agreement. Initially developed, fine-tuned, and deployed at scale in the automotive sector, the #shouldcost methodology uses bottom-up modeling of all supply chain costs through a four-step approach: ➡️Step 1: Analyzing the design choices and 2D or 3D drawings of the project to derive a bill of quantities for raw and bulk materials. ➡️Step 2: Mapping the end-to-end value chain to identify all the manufacturing steps required to produce each component. ➡️Step 3: Costing the required quantities and value chains to calculate direct costs, leveraging proprietary databases and productivity models tailored to each country, technology, and sector. ➡️Step 4: Completing the bottom-up should-cost calculations to define should-cost components, including all elements of suppliers’ cost structures. Through its flexible, unbiased, and fact-based methodology, a should-cost analysis can, therefore, provide up-to-date, end-to-end transparency on the entire supply chain cost structure for an upstream project’s tenancy in common (TIC) investment. To illustrate, we performed a deep dive should-cost analysis for #LNG tanks, providing full transparency on key cost drivers for further negotiation with the supplier. This analysis enabled a fact-based negotiation with the supplier and led to an 8% cost reduction on the final negotiated price compared to the initial bid. #capitalexcellence #mckinsey #lngtanks #oilandgas #procurement #projectmanagement #labor #materials