Engineering Project Cost Estimation

estimating equipment cost from engineering drawings: First Estimating Material Cost from Thickness: The thickness of a material directly affects its cost per unit area. Thicker materials require more raw material and often involve more complex manufacturing processes, leading to a higher cost. Example: Let's consider a simple example: a rectangular steel plate. Given: Material: Steel Thickness: 10 mm Dimensions: 2 meters x 1 meter Unit cost of steel: $500/ton (assuming the density of steel is 7.85 g/cm³) Calculations: Calculate the volume: Volume = Length x Width x Thickness Volume = 2m x 1m x 0.01m = 0.02 cubic meters Convert volume to mass: Mass = Volume x Density Mass = 0.02 m³ x 7.85 g/cm³ x (1000 kg/ton) / (1000000 cm³/m³) = 0.157 tons Calculate the cost: Cost = Mass x Unit cost Cost = 0.157 tons x $500/ton = $78.50 Conclusion: For a steel plate with a thickness of 10 mm, the cost would be $78.50 based on the given unit cost of steel. Understanding Manufacturing Processes The cost of converting materials into static equipment depends on various manufacturing processes, including: Cutting: Cutting materials into specific shapes (e.g., laser cutting, waterjet cutting) Forming: Shaping materials into desired forms (e.g., bending, stamping, forging) Welding: Joining materials together (e.g., arc welding, TIG welding) Machining: Removing material to create precise dimensions (e.g., milling, drilling) Assembly: Combining components into a final product Example: A Pressure Vessel Given: Material: Steel (already calculated cost: $78.50) Manufacturing processes: Cutting: Laser cutting (cost per meter: $20) Forming: Press bending (cost per bend: $15) Welding: TIG welding (cost per meter: $30) Machining: Drilling (cost per hole: $5) Assembly: Simple bolt-on assembly (cost per hour: $50) Calculations: Cutting: Assuming 10 meters of cutting are required: Cutting cost = 10 meters x $20/meter = $200 Forming: Assuming 5 bends are required: Forming cost = 5 bends x $15/bend = $75 Welding: Assuming 20 meters of welding are required: Welding cost = 20 meters x $30/meter = $600 Machining: Assuming 10 holes need to be drilled: Machining cost = 10 holes x $5/hole = $50 Assembly: Assuming 1 hour of assembly is required: Assembly cost = 1 hour x $50/hour = $50 Total Manufacturing Cost: Total cost = Material cost + Cutting cost + Forming cost + Welding cost + Machining cost + Assembly cost Total cost = $78.50 + $200 + $75 + $600 + $50 + $50 = $1053.50 Key Points: Process-Specific Costs: The cost of each manufacturing process depends on factors like complexity, material thickness, and equipment used. Labor Costs: The cost of labor, especially for skilled trades like welding and machining, can significantly impact the overall cost. Overhead Costs: Overhead costs (e.g., facility rent, utilities) should also be considered, as they contribute to the final product cost.

Many FP&A teams forecast compensation using top-down assumptions like "salaries grow 3% year-over-year and benefits are 25% of pay." But this usually fails. Bottoms-up cost builds allow FP&A professionals to build accurate compensation models like this one. Instead of starting with high-level assumptions and averages, it begins with inputs that can then drive the averages used in the financial model. This is an example I sometimes use to illustrate how FP&A teams can build more accurate payroll forecasts: • Separate senior professionals from junior professionals • Build salary growth rates at the category level • Add fringe and statutory costs line by line • Calculate each cost as a % or salaries or per person • Include benefits % of salary to capture non-cash comp The result of this technique is you get a transparent, auditable model with inputs that can be easily flexed. You get immediate sensitivities that you can run on headcount, pay mix, or changes to benefits. And you can easily integrate these assumptions with workforce planning. You can also break down leadership, management, and staff by job category and assign salary bands. If the CFO asks why personnel costs went up 8%, you can show exactly where that increase is coming from. A bottoms-up cost build like this doesn't just make your forecast more detailed. It makes it more defensible for FP&A business partners serving human resources.

Neural Networks can make predictions that violate basic physics or laws of thermodynamics if aimed only at minimizing a loss function. To fix this issue, ML scientists introduced PINNs - Physics Informed Neural Networks - where you penalize a neural network when it makes physically nonsense predictions. But what if you don’t know the full physics of a system? How do you penalize the neural network in that case? Universal Differential Equations (UDE) is the answer. I am writing this article in praise of this marvelous technique that is truly changing the way we are looking at how to bring science and ML together. Even a popular domain is emerging as a result: Scientific Machine Learning (SciML). Let us look at a spring-mass-damper system - a classic example in physics and engineering. Usually, it goes like this: mx''+bx'+kx=0 In a perfect world, these parameters m, c, k would be constants we measure in a lab. But in real life, your damper might behave non-linearly. So you may not know what the damping force is. That is where we can bring Universal Differential Equations into the picture. Instead of blindly trusting a neural network or strictly forcing your physical laws down the model’s throat, you merge them. In short, a UDE says: “I know some of the physics. Let me put that in. The rest that I don’t know? That’s the chunk I will replace with a neural network.” So how do we do it with the spring–mass–damper? A hybrid model: Part physics, part neural network. We know there is a second-order ODE term to account for acceleration and a ‘kx’ term for spring force. However, suppose, we suspect the damping force is not the usual linear form. Maybe it is more complicated, or partially unknown. mx''+kx+[unknown]=0 Now the “something unknown” becomes a learned function modeled by a neural network NN(θ). [unknown] = NN(θ) If you suspect a hidden/unknown effect, you can funnel that knowledge gap straight into the neural network term. Note that here the neural network is predicting the damping term. We want to predict displacement x(t). What does the UDE predict? The “neural network” alone is not the UDE. Because the UDE has to predict x(t) so that you can compare the predicted x(t) with experimental x(t) and define the loss. So how exactly does UDE predict x(t)? 1) Initial condition and experimental data fed to NN(θ) 2) Neural Network NN(θ) for the unknown term predicts damping 3) Combine with the known ODE: mx''+kx+NN(θ)=0 4) Numerical integration to predict x and x' 5) Compare predictions to experimental data 6) Back-propagation and optimization till you minimize the loss You have the final UDE model. I have made a lecture video on UDEs (for absolute beginners) on Vizuara’s YouTube channel. Do check this out. I hope you enjoy watching this lecture as much as I enjoyed making it: https://lnkd.in/gPWQuXHR

𝐖𝐡𝐚𝐭 𝐝𝐨𝐞𝐬 𝐚 100 𝐌𝐖 𝐮𝐭𝐢𝐥𝐢𝐭𝐲-𝐬𝐜𝐚𝐥𝐞 𝐬𝐨𝐥𝐚𝐫 𝐏𝐕 𝐩𝐫𝐨𝐣𝐞𝐜𝐭 𝐫𝐞𝐚𝐥𝐥𝐲 𝐜𝐨𝐬𝐭? In reality, a solar project is a stack of interdependent cost blocks, each carrying execution and bankability risk. 𝐇𝐞𝐫𝐞’𝐬 𝐡𝐨𝐰 𝐚 100 𝐌𝐖 𝐬𝐨𝐥𝐚𝐫 𝐏𝐕 𝐩𝐫𝐨𝐣𝐞𝐜𝐭 (~𝐔𝐒𝐃 95 𝐦𝐢𝐥𝐥𝐢𝐨𝐧 𝐭𝐨𝐭𝐚𝐥 𝐂𝐀𝐏𝐄𝐗 𝐢𝐧 𝐭𝐡𝐞 𝐔𝐒) 𝐭𝐲𝐩𝐢𝐜𝐚𝐥𝐥𝐲 𝐛𝐫𝐞𝐚𝐤𝐬 𝐝𝐨𝐰𝐧 👇 ▶ 𝐎𝐯𝐞𝐫𝐚𝐥𝐥 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐕𝐚𝐥𝐮𝐞 (𝐔𝐒𝐀 – 𝐁𝐞𝐧𝐜𝐡𝐦𝐚𝐫𝐤 𝐂𝐚𝐬𝐞) • Total EPC CAPEX: ~USD 95 million (~USD 0.95/W) • Covers development to commissioning and COD • Excludes owner-side financing & long-term O&M costs ▶ 𝐌𝐚𝐣𝐨𝐫 𝐂𝐨𝐬𝐭 𝐂𝐨𝐦𝐩𝐨𝐧𝐞𝐧𝐭𝐬 – 𝐖𝐡𝐞𝐫𝐞 𝐭𝐡𝐞 ��𝐨𝐧𝐞𝐲 𝐑𝐞𝐚𝐥𝐥𝐲 𝐆𝐨𝐞𝐬 • Solar PV Modules: ~USD 28.0M (~30%) • Mounting Structures / Trackers: ~USD 11.0M (~12%) • Inverters: ~USD 6.5M (~7%) • DC + AC BOS & Electrical Systems: ~USD 14.5M (~15%) • Civil & Site Works: ~USD 5.5M (~6%) • Grid Interconnection & Substation: ~USD 7.0M (~7%) • Construction & Installation Labor: ~USD 6.5M (~7%) ▶ 𝐎𝐟𝐭𝐞𝐧 𝐎𝐯𝐞𝐫𝐥𝐨𝐨𝐤𝐞𝐝 — 𝐁𝐮𝐭 𝐂𝐫𝐢𝐭𝐢𝐜𝐚𝐥 𝐟𝐨𝐫 𝐁𝐚𝐧𝐤𝐚𝐛𝐢𝐥𝐢𝐭𝐲 • Project development & land control: ~USD 2.0M • Engineering, permitting & studies: ~USD 2.5M • EPC management, HSE & QA/QC: ~USD 2.5M • Testing, commissioning & COD activities: ~USD 1.2M • Insurance, contingency & spares: ~USD 4.8M 𝐊𝐞𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲 A solar project’s success is not defined by module pricing alone — it’s defined by how well development, engineering, grid integration, construction, and risk buffers are structured into the overall project valuation. Visit 👉 https://alendei.energy/ or connect with us for solar and Bess EPC, investment and IPP. Alendei from Bharat Alendei Green RE Pvt. Ltd. #RenewableEnergy #SolarEnergy #WindEnergy #CleanTech #IPP #UtilityScaleSolar #OnshoreWind #ClimateTech #EnergyTransition #NetZero #TataPowerRenewables #Suzlon #InoxWind #JSWEnergy #NTPC #SECI #LarsenAndToubro #ACWAPower #Masdar #DEWA #EWEC #NEOM #AmeaPower #AlFanar #CEPCO #SaudiEnergy #UAEEnergy #LekelaPower #Globeleq #AfreximBank #KenGen #Eskom #ZESCO #AfricaIPP #NextEra #Invenergy #PatternEnergy #Enbridge #BrookfieldRenewables #AES #EDFrenewables #HydroOne #DominionEnergy #TCenergy #Vestas #SiemensGamesa #GErenewables #Nordex #FirstSolar #TrinaSolar #CanadianSolar #JinkoSolar #JAsolar #SolarEPC #WindEPC #NextEraEnergy #Invenergy #AESCorporation #PatternEnergy #DominionEnergy #NRG #DukeEnergy #Exelon #Enbridge #BrookfieldRenewables #AlgonquinPower #HydroOne #OntarioPowerGeneration #EDFrenewables #EDPRenewables #ShellRenewables #BPAlternativeEnergy #ClearwayEnergy #ApexCleanEnergy #ArrayTechnologies #Nextracker #FluorEnergy #BechtelEPC #BlackAndVeatch #BurnsAndMcDonnell #RESAmericas #VestasAmericas #GErenewables #SiemensGamesa #NordexAcciona #SungrowAmerica #TeslaEnergy #LGenergySolution #EatonEnergy #ABBPowerGrids #OmegaEnergia #AtlasRenewableEnergy #Neoenergia #Energisa #CPFLenergia #AesBrasil #AccionaEnergia

Reducing Manufacturing Costs with GD&T: A Game-Changer for Engineers In the world of manufacturing, reducing costs without compromising quality is a constant challenge. One powerful tool that bridges the gap between design intent and cost efficiency is Geometric Dimensioning and Tolerancing (GD&T). Here's how GD&T helps reduce manufacturing costs: 1. Clear Communication: GD&T provides precise definitions of design requirements, eliminating ambiguity in engineering drawings. This ensures that all teams — from design to manufacturing — are aligned, reducing errors and rework. 2. Reduced Tolerance Stacking: By controlling geometric tolerances instead of relying solely on linear dimensions, GD&T minimizes overly tight tolerances. This reduces material waste, machining time, and inspection complexity, all of which lower costs. 3. Optimized Inspection: GD&T allows for easier and faster inspection using advanced tools like Coordinate Measuring Machines (CMM). This reduces the inspection cycle time and ensures products meet requirements without excessive testing. 4. Improved Assembly: Parts designed with GD&T fit together correctly the first time, reducing assembly issues and costly adjustments during production. 5. Flexibility in Manufacturing: GD&T allows manufacturers to use alternative processes or machines as long as they meet the geometric requirements. This flexibility leads to cost savings by utilizing available resources effectively. Why It Matters Incorporating GD&T into your design process isn’t just about technical precision; it’s about delivering cost-effective, high-quality products. For industries like aerospace, automotive, and medical devices, where precision is critical, GD&T is a competitive advantage. Are you leveraging GD&T in your processes? Share your experience or challenges in implementing it! Let’s discuss how we can use this tool to drive efficiency and innovation in manufacturing.

⏰ How To Improve Your Time Estimates (https://lnkd.in/egWd45RF), an honest article of lessons learned from going massively over on a fixed-price contract — with action points on what our estimates typically miss, how to estimate better and how to be prepared when things go sideways. By Dave Stewart. ✅ “Planned work” may be as little as 20% of the total project effort. ✅ “Extra work” increases proportionally to the complexity of the work. ✅ Account for changes (20%) and unexpected slowdowns (15%). ✅ Access to data, docs, tools, people is a huge estimate trap. ✅ Run postmortems on past projects to anchor yourself to reality. ✅ Estimate with at most 6–6.5 productive hours per day. ✅ Always estimate in ranges, and never in precise numbers. ✅ Safe way to estimate better is to estimate smaller units of work. ✅ Always add at least 15–20% of buffer time: you will need them. ✅ Every new team member speeds up the work by 1.5–1.8×. 🚫 Troubles start when designers aren’t involved in estimates. 🚫 Stakeholders rarely know what causes delays and extra costs. ✅ Re-iterate that late changes are expensive and cause delays. ✅ Life is full of surprises: budget too much, not too little. ✅ When in trouble, raise a hand, rather than doubling down. As Dave has rightfully noted, much of the work we do is actually happening “around the work” — on the fringes of the project, before, between and beyond actual design work. It covers everything, from daily routine tasks (emails, meetings, reports) to complex dependencies, unknowns and legacy limitations. In the past, I was always trying to underpromise and overdeliver. I was thinking that ultimately that would put me in a good light — appearing as accountable, reliable and committed to quality work, despite the initial scope. Yet it has also resulted in poor estimates, delays, late night work and overlapping projects. So instead, I started dedicating time into drafting a very detailed scope of work to estimate better. Typically it includes: 1. That’s how we understood the problem, 2. That’s what we believe the solution requires, 3. That’s the breakdown of tasks we’ll do, 4. That’s the assumptions we make, 5. That’s dependencies we uncovered, 6. That’s data, docs, tools, people need to be involved, 7. That’s how we are planning to solve it, 8. That’s when stakeholder’s (timely) input will be needed, 9. That’s milestones and timelines we commit to, 10. That’s the fixed scope of our final delivery, 11. That’s the delivery date we commit to, 12. That’s how pricing and payment will work, 13 That’s how we’ll deal with late adjustments and scope changes. And most importantly: for every step of the process — in emails, calls, meetings — make sure to mention that late scope changes are very expensive and will eventually cause delays. So ask for the best channels and frequency for communication with stakeholders. Chances are high that you will need it. #ux #design

Most revenue models are built backwards. Finance picks a number. Sales divides by average quota. You end up with something like: “We need $40M, our quota is $1M per rep, so let’s hire 40 reps.” It looks tidy in a spreadsheet...and it almost never works in the real world. :) Why? Well, because this model assumes every rep: - Ramps on time - Hits 100% - Stays the full year Which is like assuming every Uber driver wins the Indy 500. Here’s a better way to build a revenue model: First off, stop treating quota as a fixed assumption, and start building around ramped capacity, rep variability, and reality. 1. Plan using RRE, not headcount. RRE = Ramp-Weighted Ramped Equivalents Forget how many reps you have. Focus on how many fully productive equivalents you’ll actually have in a given quarter. This accounts for: - Ramp time. - Attrition. - Variance in performance bands. That new rep you just hired? They're not a "1" in your model. They're a 0.2, then 0.4, then maybe 0.7 if you're lucky. Ten reps with half still ramping = 6.5 RREs. Not 10. 2. Build top down and bottom up...then reconcile. Top down: What makes the VCs happy? Bottom up: What's actually possible given productivity curves? When these numbers don't match (spoiler: they won't), you've found your strategic tension point. 3. Layer in performance bands. Not all reps hit quota. And that’s not failure. That’s just math. Try modeling based on realistic performance distribution: - Top 20% hit 120-150% - Middle 60% hit 70-90% - Bottom 20% hit 0-50% If your plan assumes everyone hits 100%, you’re either new here… ...or about to be. 😬 4. Bake in operational drag. Every revenue model looks clean...until enablement stalls, marketing underdelivers, or a region goes sideways. So you should build in a drag factor: - Deal slippage. - Hiring delays. - Funnel softness. - Internal execution risk. Don’t present worst case scenarios, but do plan for them. Some revenue leaders treat quota like a scoreboard, whereas you should treat it like an operating system. Don’t ask: “How many reps do we need to hit $40M?” Instead, ask: “How do we engineer the system to consistently produce $40M - with margin for error?” That’s the difference between running a sales org and running a revenue machine.

Are you aware of the hidden costs in your product's raw material? : : Accurately calculating raw material costs is a cornerstone of should-cost modeling. By effectively identifying the materials required, determining the cost per unit, and accounting for potential waste and additional costs like handling and transportation, you can develop a comprehensive and reliable cost model. Key Parameters for Should Cost Process in Material Calculation: # Raw Material Identification: ·  Material type and grade ·  Material source/origin # Material Quantity: · Required quantity (per unit or batch) · Packaging units # Material Cost per Unit: · Supplier quotes · Market prices · Historical data · Discounts and bulk pricing # Material Waste or Loss: · Scrap/waste factor ·  Defects and rejections # Handling and Storage Costs: ·  Material handling · Storage costs (rent, insurance, utilities) · Inventory management # Freight and Transportation: ·  Shipping costs · Delivery method (air, sea, road) ·  Customs and tariffs # Lead Time and Order Frequency: · Lead time variations · Order volume # Supplier Terms and Conditions: · Payment terms · Return and warranty policies · Exchange Rates (For Imported Materials) # Material Substitution and Alternatives: · Substitute materials ·  Material optimization # Environmental and Regulatory Factors: · Recycling or sustainability initiatives · Regulatory compliance # Operational Overheads Related to Materials: · Processing costs · Energy costs ------------------------------------------------------------------------------------- # Ask Yourself: -> Did you consider the net weight and gross weight calculation properly? -> Did you consider scrap weight and scrap cost in your estimation? -> Do you have access to the global raw material index and recent material price database? -> Have you asked your supplier about the raw material cost per kg as well as the scrap cost per kg? -> Do you consider Manufacturing overhead (MOH) and inventory cost (raw materials)? -> What about the scrap cost percentage based on different commodities? -> Did you optimize material through strip layout, nesting, cavity, and other techniques? -> What’s your strategy when the supplier asks for material cost increases due to market fluctuations? -> Did you consider the volume/batch/MOQ impact, as well as regional cost impact, in your calculations? -> Did you consider any coating and primary requirements in the raw material stage? -> Commodity-Specific Considerations, etc.

CAPEX estimation for low maturity technology projects is challenging, particularly when we talk about new equipment. Yet, we still need to be able to get fairly accurate figures to justify the viability of the technology and secure funding for its development. How to do it? Here is what we usually do for hydrogen and carbon capture projects. 1. Define the Project Scope Start by clearly outlining all project boundary, objectives and deliverables. Identify every cost elements required for full scale implementation, from engineering and design to construction and commissioning, while distinguishing between one-off investments and those that can be standardised. 2. Develop the first-of-a-kind CAPEX Estimate • Detailed Bottom-Up Analysis: Break down the project into its individual components, accounting for bespoke engineering, pilot testing, specialized installations, and comprehensive project management. • Risk and Contingency: Due to the innovative nature and inherent uncertainties of FOAK projects, incorporate generous contingencies to cover design modifications, unforeseen challenges, and regulatory uncertainties. • Documentation: Maintain thorough records of assumptions and decisions made during this phase, as these will inform future projects. 3. Estimate to the nth-of-a-kind estimate with learning curves Leverage the insights from the FOAK phase to isolate repeatable cost elements. With each subsequent build, learning curves drive efficiencies: • Standardize Processes: As you replicate the project, streamline designs and processes. • Realize Efficiency Gains: Experience leads to better vendor relationships and operational refinements, translating into significant cost reductions for repeatable components. • Adjust Estimates: Update your cost models to reflect these improvements, using your own or reported learning curves, ensuring more accurate and lower capital expenditure projections for future projects. 4. Implement Continuous Improvement Regularly revisit and refine both FOAK and NOAK estimates. As more operational data becomes available, adjust your assumptions and conduct sensitivity analyses to maintain a robust, realistic capex projection. How do you estimate CAPEX for your technology? #Innovation #research #hydrogen #carboncapture #science #scientist #chemicalengineering

6 months into a $20,000,000 Bel Air spec home. The build was already $2,000,000 over budget. That’s when I was called. On paper, the culprit looked like “change orders.” In reality, there were 2 issues: 1. Finishes and fixtures had never been defined. 2. There was no contingency in the budget. Every time a higher-end material was chosen, it triggered a new “change order.” What looked like scope changes were really just undefined scope. This is where first-time developers get burned. Not all change orders are equal. Some are legitimate. Some are profit padding. Here are 3 ways to tell the difference: 1. Trace it back to the drawings - If the work was clearly shown in the plans or specs, it’s not a change order. It’s the contractor’s responsibility. Legitimate change orders usually arise when something wasn’t on the drawings, or the drawings conflict. 2. Ask: Scope Expansion or Scope Clarification? - Did the contractor “discover” something you assumed was included (blocking, fire caulking, waterproofing tie-ins)? That’s scope clarification = red flag. - Did you add new work (like a skylight or additional bathroom)? That’s scope expansion = legitimate. 3. Check the Pricing Against Market Reality - A $10,000 line item for 50 feet of conduit? Call another sub. Quick benchmarking protects you from padded numbers. Real change orders will hold up under outside pricing pressure. Change orders aren’t the enemy. They’re a tool to adjust when conditions legitimately shift. But as an investor or first-time developer, your job is to know which ones are real, and which ones are avoidable costs hidden in plain sight. P.S. Have you ever been hit with a questionable change order?