Performance Baseline Analysis

WHAT MAKES A BASELINE CREDIBLE (AND WHY MOST FAIL) There is plenty of discussion about float, consent, ownership and delay methodology. But none of it matters if the baseline is not credible. The baseline is the foundation of project control. If it is weak, everything that sits on top of it becomes unstable. So what actually makes a baseline schedule credible? Every major authority gives the same answer. AACE International, the SCL Protocol, PMI standards, CIArb guidance and leading case law all point to a single standard: A credible baseline is a realistic, buildable and defensible plan that can support live project decisions and withstand scrutiny. This is the world class benchmark: 1. Coherent logic A properly linked network that reflects the actual sequence of construction. 2. A defensible critical path Stable, traceable and based on means and methods, not adjusted to suit dates. 3. Transparent float Float visible and aligned to risk and interfaces, not hidden or manipulated. 4. Realistic durations Durations supported by productivity, crew size, access and physical constraints. 5. Resources aligned with the plan Manpower and equipment that can actually deliver the logic. 6. Integrated design and procurement Approvals, long lead items and fabrication embedded correctly. 7. Contractual alignment Milestones, constraints, access points and sectional completions represented accurately. 8. Risk exposure shown Logic that reflects real vulnerabilities, not a perfect theoretical flow. 9. Update-ready structure A programme that can be monitored, updated and analysed objectively. 10. Strength under interrogation A baseline that survives challenge from the Engineer, independent experts and tribunals. Why do most baselines fail? Because they fall into the same predictable pitfalls such as: Manufactured critical paths. Aspirational durations. Hidden float. Weak logic. No procurement integration. Resource curves disconnected from reality. Contractual obligations not embedded. Stacked trades that cannot physically work together. Optimistic calendars. Tender-programme thinking. Schedules built for consent rather than delivery. When these weaknesses exist, the baseline collapses at the moment you need it most. Progress becomes unclear. Delay analysis becomes unstable. Claims become harder to support. Disputes become inevitable. The solution is not new concepts or terminology. It is a credible baseline at the start. Everything else grows from that.

We are happy to share the results of our exhaustive benchmarking study on forecasting models, where we assessed 87 models across 24 varied datasets. This project aimed to evaluate the performance of univariate forecasting models ranging from naive baselines to sophisticated neural networks, using a comprehensive set of metrics such as RMSE, RMSSE, MAE, MASE, sMAPE, WAPE, and R-squared. The 24 datasets contained a wide range of frequencies, including hourly (4 datasets), daily (5), weekly (2), and monthly (4), quarterly (2), yearly (3). Additionally, there are 4 synthetic datasets without a specific frequency. Some of the datasets also contain covariates (exogenous features) of static, past, and/or future nature. For each model, we aimed to identify hyperparameters that were effective on a global level, across all datasets. Dataset specific hyperpameter tuning for each model was not performed due to budget constraints on this project. We use a simple train/test split along the temporal dimension, ensuring models are trained on historical data and assessed on unseen future data. The attached chart shows a heatmap of the average RMSSE scores for each model, grouped by dataset frequency. The results are filtered to 43 models for brevity, excluding noticeably inferior models and redundant implementations. RMSSE is a scaled version of RMSE, where a model's RMSE score is divided by the RMSE of a naive model. With RMSSE, the lower the score, the better the model's performance. A score of 1.0 indicates performance on par with the naive baseline. Key Findings: - Machine-Learning Dominance: Extra trees and random forest models demonstrate the best overall performance. - Neural Network Success: Variational Encoder, PatchTST, and MLP emerged as top neural network models, with Variational Encoder showing the best results, notably including pretraining on synthetic data. - Efficacy of Simplicity: DLinear and Ridge regression models show strong performance, highlighting efficiency in specific contexts. - Statistical Models' Relevance: TBATS stands out among statistical models for its forecasting accuracy. - Yearly Datasets Insight: On yearly datasets, none of the advanced models surpassed the performance of the naive mean model, highlighting the difficulty of forecasting with datasets that lack conspicuous seasonal patterns. - Pretraining Advantage: The improvement in models like Variational Encoder and NBeats through pretraining on synthetic data suggests a promising avenue for enhancing neural networks' forecasting abilities. All models and datasets are open-source. For a detailed examination of models, datasets, and scores, visit https://lnkd.in/d6mMSudJ. Registration is free, requiring only your email. Our platform is open to anyone interested in benchmarking their models. Any feedback or questions are welcome. Let's raise the state of the art in forecasting!

Are You Truly Measuring Energy Savings Scientifically? In any ISO 50001-compliant Energy Management System (EnMS), Establishing an Energy Baseline (EnB) and selecting Energy Performance Indicators (EnPIs) are the absolute foundation. Without them, you cannot reliably prove energy savings or demonstrate continuous improvement. Let us see clear breakdown of these critical steps: 🔹 1. Establishing the Energy Baseline (EnB) The EnB is your quantitative reference point: "How much energy would we have used today if no improvements had been made?" Data Collection: Gather at least 12 months of historical data (energy consumption + relevant variables like production volume, degree days) to capture seasonality. Normalization: Avoid simple static baselines (e.g., last year’s total). Identify and account for key drivers (weather, output levels) that significantly affect consumption. Regression Analysis (Best Practice): Use linear or multivariable regression to build a model (e.g., y = mx + c). This lets you calculate expected vs. actual energy use under current conditions. 🔹 2. Selecting Energy Performance Indicators (EnPIs) EnPIs should be hierarchical — from facility-wide down to specific equipment ,and focus on efficiency, not just total consumption. A. High-Level (Facility-Wide) Energy Use Intensity (EUI): Total energy ÷ floor area (kWh/m²/yr) — ideal for buildings. Energy Intensity (EI): Total energy ÷ production output (e.g., kWh/unit) , standard in manufacturing. B. System & Equipment Level (Significant Energy Users) Chillers: kW/ton or COP Boilers: Combustion efficiency (%) or steam intensity Compressed Air: Specific power (kW/100 cfm) C. Productivity Metrics Link energy to value: kWh/kg of product or energy cost per unit sold. The Process in a Nutshell Identify Significant Energy Users (SEUs) Determine key driving variables Build the EnB using regression on historical data Choose EnPIs that track true efficiency Getting these steps right turns energy management from guesswork into data-driven success. And a final question for energy managers, sustainability leaders, and facility engineers: what has your experience been with baselines and EnPIs? Have you encountered common pitfalls, or found go‑to tools, for regression analysis? If you have a question, insight, or story to share, feel free to comment. #EnergyManagement #ISO50001 #EnergyEfficiency #Sustainability #EnMS #EnergyPerformance #NetZero

When you start building an ML model, you almost always want to begin with a baseline. But why does it matter so much? Here are 4 key reasons: 1. Deliver user value fast. Even a simple baseline can provide immediate value. 2. Test your pipeline. It’s a sanity check that all the system parts work together as expected. 3. Benchmark progress. The baseline becomes your reference point to measure improvements and evaluate the ROI of investing in more complex models. 4. De-risk early. You minimize risk with the lowest cost, time, and effort while still moving the product forward. 🔄 How baselines evolve: Constant → Rule-based → Linear models 👉 Always start with the simplest: a constant baseline. For regression tasks, that could be: -> the mean or median -> last available value -> first available value -> a quantile -> …or any simple statistic that makes sense for your data and business. Only increase complexity when the baseline no longer serves your needs. Baselines aren’t just “toy models.” They’re your safety net, your test harness, and your first step toward reliable ML systems.

You just spent 45 minutes hunting for the right baseline. Again. P6's baseline button is easy to click. Organizing what comes after? That's where schedulers lose hours every week. I've watched project teams waste entire afternoons tracking down the correct baseline for a delay analysis. Here's a couple of my favorite Primavera P6 baseline strategies. Set Up Your System Before You Need It Organization beats speed every single time. When delays hit or scope changes pile up, you won't have time to figure out where Baseline_Final_v3_ACTUAL is hiding. I keep three copies of every baseline. One on a shared drive outside P6 as a failsafe. One standalone project in P6 under a dedicated EPS called "_BASELINES". And one assigned directly to my latest progress update for instant variance comparisons. Name Everything Like You'll Forget Everything Because you will. Use a system that makes sense six months from now when you're building a delay claim at 2am. Example: ProjectName_Baseline_ContractRebaseline_YYYYMMDD And: ProjectName_Update_YYYYMMDD Simple. Consistent. Searchable. Decide Your Rebaseline Strategy Now Not when the owner demands it. There are multiple approaches. Retained logic vs progress override. Maintaining vs resetting actuals. Keeping vs scrapping out of sequence work. Each method tells a different story about what happened on your project. Pick one approach and document why before anyone asks. Assign Previous Updates for Instant Analysis Most people compare current schedule to original baseline. That's useful. But comparing current schedule to last month's update? That's where you catch scope creep, logic changes, and estimate updates before they become problems. I keep at least three progress snapshots assigned: baseline and two previous months. Use P6's Restore Feature for Forensic Work This is where baselines become gold mines. P6 lets you restore any baseline as a full project. Not just for lookback. For actual analysis. Rebuild Time Impact Analyses from the exact schedule state when a change order hit. Prove delay responsibility by showing the critical path the day the RFI sat unanswered for three weeks. Extract proven work sequences from past projects and drop them into new schedules. Your baselines become a library of what actually works. Bottom Line P6 baseline management isn't about complexity. It's about organization that saves you hours when deadlines are tight and everyone needs answers yesterday. Master this and you'll spend less time hunting for files and more time actually managing the schedule. What strategies am I missing? ♻️ Repost to help a scheduler improve their baseline strategy.

Most arguments and disagreements in analytics are really baseline arguments: 🔹 Is this lift meaningful? 🔹 Is this drop alarming, or just noise? 🔹 What do you do when you don’t have historical data? 🔹 What are you actually measuring performance against? Your Forecast? A competitor benchmark? Last year? A baseline is the reference point that defines what normal looks like. Without a baseline, you don’t know what is “good” or “bad”. What should this metric look like if nothing unusual is happening? Ironically, even strong teams often don’t define baselines explicitly. They store multiple variations in spreadsheets or dashboards, let them drift without versioning, and end up with conflicting stakeholder interpretations. And yet, baselines are the foundation of forecasting, experimentation, alerting, performance reviews, pricing decisions, and more. You can’t measure change without first defining normal. For B2B and SaaS: - Forecast-driven baselines usually work  - Stable sales cycles + historical trends = predictable reference points. For B2C / Subscription / Ads, where growth is driven by marketing spend shifts, channel mix experiments, platform algorithms, or funnel rebuilds, forecast-only baselines are likely to fail. Read below my breakdown of how to calculate baselines for new or fast-growing products.

𝐇𝐨𝐰 𝐭𝐨 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐞 𝐋𝐋𝐌𝐬: 𝐇𝐮𝐦𝐚𝐧𝐄𝐯𝐚𝐥, 𝐌𝐌𝐋𝐔, 𝐓𝐫𝐮𝐭𝐡𝐟𝐮𝐥𝐐𝐀 High benchmark scores don’t guarantee real-world reliability. A model scoring 90% may excel at the test but fail in production: hallucinations, brittle reasoning, silent failures, and performance drops under distribution shifts are common. 𝐊𝐞𝐲 𝐁𝐞𝐧𝐜𝐡𝐦𝐚𝐫𝐤𝐬 1. 𝐇𝐮𝐦𝐚𝐧𝐄𝐯𝐚𝐥 – 𝐂𝐨𝐝𝐞 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 -Tests small, self-contained functions. Measures syntax, instruction following, pattern completion. -Does not test multi-file reasoning, debugging, system-level engineering, or long-term consistency. 2. 𝐌𝐌𝐋𝐔 – 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐁𝐫𝐞𝐚𝐝𝐭𝐡 -Covers 50+ academic domains. Tests recall and structured reasoning. -Fails to capture messy real-world prompts, multi-step tool usage, self-correction, and adaptive intelligence. 3. 𝐓𝐫𝐮𝐭𝐡𝐟𝐮𝐥𝐐𝐀 – 𝐇𝐚𝐥𝐥𝐮𝐜𝐢𝐧𝐚𝐭𝐢𝐨𝐧 𝐑𝐞𝐬𝐢𝐬𝐭𝐚𝐧𝐜𝐞 -Checks if models avoid repeating common misconceptions. Surfaces factual alignment issues. -Still misses dynamic hallucinations like fake citations, invented APIs, and subtle errors in reasoning. 𝐓𝐡𝐞 𝐁𝐢𝐠𝐠𝐞𝐫 𝐏𝐫𝐨𝐛𝐥𝐞𝐦 Benchmarks assume IID data. Production is non-IID: user prompts drift, context length increases, domains mix, tools fail. Leaderboard leaders often fail in real pipelines. 𝐒𝐞𝐧𝐢𝐨𝐫 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐞 𝐚𝐠𝐚𝐢𝐧𝐬𝐭 𝐲𝐨𝐮𝐫 𝐟𝐚𝐢𝐥𝐮𝐫𝐞 𝐦𝐨𝐝𝐞𝐬, 𝐧𝐨𝐭 𝐥𝐞𝐚𝐝𝐞𝐫𝐛𝐨𝐚𝐫𝐝 𝐬𝐜𝐨𝐫𝐞𝐬: -Offline benchmarks for baseline ability -Domain-specific data tests -Adversarial and ambiguous prompt tests -Long-context and multi-step workflows -Tool integration and consistency checks -Latency-cost tradeoffs 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲: Benchmarks are useful signals. Real intelligence in production is measured by reliability, adaptability, and context-aware performance, not scores. Follow Karthik Chakravarthy for more insights

Most of us forced to optimise campaigns like intraday traders. Open Ads Manager. Look at last 7 days. React. That’s how you go nuts. But, multi-baseline performance is sanity. Today vs last 7 days. vs last 14. vs last 28. One view. You instantly see: Seasonality Day of week swings Whether today is actually bad, or just worse than yesterday but fine vs last month That’s the difference between an intraday trader and a portfolio manager. The trader panics on every red candle. The portfolio manager looks at trend, not noise. Multi-baseline gives you that portfolio view for your ad account. But here’s the catch: Even a beautiful multi-baseline screen is useless if it doesn’t connect to business objectives. I still need to know: How is new customer acquisition doing vs baselines What about retention / repeat Are my high AOV products holding up Which campaigns are aligned to actual business goals, not just cheap clicks So yes, multi-baseline is non-negotiable. It stops emotional decisions and exposes seasonality clearly. But the real power is: Multi-baseline view granular breakdown by business objective. That’s where media buying starts to look like real portfolio management, not just fancy refresh of last 7 days.