Evaluating Project Performance Metrics

𝗧𝗼𝗱𝗮𝘆, 𝗣𝗠𝗜 𝗿𝗲𝗹𝗲𝗮𝘀𝗲𝘀 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝗿𝗲𝘀𝘂𝗹𝘁𝘀 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗹𝗮𝗿𝗴𝗲𝘀𝘁 𝘀𝘁𝘂𝗱𝘆 𝘄𝗲’𝘃𝗲 𝗲𝘃𝗲𝗿 𝗰𝗼𝗻𝗱𝘂𝗰𝘁𝗲𝗱 - 𝗼𝗻 𝗮 𝘁𝗼𝗽𝗶𝗰 𝘁𝗵𝗮𝘁 𝗶𝘀 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝘁𝗼 𝗼𝘂𝗿 𝗽𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻: 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗦𝘂𝗰𝗰𝗲𝘀𝘀. 📚 Read the report: https://lnkd.in/ekRmSj_h With this report, we are introducing a simple and scalable way to measure project success. A successful project is one that 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝘀 𝘃𝗮𝗹𝘂𝗲 𝘄𝗼𝗿𝘁𝗵 𝘁𝗵𝗲 𝗲𝗳𝗳𝗼𝗿𝘁 𝗮𝗻𝗱 𝗲𝘅𝗽𝗲𝗻𝘀𝗲, as perceived by key stakeholders. This clearly represents a shift for our profession, where beyond execution excellence we also feel accountable for doing anything in our power to improve the impact of our work and the value it generates at large. The implications for project professionals can be summarized in a framework for delivering 𝗠𝗢𝗥𝗘 success: 📚𝗠anage Perceptions For a project to be considered successful, the key stakeholders - customers, executives, or others - must perceive that the project’s outcomes provide sufficient value relative to the perceived investment of resources. 📚𝗢wn Project Success beyond Project Management Success Project professionals need to take any opportunity to move beyond literal mandates and feel accountable for improving outcomes while minimizing waste. 📚𝗥elentlessly Reassess Project Parameters Project professionals need to recognize the reality of inevitable and ongoing change, and continuously, in collaboration with stakeholders, reassess the perception of value and adjust plans. 📚𝗘xpand Perspective All projects have impacts beyond just the scope of the project itself. Even if we do not control all parameters, we must consider the broader picture and how the project fits within the larger business, goals, or objectives of the enterprise, and ultimately, our world. I believe executives will be excited about this work. It highlights the value project professionals can bring to their organizations and clarifies the vital role they play in driving transformation, delivering business results, and positively impacting the world. The shift in mindset will encourage project professionals to consider the perceptions of all stakeholders- not just the c-suite, but also customers and communities. To deliver more successful projects, business leaders must create environments that empower project professionals. They need to involve them in defining - and continuously reassessing and challenging - project value. Leverage their expertise. Invest in their work. And hold them accountable for contributing to maximize the perception of project value at all phases of the project - beyond excellence in execution. 📚 Please read the report, reflect on its findings, and share it broadly. And comment! Project Management Institute #ProjectSuccess #PMI #Leadership #ProjectManagementToday

How to compare your eng team's velocity to industry benchmarks (and increase it): Step 1: Send your eng team this 4-question survey to get a baseline on key metrics: https://lnkd.in/gQGfApx4 You can use any surveying tool to do this—Google Forms, Microsoft Forms, Typeform, etc.—just make sure you can view the responses in a spreadsheet in order to calculate averages. Important: responses must be anonymous to preserve trust, and this survey is designed for people who write code as part of their job. Step 2: Calculate your how you're doing. - For Speed, Quality, and Impact, find the average value for each question’s responses. - For Effectiveness, calculate the percent of favorable responses (also called a Top 2 Box score) across all Effectiveness responses. See the example in the template above. Step 3: Track velocity improvements over time. Once you’ve got a baseline, you can start to regularly re-run this survey to track your progress. Use a quarterly cadence to begin with. Benchmarking data, both internal and external, will help contextualize your results. Remember, speed is only relative to your competition. Below are external benchmarks for the key metrics. You can also download full benchmarking data, including segments on company size, sector, and even benchmarks for mobile engineers here: https://lnkd.in/gBJzCdTg Look at 75th percentile values for comparison initially. Being a top-quartile performer is a solid goal for any development team. Step 4: Decide which area to improve first. Look at your data and using benchmarking data as a reference point, pick which metric you believe will make the biggest impact on velocity. To make this decision about what to work on to improve product velocity, drill down to the data on a team level, and also look at qualitative data from the engineers themselves. Step 5: Link efficiency improvements to core business impact metrics Instead of presenting these CI and release improvement projects as “tech debt repayment” or “workflow improvements” without clear goals and outcomes, you can directly link efficiency projects back to core business impact metrics. Ongoing research (https://lnkd.in/grHQNtSA) continues to show a correlation between developer experience and efficiency, looking at data from 40,000 developers across 800 organizations. Improving the Effectiveness score (DXI) by one point translates to saving 13 minutes per week per developer, equivalent to 10 hours annually. With this org’s 150 engineers, improving the score by one point results in about 33 hours saved per week. For so much more, don't miss the full post: https://lnkd.in/grrpfwrK

Evaluating LLMs is hard. Evaluating agents is even harder. This is one of the most common challenges I see when teams move from using LLMs in isolation to deploying agents that act over time, use tools, interact with APIs, and coordinate across roles. These systems make a series of decisions, not just a single prediction. As a result, success or failure depends on more than whether the final answer is correct. Despite this, many teams still rely on basic task success metrics or manual reviews. Some build internal evaluation dashboards, but most of these efforts are narrowly scoped and miss the bigger picture. Observability tools exist, but they are not enough on their own. Google’s ADK telemetry provides traces of tool use and reasoning chains. LangSmith gives structured logging for LangChain-based workflows. Frameworks like CrewAI, AutoGen, and OpenAgents expose role-specific actions and memory updates. These are helpful for debugging, but they do not tell you how well the agent performed across dimensions like coordination, learning, or adaptability. Two recent research directions offer much-needed structure. One proposes breaking down agent evaluation into behavioral components like plan quality, adaptability, and inter-agent coordination. Another argues for longitudinal tracking, focusing on how agents evolve over time, whether they drift or stabilize, and whether they generalize or forget. If you are evaluating agents today, here are the most important criteria to measure: • 𝗧𝗮𝘀𝗸 𝘀𝘂𝗰𝗰𝗲𝘀𝘀: Did the agent complete the task, and was the outcome verifiable? • 𝗣𝗹𝗮𝗻 𝗾𝘂𝗮𝗹𝗶𝘁𝘆: Was the initial strategy reasonable and efficient? • 𝗔𝗱𝗮𝗽𝘁𝗮𝘁𝗶𝗼𝗻: Did the agent handle tool failures, retry intelligently, or escalate when needed? • 𝗠𝗲𝗺𝗼𝗿𝘆 𝘂𝘀𝗮𝗴𝗲: Was memory referenced meaningfully, or ignored? • 𝗖𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗶𝗼𝗻 (𝗳𝗼𝗿 𝗺𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝘀𝘆𝘀𝘁𝗲𝗺𝘀): Did agents delegate, share information, and avoid redundancy? • 𝗦𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗼𝘃𝗲𝗿 𝘁𝗶𝗺𝗲: Did behavior remain consistent across runs or drift unpredictably? For adaptive agents or those in production, this becomes even more critical. Evaluation systems should be time-aware, tracking changes in behavior, error rates, and success patterns over time. Static accuracy alone will not explain why an agent performs well one day and fails the next. Structured evaluation is not just about dashboards. It is the foundation for improving agent design. Without clear signals, you cannot diagnose whether failure came from the LLM, the plan, the tool, or the orchestration logic. If your agents are planning, adapting, or coordinating across steps or roles, now is the time to move past simple correctness checks and build a robust, multi-dimensional evaluation framework. It is the only way to scale intelligent behavior with confidence.

📊 Applications of Statistics in Agriculture: Tools, Purpose, and Real-World Examples 🌾 Statistics is transforming modern agriculture — from improving crop yields to enhancing agribusiness decisions. Here's a quick overview of how different statistical tools are driving agricultural innovation: ✅ Crop Yield Prediction Tool: Regression Analysis Purpose: Predict crop yield based on factors like rainfall and fertilizer. Example: Forecasting wheat yield from seasonal rainfall data. ✅ Soil Health Assessment Tool: Descriptive Statistics, Cluster Analysis Purpose: Summarize and group soils based on fertility. Example: Grouping soil samples by pH and organic matter content. ✅ Pest and Disease Management Tool: Probability Distributions, Time Series Analysis Purpose: Model frequency and timing of pest outbreaks. Example: Predicting locust swarms after monsoon rainfall. ✅ Breeding and Variety Trials Tool: ANOVA, Experimental Designs (RCBD, CRD) Purpose: Compare different crop varieties. Example: Testing new rice varieties for higher yield. ✅ Agricultural Marketing Tool: Time Series Forecasting Purpose: Predict commodity price trends. Example: Forecasting onion prices for market planning. ✅ Irrigation and Water Management Tool: Correlation Analysis Purpose: Understand relationships between irrigation and crop performance. Example: Analyzing irrigation frequency and maize yield. ✅ Precision Agriculture Tool: Cluster Analysis Purpose: Classify farms into management zones. Example: Dividing fields by nitrogen requirements for targeted fertilization. ✅ Sustainability and Risk Management Tool: Probability and Risk Models Purpose: Analyze risks like droughts and climate impacts. Example: Calculating drought risk for cotton farmers. ✅ Post-Harvest Loss Analysis Tool: Chi-square Tests Purpose: Identify causes of storage losses. Example: Associating storage methods with grain spoilage rates. ✅ Livestock Productivity Studies Tool: Regression Analysis Purpose: Predict livestock output based on feeding patterns. Example: Forecasting dairy cow milk production from feed intake. 🌱 Key Insight: "Statistics isn't just about numbers — it's about making smarter, data-driven decisions that transform agriculture sustainably and profitably."

Most people evaluate LLMs by just benchmarks. But in production, the real question is- how well do they perform? When you’re running inference at scale, these are the 3 performance metrics that matter most: 1️⃣ Latency How fast does the model respond after receiving a prompt? There are two kinds to care about: → First-token latency: Time to start generating a response → End-to-end latency: Time to generate the full response Latency directly impacts UX for chat, speed for agentic workflows, and runtime cost for batch jobs. Even small delays add up fast at scale. 2️⃣ Context Window How much information can the model remember- both from the prompt and prior turns? This affects long-form summarization, RAG, and agent memory. Models range from: → GPT-3.5 / LLaMA 2: 4k–8k tokens → GPT-4 / Claude 2: 32k–200k tokens → GPT-OSS-120B: 131k tokens Larger context enables richer workflows but comes with tradeoffs: slower inference and higher compute cost. Use compression techniques like attention sink or sliding windows to get more out of your context window. 3️⃣ Throughput How many tokens or requests can the model handle per second? This is key when you’re serving thousands of requests or processing large document batches. Higher throughput = faster completion and lower cost. How to optimize based on your use case: → Real-time chat or tool use → prioritize low latency → Long documents or RAG → prioritize large context window → Agentic workflows → find a balance between latency and context → Async or high-volume processing → prioritize high throughput My 2 cents 🤌 → Choose in-region, lightweight models for lower latency → Use 32k+ context models only when necessary → Mix long-context models with fast first-token latency for agents → Optimize batch size and decoding strategy to maximize throughput Don’t just pick a model based on benchmarks. Pick the right tradeoffs for your workload. 〰️〰️〰️ Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

I wish someone taught me this in my first year as a PM. It would’ve saved years of chasing the wrong goals and wasting my team's time: "Choosing the right metric is more important than choosing the right feature." Here are 4 metrics mistakes even billion-dollar companies have made and what to do instead with Ron Kohavi: 1. Vanity Metrics They look good. Until they don’t. A social platform he worked with kept showing rising page views… While revenue quietly declined. The dashboard looked great. The business? Not so much. Always track active usage tied to user value, not surface-level vanity. 2. Insensitive Metrics They move too slowly to be useful. At Microsoft, Ronny Kohavi’s team tried using LTV in experiments. but saw zero significant movement for over 9 months. The problem is you can’t build momentum on data that’s stuck in the future. So, use proxy metrics that respond faster but still reflect long-term value. 3. Lagging Indicators They confirm success after it’s too late to act. At a subscription company, churn finally spiked… but by then, 30% of impacted users were already gone. Great for storytelling but let's be honest, it's useless for decision-making. You can solve it by pairing lagging indicators with predictive signals. (Things you can act on now.) 4. Misaligned Incentives They push teams in the wrong direction. One media outlet optimized for clicks and everything was looking good until it wasn't. They watched their trust drop as clickbait headlines took over. The metric had worked. They might had "more MRR". But the product suffered in the long run. It's cliche but use metrics that align user value with business success. Because Here's The Real Cost of Bad Metrics - 80% of team energy wasted optimizing what doesn’t matter - Companies with mature metrics see 3–4× stronger alignment between experiments and outcomes - High-performing teams run more tests but measure fewer, better things Before you trust any metric, ask: - Can it detect meaningful change in faster? - Does it map to real user or business value? - Is it sensitive enough for experimentation? - Can my team interpret and act on it? - Does it balance short-term momentum and long-term goals? If the answer is no, it’s not a metric worth using. — If you liked this, you’ll love the deep dive: https://lnkd.in/ea8sWSsS

Things I’m Not Falling For (Nonprofit Edition) 🙅🏾♀️ ❌ “Participant numbers = impact.” Serving 500 people isn’t impact — it’s activity. Funders don’t care how many walked through your doors. They want to know how lives changed. “We had 200 participants” tells me nothing about transformation. “85% secured stable employment within 6 months” tells me everything. Stop counting bodies. Start measuring change. ❌ “You can figure out evaluation later.” Wrong. Impact measurement isn’t an afterthought — it’s baked into your program from day one. Nonprofits scrambling to pull together inconsistent reports usually waited too long. The ones landing multi-year, six-figure grants? They planned for impact before serving their first participant. ❌ “Good work speaks for itself.” Not today. Your passion got you the first $50K grant. Scaling to $250K+ requires proof. Funders invest strategically, not emotionally. Your heart might be in the right place, but your data better be too. ❌ “Relationships are all you need for funding.” Relationships open doors. Impact keeps them open. I’ve seen nonprofits raise millions on founder connections — then lose funding when leadership changes or tough questions come. The survivors? They built programs that prove their worth beyond personal ties. Ready to move beyond myths and build real impact? Let’s talk.

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

Here are some realistic KPIs that project managers can actually track : 1. Schedule Management 🔹 Average Delay Per Milestone – Instead of just tracking whether a project is on time or not, measure how many days/weeks each milestone is getting delayed. 🔹 Number of Change Requests Affecting the Schedule – Count how many changes impacted the original timeline. If the number is high, the planning phase needs improvement. 🔹 Planned vs. Actual Work Hours – Compare how many hours were planned per task vs. actual hours logged. 2. Cost Management 🔹 Budget Creep Per Phase – Instead of just tracking overall budget variance, break it down per phase to catch overruns early. 🔹 Cost to Complete Remaining Work – Forecast how much more is needed to finish the project, based on real-time spending trends. 🔹 % of Work Completed vs. % of Budget Spent – If 50% of the budget is spent but only 30% of work is completed, there's a financial risk. 3. Quality & Delivery 🔹 Number of Rework Cycles – How many times did a deliverable go back for corrections? High numbers indicate poor initial quality. 🔹 Number of Late Defect Reports – If defects are found late in the project (e.g., during UAT instead of development), it increases risk. 🔹 First Pass Acceptance Rate – Measures how often stakeholders approve deliverables on the first submission. 4. Resource & Team Management 🔹 Average Workload per Team Member – Tracks who is overloaded vs. underloaded to ensure fair distribution. 🔹 Unplanned Leaves Per Month – A rise in unplanned leaves might indicate burnout or dissatisfaction. 🔹 Number of Internal Conflicts Logged – Measures how often team members escalate conflicts affecting productivity. 5. Risk & Issue Management 🔹 % of Risks That Turned into Actual Issues – Helps evaluate how well risks are being identified and mitigated. 🔹 Resolution Time for High-Priority Issues – Tracks how quickly critical issues get fixed. 🔹 Escalation Rate to Senior Management – If too many issues are getting escalated, it means the PM or team lacks decision-making authority. 6. Stakeholder & Client Satisfaction 🔹 Number of Unanswered Client Queries – If clients are waiting too long for responses, it could lead to dissatisfaction. 🔹 Client Revisions Per Deliverable – High revision cycles mean expectations were not aligned from the start. 🔹 Frequency of Executive Status Updates – If stakeholders are always asking for updates, the communication process might be weak. 7. Agile Scrum-Specific KPIs 🔹 Story Points Completed vs. Committed – If a team commits to 50 points per sprint but completes only 30, they are overestimating capacity. 🔹 Sprint Goal Success Rate – Tracks how many sprints successfully met their goal without major spillovers. 🔹 Number of Bugs Found in Production – Helps measure the effectiveness of testing. PS: Forget CPI and SPI - I just check time, budget, and happiness. Simple and effective! 😊