Evaluating Project Performance Metrics

Glen Palmer, PSP, CFCC, FAACE and I are honored by AACE publishing another of our Top Ten series of papers in the Cost Engineering Journal. Resource management sits at the heart of project success—and, too often, at the root of costly construction claims. Why Focus on Resources? Most construction schedules are built on assumptions about production rates, durations, and quantities. But when resource planning falls short—whether due to unrealistic manpower peaks, lack of skilled labor, or poor coordination—projects risk delays, cost overruns, and disputes. Rather than waiting for claims to arise, Palmer and Carson argue for a proactive approach: plan, validate, and monitor your resources from day one. Key Takeaways from the Top Ten Approaches: 1. Validate Resources by Discipline: Go beyond surface-level schedule checks. Detailed resource validation—using field-experienced personnel—can identify unrealistic resource peaks and prevent unachievable schedules. 2. Formalize Punch and Warranty List Management: Avoid never-ending completion and warranty periods by developing comprehensive, early punch lists and using structured warranty management systems. 3. Check Resource Earning Curves: Ensure planned progress is actually achievable by comparing planned manpower curves and production rates to real-world constraints. 4. Manage Schedule Compression: When compressing schedules, understand the risks and costs of acceleration and recovery. Use structured analysis and documentation to avoid disputes. 5. Review General Conditions Labor: Monitor and budget field overhead costs carefully, and avoid relying on variable, hard-to-track level-of-effort activities. 6. Use Constructability Reviews: Always have experienced field experts review “fast-tracked” project schedules to spot resource and constructability problems early. 7. Address Trade Stacking and Overcrowding: Analyze crew concurrency and area usage to prevent inefficiencies from too many workers or trades in the same space. 8. Specify Resource Requirements in Schedules: Include resource histograms and percent curves in scheduling specifications to enable thorough schedule reviews. 9. Plan for Resource Availability: Evaluate the availability of skilled labor and specialty resources, especially on large or geographically constrained projects. 10. Minimize Inefficiencies from Disrupted Trade Work: Align procurement, sequencing, and trade starts to reduce disruption, and use targeted planning to ensure work is completed efficiently on the first attempt. Conclusion: Resource-related claims are often avoidable with disciplined planning, honest schedule validation, and ongoing monitoring. By following these ten approaches, project teams can dramatically reduce the risk of disputes, keep projects on track, and protect both profit and reputation.

Lou Gerstner walked into IBM in 1993 expecting a strategy problem. What he found was worse. Here's what leaders need to learn: Every division had a strategy. Every executive had a vision. Every team was chasing a different goal. Engineering was building for one future. Sales was selling into another. Marketing had its own roadmap entirely. At his first exec meeting, each leader presented different success metrics: Revenue. Market share. Innovation. NPS. Same company, completely different definitions of winning. Gerstner didn’t write a new strategy. He did something more powerful: He mandated one framework for priorities. Same metrics. Same language. Same scorecard. Within 6 months, misalignment became visible. Within a year, IBM started moving as one. I saw the same pattern play out in a Fortune 500 basement. The quarterly review was nearly over when the Head of Ops paused: “I need to be honest. I don’t even know what our top 3 priorities are right now.” Silence. Then heads nodded. The CMO had been focused on brand. Sales thought revenue was the priority. The CTO was deep in infrastructure rebuild. The CFO was chasing cost control. 9 executives. 27 different priorities. 3 overlaps. That’s not a team. That’s a collection of soloists. Strategy isn’t the problem. Alignment is. Everyone knows the strategy. But what are they actually optimizing for this week? I’ve seen it again and again: • Monday: “Retention is everything” • Friday: Sales signs three bad-fit clients to hit quota • Product starts chasing new features • Success never gets the memo 5 days. Alignment gone. So how do you fix it? 1. Make priorities visible weekly Every Monday: top 3 org-wide priorities, posted publicly. No guessing. No side quests. 2. Create explicit handoffs Marketing, sales, product, and success - define the exact criteria for every handoff. Spotify did this. Discovered 40% of handoffs had misaligned expectations. 3. Run weekly alignment checks One question: What are you optimizing for this week? If it doesn’t match the org’s top 3, you catch drift instantly. 4. One source of truth No more 50 dashboards. Microsoft did this with their Customer Success Score. Every division had to contribute to the same North Star. Alignment doesn’t happen by accident. It deteriorates by default. Great companies don’t assume alignment. They build it systematically. That Fortune 500 team? 6 months later, they went from 27 priorities to 3. Revenue grew 18%. Engagement jumped 43% → 71%. All because they stopped guessing. Want more research-backed frameworks like this? Join 11,000+ execs who get our newsletter every week: 👉 https://lnkd.in/en9vxeNk

A viral image of an ATM in Ludhiana recently caught my attention - a dangerously steep ramp ending abruptly at a glass door, with a staircase running alongside that leads nowhere. A perfect reminder of a hard-earned lesson in fintech: "Compliance isn’t just a checkbox." Product Managers: You don't want to miss saving 💾 this post for your future reference. This ramp was technically "compliant" - yes, there was a wheelchair access ramp. But it completely missed the purpose of accessibility. People had angry comments on social media about the apathy with which wheelchair-bound customers were treated and how the bank had made a mockery of accessibility. No amount of regulation can account for 'compliance as a checkbox' implementations that are designed to meet the regulation but not serve their intended purpose. It's the same trap I've seen countless fintech products fall into - implementing regulations as mere checkboxes rather than embracing them as design principles. I've experienced regulatory hurdles umpteen times in product launches; in fact, I've never experienced a straightforward implementation that hasn't hit a regulatory roadblock. BUT I can say this confidently: Compliance-first design is the secret sauce that makes the battle easier and less arduous, and inarguably 'faster' IF You just stick to the first principles of building this into your product strategy from day one . Regulations can either slow you down or become your competitive edge. To make compliance your strategic advantage, here's my 3-step playbook: 1/ Design Integration: Make regulatory adherence a natural part of the user experience rather than an afterthought ↳Embed compliance requirements into your initial product design ↳Get feedback from legal and compliance teams, and even the regulator if needed ↳Validate, Test, Iterate, Repeat 2/ Cross-Functional Collaboration: Build bridges between product, legal/compliance teams from day one ↳Involve them early ↳Make compliance & legal stakeholders brainstorm and provide feedback ↳Balance innovation with regulatory requirements using case studies and data to back up assertions instead of getting into crosshairs with them 3/ Validate Early, Validate Often: ↳Test with real scenarios ↳Get early feedback from regulators ↳Regular compliance assessments, no matter what stage of development you are in One golden tip - document everything, err on the side of caution when it comes to building and fostering trust with legal and compliance counterparts. The lesson in one line? Build WITH compliance, not around it. Instead of working around regulations, let's build with them. Because when you design within the right guardrails, innovation doesn't just survive—it scales. What's your strategy for managing fintech compliance? Share below. 👍 LIKE this post, 🔄 REPOST this to your network and follow me, Monica Jasuja

Most are sleeping on the power of 𝗠𝗼𝗱𝗲𝗹 𝗗𝗶𝘀𝘁𝗶𝗹𝗹𝗮𝘁𝗶𝗼𝗻, and every company should have a Distillation Factory to stay competitive This technique is reshaping how companies build efficient, scalable, and cost-effective AI. First, 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗠𝗼𝗱𝗲𝗹 𝗗𝗶𝘀𝘁𝗶𝗹𝗹𝗮𝘁𝗶𝗼𝗻? Also known as knowledge distillation, is a machine learning technique where a smaller, more efficient "student" model is trained to replicate the behavior and performance of a larger, more complex "teacher" model. Think of it as a master chef (the teacher) passing down their culinary expertise to an apprentice (the student) without sharing the exact recipe. The student learns by observing the teacher’s outputs and mimicking their decision-making process, resulting in a lightweight model that retains much of the teacher’s capabilities but requires fewer resources. Introduced by Geoffrey Hinton in his 2015 paper, “Distilling the Knowledge in a Neural Network,” the process involves: 1/ Teacher Model: A large, powerful model trained on massive datasets. 2/ Student Model: A smaller, efficient model built for faster, cheaper deployment. 3/ Knowledge Transfer: The student learns from the teacher’s outputs—distilling its intelligence into a lighter version. There are several types of distillation: 1/ Response-Based: The student mimics the teacher’s final outputs 2/ Feature-Based: The student learns from the teacher’s intermediate layer representations. 3/ Relation-Based: The student captures relationships between the teacher’s outputs or features. The result? A student model that’s faster, cheaper to run, and nearly as accurate as the teacher, making it ideal for real-world applications. 𝗪𝗵𝘆 𝗘𝘃𝗲𝗿𝘆 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗡𝗲𝗲𝗱𝘀 𝗮 𝗗𝗶𝘀𝘁𝗶𝗹𝗹𝗮𝘁𝗶𝗼𝗻 𝗙𝗮𝗰𝘁𝗼𝗿𝘆? In today’s AI landscape, very large LLMs are incredibly powerful but come with significant drawbacks: high computational costs, massive energy consumption, and complex deployment requirements. A Distillation Factory is a dedicated process or team focused on creating distilled models, addressing these challenges and unlocking transformative benefits. Here’s why every company should invest in one: 1/ Cost Efficiency: Distilled models cut costs, running on minimal GPUs or smartphones, not data centers. 2/ Scalability: Smaller models deploy easily. 3/ Faster Inference: Quick responses suit real-time apps. 4/ Customization: Tailor models for healthcare or finance with proprietary data, no full retraining. 5/ Sustainability: Lower compute needs reduce carbon footprints, aligning with green goals. 6/ Competitive Edge: Rapid AI deployment via distillation outpaces costly proprietary models. A Distillation Factory isn’t just a technical process; it’s a strategic move.

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! 😊

8D PROBLEM SOLVING EXAMPLE FORMAT AND EXAMPLE: PROBLEM: Surface Scratches on Painted Parts D1 – Team Formation Formed a cross-functional team with members from Quality, Production, Paint Shop, and Maintenance. D2 – Problem Description Issue: Surface scratches on final painted parts. Location: Paint Shop – Final Inspection Area. Timing: Observed consistently over the past 3 days. Magnitude: Rejection rate increased to 15% (standard is <2%). Impact: Increased rework and delayed customer shipments. D3 – Interim Containment Action Isolated all painted parts for 100% visual inspection. Reworked all affected parts before dispatch. Instructed operators to wear gloves when handling painted components. D4 – Root Cause Analysis Scratches caused by burrs on metal hanging hooks in the conveyor system. No preventive maintenance schedule existed for hook inspection. Root Cause: Worn-out, unmaintained hooks with sharp edges. D5 – Permanent Corrective Action Replaced all burr-marked hooks with new, smooth-surfaced ones. Introduced a weekly checklist for hook inspection and maintenance. D6 – Implementation and Validation Actions implemented across all 3 shifts. Post-implementation, rejection rate dropped from 15% to 1.2% in 2 weeks. Effectiveness validated through quality audits and data analysis. D7 – Prevent Recurrence SOP updated to include hook inspection procedure. Conducted training for all Paint Shop and maintenance staff. Hook audit added to monthly quality schedule. D8 – Team Recognition Appreciation email sent by Quality Head. Team recognized during the monthly Quality Circle meeting.

If you're studying Project Management this year, here's a little checklist for closing a project (or a phase in your project). Remember, a "Predictive" or Waterfall approach might do this once at the end, but an "Adaptive" or Agile approach might do this once for each usable Increment we deliver. We want to: ➡️ Confirm formal acceptance of the deliverable(s) - usually with the Project Sponsor or project customer representative. ➡️ Finalize any open claims or disputes on the deliverables. ➡️ Ensure a smooth transition to Operations or the Business. The rise of "Organizational Change Management" like ADKAR or the Bridges model comes in here. ➡️ Measure the Product benefits. ➡️ Note all the final Lessons Learned - perhaps with a Post Implementation Review, Project Post Mortem, or Retrospective. ➡️ Now we can formally release our project resources. ➡️ We can finalize and close our project accounts. ➡️ Create the Final Report to summarize how the project went (e.g. planned vs actual), and whether the benefits were achieved. ➡️ Archive all this project information for future use by the organization. And remember - the world needs Project Managers like you, to deliver change and business value. You are doing the right thing. Keep going!

🚀 𝐁𝐞𝐧𝐜𝐡𝐦𝐚𝐫𝐤𝐢𝐧𝐠 𝐌𝐋-𝐁𝐚𝐬𝐞𝐝 𝐏𝐃𝐄 𝐒𝐨𝐥𝐯𝐞𝐫𝐬: 𝐁𝐞𝐬𝐭 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐬 Last week, I shared insights from the study 𝘞𝘦𝘢𝘬 𝘉𝘢𝘴𝘦𝘭𝘪𝘯𝘦𝘴 𝘢𝘯𝘥 𝘙𝘦𝘱𝘰𝘳𝘵𝘪𝘯𝘨 𝘉𝘪𝘢𝘴𝘦𝘴 𝘓𝘦𝘢𝘥 𝘵𝘰 𝘖𝘷𝘦𝘳𝘰𝘱𝘵𝘪𝘮𝘪𝘴𝘮 𝘪𝘯 𝘔𝘢𝘤𝘩𝘪𝘯𝘦 𝘓𝘦𝘢𝘳𝘯𝘪𝘯𝘨 𝘧𝘰𝘳 𝘍𝘭𝘶𝘪𝘥-𝘙𝘦𝘭𝘢𝘵𝘦𝘥 𝘗𝘢𝘳𝘵𝘪𝘢𝘭 𝘋𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘵𝘪𝘢𝘭 𝘌𝘲𝘶𝘢𝘵𝘪𝘰𝘯𝘴 by Nick McGreivy and Ammar Hakim (link in comments). The authors highlighted a crucial issue: many #ML -based #solvers aren't benchmarked against appropriate baselines, leading to misleading conclusions. ⚠️ 𝐒𝐨, 𝐰𝐡𝐚𝐭’𝐬 𝐭𝐡𝐞 𝐫𝐢𝐠𝐡𝐭 𝐚𝐩𝐩𝐫𝐨𝐚𝐜𝐡? 🤔 The key lies in comparing the #Cost vs. #Accuracy of #algorithms, reflecting the inherent trade-off between efficiency and precision in numerical methods. While quick, low-accuracy approximations are common, highly accurate results typically require more computational time. ⏱️ 📊 𝐀𝐜𝐜𝐮𝐫𝐚𝐜𝐲-𝐒𝐩𝐞𝐞𝐝 𝐏𝐚𝐫𝐞𝐭𝐨 𝐂𝐮𝐫𝐯𝐞𝐬: 𝐀 𝐁𝐞𝐧𝐜𝐡𝐦𝐚𝐫𝐤𝐢𝐧𝐠 𝐒𝐭𝐚𝐧𝐝𝐚𝐫𝐝 From my experience, the most effective way to benchmark is by using Pareto curves of accuracy versus computational time (see the figure below). These curves offer a clear, visual comparison, showing how different methods perform under the same hardware conditions. They also mirror real-world engineering decisions, where finding a balance between speed and accuracy is critical. ⚖️ An example of this can be seen in Aditya Phopale’s master thesis, where the performance of a #NeuralNetwork-based solver was compared against the state-of-the-art general purpose #Fenics solver. 🔍 𝐂𝐡𝐨𝐨𝐬𝐢𝐧𝐠 𝐭𝐡𝐞 𝐑𝐢𝐠𝐡𝐭 𝐁𝐚𝐬𝐞𝐥𝐢𝐧𝐞 𝐒𝐨𝐥𝐯𝐞𝐫 Nick McGreivy and Ammar Hakim also emphasize the importance of selecting an appropriate baseline. While Fenics might not be the top-notch choice when it comes to computational efficiency for a specific problem (e.g., vs spectral solvers), it is still highly relevant from an #engineering perspective. Both the investigated solver and Fenics share a similar philosophy: they are general-purpose, Python-based solvers that are based on equation formulations. 🧩 Additionally, unlike #FiniteElement solvers like Fenics, the investigated Neural Network solvers don’t require complex discretization. Thus, Fenics serves as a suitable baseline for practical engineering applications, despite its "limitations" from a more theoretical context. 💡 𝐖𝐡𝐚𝐭 𝐀𝐫𝐞 𝐘𝐨𝐮𝐫 𝐁𝐞𝐬𝐭 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐬? I’m curious to hear from others: what best practices do you follow when benchmarking ML-based PDE solvers? Let’s discuss! 👇

GCC Leaders: Are You Measuring What Truly Matters? To measure the real impact of your Global Capability Center (GCC), you must go beyond traditional operational KPIs like cost savings or headcount. Those are hygiene. What truly matters is how your GCC moves the needle for the business. Here are 5 strategic metrics every GCC leader should track: 1. Value Delivered per Dollar Spent Why it matters: Shows how effectively the GCC converts investment into business outcomes. How to measure: • Business value (e.g., product revenue, productivity gains, IP created) / Total GCC cost • Can be benchmarked against alternative models (outsourcing, onshore) 2. Time to Market Acceleration Why it matters: Reflects the GCC’s ability to improve speed of execution for product development, support, or operations. How to measure: • % improvement in release velocity or cycle times after GCC involvement • Lead time from idea to launch before vs. after GCC enablement 3. Innovation Output Why it matters: Indicates contribution toward competitive advantage and future growth. How to measure: • Patents filed, features launched, automation use cases deployed • Number of AI/GenAI initiatives incubated and scaled • New product ideas or MVPs driven from GCC 4. Business Function Ownership & Accountability Why it matters: Measures the maturity and strategic importance of the GCC. How to measure: • % of global business function fully owned or co-owned by GCC (e.g., platforms, support functions, analytics COEs) • Strategic roles (Directors, VPs) based in the GCC • Participation in global decision-making forums 5. Customer or Stakeholder NPS / Satisfaction Score Why it matters: This metric reflects how well the GCC is delivering value—both through the products it helps build and the support it provides to global stakeholders. How to measure: • NPS from external customers using products or services developed by GCC teams • NPS from internal stakeholders on the GCC’s responsiveness, collaboration, and strategic alignment • Qualitative feedback on product quality, innovation, speed of execution, and business understanding If your GCC isn’t driving the business forward, it’s just another offshore team. And in 2025, that’s not enough. Rethink how you measure. Reframe how you lead. Redefine what your GCC stands for. Zinnov Amita Goyal Karthik Padmanabhan Amaresh N. Mohammed Faraz Khan Namita Adavi Dipanwita Ghosh Sagar Kulkarni Hani Mukhey ieswariya Rohit Nair Komal Shah Saurabh Mehta

Your CFO wants to know the return on your software development budget? Here are 5 metrics that actually matter in the boardroom - and they're not story points. As a CTO, I've found these key metrics create a meaningful fitness function for your development organization: 1. Business Value per Feature: Don't just ship features - measure their impact. That new checkout process? Track how it changes conversion rates and order values. 2. Lead Time from Idea to Impact: Understand your value stream. Sometimes a 30-minute deployment is stuck behind weeks of stakeholder meetings. 3. Throughput and its composition: Monitor the balance between new features, maintenance, and bug fixes. When maintenance exceeds 25%, it's time to invest. 4. Quality Signals: Track customer experience, operational efficiency, and technical health. These are your early warning system. 5. Team Health: Happy teams deliver better results. Regular pulse checks predict delivery performance weeks before metrics show issues. But never compare teams through these metrics. Each team operates in a unique context with different challenges. Instead, help each team understand and improve their own trends. Metrics should drive improvement, not punishment. Use them as a compass, not a hammer. What metrics do you use to measure development success?