Decision Analysis In Project Management

9 out of 10 CEOs are tracking the wrong metrics. (I learned this the hard way.) So many are flying blind. Making gut decisions. Wondering why growth feels so hard. But these 18 KPIs change everything. Here's what every CEO should be watching: REVENUE & PROFITABILITY ↳ Revenue Growth Rate shows if you're gaining momentum ↳ Gross Margin reveals your pricing power ↳ Net Profit Margin tells the real health story CASH & RUNWAY ↳ Operating Cash Flow confirms you're funding yourself ↳ Cash Runway warns when to raise or cut spend ↳ Burn Multiple shows capital efficiency to investors CUSTOMER METRICS ↳ Customer Acquisition Cost guides marketing budgets ↳ Customer Lifetime Value validates if CAC is justified ↳ LTV-to-CAC Ratio predicts long-term profitability RETENTION & GROWTH ↳ Net Revenue Retention measures product stickiness ↳ Churn Rate gives early alerts on product issues ↳ Net Promoter Score predicts retention and referrals OPERATIONAL EFFICIENCY ↳ Sales Cycle Length impacts cash flow forecasts ↳ Days Sales Outstanding signals collection efficiency ↳ Employee Turnover Rate reflects culture and hiring FINANCIAL HEALTH ↳ EBITDA strips out accounting noise ↳ Growth Efficiency Ratio reveals expansion quality ↳ Average Revenue Per Account tracks upsell impact The magic isn't in tracking everything. It's in tracking the RIGHT things consistently. Most CEOs drown in vanity metrics while missing the signals that actually predict success. These 18 KPIs cut through the noise. They give you the clarity to make confident decisions. And the confidence to sleep better at night. 🔖 Save this cheat sheet. Review it monthly. ♻️ Share it. Help a CEO in your network. P.S. Which KPI do you watch most closely? Share in the comments below. Want a PDF of the 18 KPIs for CEOs? Get it free: https://lnkd.in/dhh5irfH And follow Eric Partaker for more CEO insights. ————— 📢 Ready to become a world-class CEO? I'm hosting a FREE TRAINING: "7 Steps to Become a Super Productive CEO" Thur, June 12th, 12 noon Eastern / 5pm UK time https://lnkd.in/d9BuZcrd 📌 20+ Founders & CEOs have already enrolled in our  next CEO Accelerator cohort, starting July 23rd. Earlybird offer ENDS SOON. Learn more and apply: https://lnkd.in/dwjGUkEN

#CEOLife #CEOTalks #Collaboration #DecisionMaking "Diversity in Opinion. Unity in Decision." This is one of the thoughts that I have been working on as a leadership style to drive good Decision Making with Collaboration. In many organizations, managers/leaders by default resort to a hierarchical way of Decision Making. In some, you just get stalled while people try to get to Consensus, which is not easy (and may not be right even). Very few teams and organizations are able to build a culture where people across levels feel free and encouraged to share their views openly but then once a decision is made demonstrate true unity even if their personal view was different. Getting this balance right is critical to get to right decisions for organizations (and not individuals) and create a collaborative culture. A few thoughts that I think are great for managers and leaders to reflect on as they try to get this balance right: - Build trust and mutual respect - you have a team with different expertise and experiences to ensure you look at an issue from multiple perspectives. So build the respect for them versus consider them as threats/challengers - Maintain focus on the Organization and the larger purpose during discussions - stop that voice in your head that tries to make views others express as a challenge to your authority or intellect! That's what they are paid to bring to the table! Stay focused on thought that your job is to get the right decision for the organization - Use the debate/discussion not so much to put your idea on the table as the boss but what the priorities (and why) are for the organization that others may not fully have visibility of. Remember your role is not necessarily to give the idea but to ensure the right idea is tabled, and selected. So remove the burden of trying to come up with the best ideas but focus on thinking through the options on merit - Closing the discussion in the right way is important and you need to ensure that why the final decision is being taken on a certain way is understood by all, and that you as a team acknowledge why done of the other options were dropped explicitly (else they will keep coming back, especially if things don't go as well!!) As a managerial/leadership team, building this culture of an open dialogue/debate but unity in the decision once taken is what perhaps differentiates great teams from others. #campustalks #careerwars #leadership #leadershipdevelopment #management #mentoring #coaching #buildingcareers

The Alan Turing Institute and General Medical Council have published a report to explore UK doctors’ experiences with, and perceptions of, AI in their work. A survey on AI use was completed by 929 UK registered doctors between December 2023 and January 2024. The survey looked at the use of three types of AI systems: 🤖 Diagnostic and decision support (DDS) systems that are designed to help doctors diagnose patients or improve clinical decision making; 🤖 Efficiency focussed systems that aim to improve the allocation of medical resources or the functioning of clinical settings 🤖 Generative systems such as ChatGPT that can aid in the creation of text and images. The survey asked a range of questions about doctors’ perceptions of both the potential and actual use of these types of AI system in their work. 𝐔𝐬𝐞 𝐨𝐟 𝐀𝐈 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 AI is embedded in the working life of a sizeable portion of doctors, with 29% reporting that they had made use of at least one AI system in their work in the last year. However, the majority of doctors are not making any use of AI systems in their work, meaning that there are significant areas where the potential of the technology isn’t being explored. 𝐓𝐲𝐩𝐞𝐬 𝐨𝐟 𝐀𝐈 𝐬𝐲𝐬𝐭𝐞𝐦 In terms of the type of system used, 16% of doctors reported using DDS systems, with a further 16% using generative AI. Fewer doctors (7%) reported using systems focussed on efficiency. 𝐏𝐞𝐫𝐜𝐞𝐩𝐭𝐢𝐨𝐧𝐬 𝐨𝐟 𝐀𝐈 Doctors were generally positive about AI systems, with a majority (52%) saying they were optimistic about the technology’s deployment in the healthcare system. Most doctors (54%) also felt opportunities for AI in healthcare were not being fully explored. Only a minority of doctors (15%) felt the technology was making them worried about their job security. 𝐄𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞𝐬 𝐰𝐢𝐭𝐡 𝐀𝐈 Doctors’ experiences of using AI systems were also positive. A majority (62%) of DDS system users felt that the systems improved their clinical decision making. A majority (62%) of generative AI system users felt that these systems improved their productivity. However, whilst most DDS users (56%) felt that they had received sufficient training on the system they were using, only a minority (15%) of generative AI users felt that the training they had received was sufficient. 𝐏𝐫𝐨𝐟𝐞𝐬𝐬𝐢𝐨𝐧𝐚𝐥 𝐫𝐞𝐬𝐩𝐨𝐧𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬 𝐚𝐧𝐝 𝐀𝐈 Doctors generally felt that they would be confident to ignore the recommendations of AI systems if they disagreed with them (54%). However, only 30% felt they had a clear understanding of who was responsible if an erroneous decision was made using an AI system, whilst only 12% felt they had sufficient training in order to enable them to understand their professional responsibilities when using AI systems (even amongst the group of doctors using AI, this number was only 17%). AI will undoubtedly be part of everyday clinical life and as doctors we need to lead the way.

Smart CRM Basics Predictive Customer Behavior Modeling The Advantages of Predictive Behavior Modeling When Marketers can target specific customers with a specific marketing action – you are likely to have the most desirable campaign impact. Every marketing campaign and retention tactic will be more successful. The ROI of upsell, cross-sell, and retention campaigns will be more significant. For example, imagine being able to predict which customers will churn and the particular marketing actions that will cause them to remain long-term customers. Customers will feel the greater relevance of the company’s communications with them – resulting in greater satisfaction, brand loyalty, and word-of-mouth referrals. Enhancing Customer Segmentation for Personalization Predictive analytics refines customer segmentation by identifying patterns within data. By understanding customer segments on a deeper level, businesses can personalize their interactions, marketing messages, and product recommendations. This tailored approach fosters a stronger connection with customers, leading to increased loyalty. Anticipating Customer Needs Through Lead Scoring Lead scoring becomes more accurate with the integration of predictive analytics. By evaluating customer data, such as interactions with emails, website visits, and social media engagement, businesses can prioritize leads based on their likelihood to convert. This ensures that sales teams focus their efforts on leads with the highest potential. Optimizing Sales Forecasting Accurate sales forecasting is crucial for effective resource allocation and business planning. Predictive analytics in CRM analyzes past sales data, market trends, and customer behaviors to generate more accurate sales forecasts. This empowers businesses to make informed decisions, allocate resources efficiently, and capitalize on emerging opportunities. Transforming CRM with Predictive Analytics Predictive analytics is revolutionizing CRM by providing invaluable insights into customer behaviors. From personalized marketing campaigns to proactive churn prevention, businesses can leverage these predictions to enhance customer relationships and drive growth. As technology continues to advance, integrating predictive analytics into CRM systems is not just a strategy for staying competitive; it's a key component in building lasting customer-centric businesses in the digital age. #PredictiveAnalytics #CRMInsights #CustomerBehavior #DataDrivenDecisions #BusinessIntelligence #CustomerRetention #SalesForecasting #MarketingStrategy #EthicalCRM #DynamicPricing

A must-read study in JAMA Network Open just compared a traditional diagnostic decision support system (DDSS), DXplain, with two large language models, ChatGPT-4 (LLM1) and Gemini 1.5 (LLM2), using 36 unpublished complex clinical cases. Key Findings: - When lab data was excluded, DDSS outperformed both LLMs: 56% vs. 42% (LLM1) and 39% (LLM2) in listing the correct diagnosis. - When lab data was included, performance improved for all: DDSS (72%), LLM1 (64%), LLM2 (58%). - Importantly, each system captured diagnoses that the others missed, indicating potential synergy between expert systems and LLMs. While DDSS still leads, the exponential improvement in #LLMs cannot be ignored. The study presents a compelling case for hybrid approaches—combining deterministic rule-based systems with the linguistic and contextual fluency of LLMs, while also incorporating structured data with standardized coding, such as LOINC codes and SNOMED International..etc The inclusion of structured data significantly enhanced diagnostic accuracy across the board. This validates the notion that structured and unstructured data must collaborate, not compete, to deliver better #CDS outcomes. #HealthcareonLinkedin #Datascience #ClinicalInformatics #HealthIT #AI #GenAI #ClinicalDecisionSupport

𝓢𝓾𝓹𝓹𝓵𝔂 𝓒𝓱𝓪𝓲𝓷 𝓓𝓲𝓼𝓻𝓾𝓹𝓽𝓲𝓸𝓷𝓼 𝓐𝓻𝓮𝓷’𝓽 𝓖𝓸𝓲𝓷𝓰 𝓐𝓷𝔂𝔀𝓱𝓮𝓻𝓮—𝓑𝓾𝓽 𝓓𝓪𝓽𝓪 𝓒𝓪𝓷 𝓗𝓮𝓵𝓹 𝓨𝓸𝓾 𝓟𝓻𝓮𝓭𝓲𝓬𝓽 𝓪𝓷𝓭 𝓟𝓻𝓮𝓹𝓪𝓻𝓮 From geopolitical tensions to energy shortages and shipping bottlenecks, supply chain shocks are now part of business-as-usual. We’ve seen how a delay at one port can ripple across continents—affecting inventories, pricing, and customer experience. Add climate-related events and policy shifts into the mix, and the volatility only grows. But amid the chaos, one thing offers 𝐜𝐥𝐚𝐫𝐢𝐭𝐲: 𝚍𝚊𝚝𝚊. ✅ 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐚𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 can flag disruptions before they escalate—by analyzing weather patterns, political instability, or supplier performance. ✅ 𝐑𝐞𝐚𝐥-𝐭𝐢𝐦𝐞 𝐦𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 helps organizations reroute logistics, rebalance inventories, and communicate proactively with partners and customers. ✅ 𝐒𝐜𝐞𝐧𝐚𝐫𝐢𝐨 𝐩𝐥𝐚𝐧𝐧𝐢𝐧𝐠 tools allow businesses to simulate “what-if” situations and prepare contingency strategies in advance. Supply chain resilience is no longer about just-in-time—it’s about being 𝗃𝗎𝗌𝗍-𝗂𝗇-𝖼𝖺𝗌𝖾. 🔍 The question is: 𝑨𝒓𝒆 𝒚𝒐𝒖 𝒖𝒔𝒊𝒏𝒈 𝒚𝒐𝒖𝒓 𝒅𝒂𝒕𝒂 𝒕𝒐 𝒑𝒍𝒂𝒚 𝒅𝒆𝒇𝒆𝒏𝒔𝒆… 𝒐𝒓 𝒕𝒐 𝒔𝒕𝒂𝒚 𝒐𝒏𝒆 𝒔𝒕𝒆𝒑 𝒂𝒉𝒆𝒂𝒅? #PredictiveAnalytics #DataDrivenDecisionMaking #SupplyChainManagement #RiskManagement #LogisticsStrategy

The harsh truth: Without proper techno-economic assessment, your net zero technology or project can be *just* a science experiment. Here’s what you need to know. Performing a techno-economic assessment (TEA) from the early stage of technology or project development will support your decision making. It will provide you with key insights into costs, benefits, and feasibility. Here’s a quick breakdown of the key steps in a TEA: 1. Define Your Goal and Scope • What are you trying to achieve with this assessment? • Set clear objectives, boundaries, and a functional unit (e.g., cost per ton of CO₂ avoided). 2. Design Your Process and System Boundaries • Map out the process with flow diagrams and identify all key input/output streams. • Establish clear boundaries to understand what’s included in the analysis. 3. Gather Data for Inventory Analysis • Collect data on capital expenditures (CAPEX), operating costs (OPEX), energy use, and material inputs. • Address gaps and uncertainties in data collection. 4. Perform Economic Modeling • Break down costs into CAPEX, OPEX, and variable costs. • Use tools like discounted cash flow (DCF) analysis to calculate metrics like NPV and ROI. 5. Assess Key Performance Indicators (KPIs) • Focus on critical metrics such as: • Cost per ton of CO₂ avoided • Energy efficiency • Payback period 6. Run Sensitivity and Uncertainty Analysis • Identify the most significant cost drivers and test assumptions under different scenarios. • Identify and understand financial risks 7. Interpret and Present Results • Link findings to actionable recommendations for optimization or decision-making. • Communicate results in a way that resonates with stakeholders (e.g., policymakers, investors). Pro Tip: Combine TEA with life cycle assessment (LCA) to address both economic and environmental impacts for a holistic evaluation. 💡 Want to learn how to build and apply a TEA for your net zero project? I’ll be hosting regular 2-day training sessions throughout 2025 to provide hands-on guidance and tools to evaluate your projects confidently. The first cohort will be announced later today (as I’m screening through 250 applications!) #CarbonCapture #Research #Scientist #Sustainability #NetZero #ChemicalEngineering #Professor

ℹ️ Gartner Publishes Market Guide for Analytics and Decision-Making Platforms for Supply Chain The analytics and decision-making platform (ADM) market is evolving, but it remains highly fragmented. Supply chain technology leaders should use this guide to navigate the market environment and inform a cohesive technology roadmap for adopting supply-chain-specific ADM platform capabilities. OUR KEY FINDINGS 🔵 Line of business teams are looking to speed up cross-functional decision making with near-real-time insights and unstructured content to quickly react to and prevent disruptions, allowing them to enhance the quality of decisions. 🔵 Leaders now recognize that supply-chain-focused analytics and decision-making (ADM) platforms are enablers of flexibility and quicker time to value when dealing with a deluge of data. 🔵 ADM platforms for supply chain management (SCM) help leaders achieve connected, contextual, continuous and compliant decisions. Leveraging composite AI — the combined application (or fusion) of different #AITechniques — within a platform improves the platform’s learning efficiency and broadens its level of knowledge representations and AI abstraction mechanisms and provides a platform to solve a wider range of supply chain problems effectively. 🔵 Vying for a bigger share of the #SupplyChain ADM platforms market, vendors are repositioning their solutions to provide a broader range of capabilities. This has resulted in all-encompassing platforms with preconfigured use case templates and blueprints, as well as a development environment that supports analytics and #AI techniques specialized for supply chain functions. OUR RECOMMENDATIONS 🔵 Create a roadmap for adopting ADM platforms for SCM by using Gartner’s Technology Adoption Roadmap for #Data and #Analytics. 🔵 Optimize solution effectiveness and implementation efficiency, and maximize business value, by carefully choosing a buy, build or partner model for #DecisionSupport, #DecisionAugmentation, and eventually, responsible and safe #DecisionAutomation. 🔵 Build trust in autogenerated recommendations from ADM platforms by ensuring #explainability of their insights and investing in data quality. Establish realistic expectations about limitations (such as #hallucinations) and embrace the needs of multiple user personas by fostering user #collaboration, #composability or automated #insights. 🔵 Mitigate the risks of functional duplication by defining, communicating and enforcing strict governance policies. Select and align success #metrics and #KPIs across #decisions. Gartner clients subscribed to our Supply Chain Technology Strategy and Selection practices can login and read: "Market Guide for Analytics and Decision-Making Platforms for Supply Chain" [Published 14 January 2025 | G00798281] led by Christian Titze, Leonard Ammerer and myself (David Pidsley): https://lnkd.in/eW8dbTBt 🥳 Congratulations to the representative vendors featured

In my last role, I led two super important 0 → 1 projects. One failed, one succeeded. Here are some learnings: The two projects: Project 1: Hey, can we take this existing unpaid feature & unbundle it to make revenue? Day 1. I’m excited! First, I need a Notion to make sure everyone is aligned. Day 10. Analysis-paralysis. Ok I think I have the structure. Let me just do a little more research. Day 20. Imposter syndrome. I have no idea what I’m doing. I'll get fired. Ugh. Project 2. Hey, for our WAU metric, I think hosting great leaders in the community weekly will be great. Day 1. Action. I’m not sure what the format should be, or who these leaders are. How does one even go about this? Let me go ask some people & speak to users. Day 3. Calendars blocked, topic closed. Now we have to announce it so people show up. Day 7, shipped. Now, what was different? 1/ Commit to a deadline before knowing the "how" In project 1, I had too much freedom without constraints. In Project 2, we decided we wanted to host the first event in 7 days. Booking the calendars of members before closing the speaker was scary. But we had now made a user commitment, we had to figure it out. When you’re doing new things, setting the deadline & then figuring it out always works. You could make the commitment in different ways: book the venue, book a meeting for a team review, just pick a launch date. 2/ Make small decisions, iterate as needed We’re taught to plan → iterate plan → build. I tried that. But, hovering at 30,000 ft gave no feedback. In reality: build → iterate → plan works. In the second project, blocking the date forced us to figure leaders we want to get, a host, structure & the GTM in time. By shipping tiny pieces, we wrote the strategy on the fly & could judge it against reality, not theory. Eventually, we were hosting great leaders every single week. We built the playbook by doing. Drafting the final launch copy, landing page, hero image kills analysis paralysis & makes things tangible. Add a ‘beta’ label on anything new; the word alone lowers the stakes. 3/ Make your project more people’s problem In Project 1, I acted like a lone-wolf. I had to do it by myself to prove my worth. This threw me into a spiral of self-doubt. In the 2nd project, I asked for help (loudly, clearly, often) & the project became the team’s project. That allowed me to get more input & support. In the end, when it was a success, it became a shared win. Way more fun, trust me. Getting buy-in isn’t automatic though. Here are three things that work: 1/ Get the founders to buy-in to your idea first. Their urgency becomes everyone’s urgency. 2/ Bring your manager along for the ride. The more they know about what you’re doing and your challenges, the more they will figure out how to support you. 3/ Use public slack channels, tag specific people for asks. Don't DM for favours, it tilts incentives.

Is your team tapping into collective wisdom or falling into groupthink? 🤔 🫶🏼 Groupthink occurs when a group's desire for harmony and agreement causes members to ignore different opinions, avoid critical thinking, and make poor decisions just to keep the peace. ☝🏼Collective wisdom happens when the aggregated opinions, knowledge or predictions of a diverse and independent group of people leads to more accurate decisions. To shift a team from groupthink to collective wisdom, the decision-making process should be structured to encourage open communication, critical thinking, and the value of diverse perspectives. How to facilitate this shift? 📝 Individual pre-work: Ask members to independently analyze the issue and prepare their opinions before group discussions. This can help prevent initial ideas from dominating the conversation. 😈 Use rotating roles ... such as "devil's advocate," "fact-checker," and "process observer" to various members, rotating these roles to ensure balanced participation and a critical examination of the group's decisions. 🧠 Use brainwriting instead of brainstorming So the ideas can get first generated individually, then shared and discussed as a group What methods have you found effective in encouraging independent thinking and open dialogue in group settings?