Tactical Planning In Project Management

Demand forecasting is the process of using information to make informed future customer demand estimations over a specific period. There are two main types of Demand Forecasting: 𝐐𝐮𝐚𝐥𝐢𝐭𝐚𝐭𝐢𝐯𝐞 and 𝐐𝐮𝐚𝐧𝐭𝐢𝐭𝐚𝐭𝐢𝐯𝐞. Qualitative focuses on using expert opinions and information gathered from the field, and quantitative uses data and analytical tools to create predictions. 𝐇𝐨𝐰 𝐢𝐬 𝐢𝐭 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭 𝐭𝐡𝐚𝐧 𝐃𝐞𝐦𝐚𝐧𝐝 𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠? Demand forecasting is a process for determining what is likely to happen, while demand planning is the operationalization to make it happen. It is taking that forecast and ensuring that each stage of the supply chain operates accordingly, ideally with the most efficiency and lowest cost. 𝐓𝐡𝐞 𝐜𝐨𝐬𝐭 𝐨𝐟 𝐢𝐧𝐚𝐜𝐜𝐮𝐫𝐚𝐭𝐞 𝐃𝐞𝐦𝐚𝐧𝐝 𝐅𝐨𝐫𝐞𝐜𝐚𝐬𝐭𝐢𝐧𝐠:  How much does a 1% Reduction in forecasting error save your company? According to the Institute of Business Planning and Forecasting, the average computer/tech company saves $𝟗𝟕𝟎,𝟎𝟎𝟎 a year by reducing the error of under-forecasting, and $𝟏.𝟓 𝐦𝐢𝐥𝐥𝐢𝐨𝐧 in over-forecasting. And this number goes up for consumer product companies: $𝟑.𝟓 𝐦𝐢𝐥𝐥𝐢𝐨𝐧 for a 1% improvement in under-forecasting and $𝟏.𝟒𝟑 𝐦𝐢𝐥𝐥𝐢𝐨𝐧 for over-forecasting. (Source: https://lnkd.in/e_NJNevk) 𝐈𝐦𝐩𝐫𝐨𝐯𝐢𝐧𝐠 𝐃𝐞𝐦𝐚𝐧𝐝 𝐅𝐨𝐫𝐞𝐜𝐚𝐬𝐭𝐢𝐧𝐠: From my personal experience, most companies run under 60% accuracy for demand forecasting. As an industry we can do a lot better! The best way to improve demand forecasting is through utilizing lots of data sources (internal and external), and taking advantage of ML to identify meaningful correlations. Working with a global steel, bulk container, and packaging manufacturer, Prevedere, one of Microsoft’s partners, generated predictive models with a 98.4% average 12-month forecast accuracy across the projected product lines and country. So yes, it can be done 😀 ****************************************** • Follow #JeffWinterInsights to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

Forecasting is a common application of data science, and it's crucial for businesses to manage their inventory, especially those with perishable items effectively. In a recent tech blog, the data science team from Afresh shared an innovative approach to accurately predict demand, incorporating non-traditional factors such as in-store promotions. Promotions are common in grocery stores, helping customers discover and purchase discounted items. However, these promotions can significantly alter customer behavior, making traditional forecasting methods less reliable. Traditional models struggle to incorporate these factors, often leading to higher prediction errors. To address this challenge, Afresh’s data science team developed a deep learning forecasting model that integrates various features, including promotional activities tied to specific products. The model's performance was evaluated using a normalized quantile loss metric, showing an 80% reduction in loss during promotion periods. This example highlights the superior performance of this solution and showcases the power of deep learning in solving a critical issue for the grocery industry. #machinelearning #datascience #forecasting #inventory #prediction – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gWRgTJ2Q 

When Teams Grow, Design Their Experience: An LXD Perspective. Rapid growth is often celebrated as a marker of success. Teams expand, business objectives increase, and new responsibilities are introduced. Yet growth often comes faster than the systems and processes that support it , leaving teams misaligned, overwhelmed, and disengaged. A sales team I worked with had grown from 10 to 25 members over six months. While expansion brought exciting opportunities , it also introduced a host of challenges: 📝 Increased administrative work and reporting requirements 📅 More frequent meetings for alignment across an expanded team 🎯 Higher performance expectations and KPIs ❓ Ambiguity in roles and responsibilities as new members joined Despite their enthusiasm and capability, the team began reporting stress, confusion, and a sense of constant pressure. From a Learning Experience Design perspective, processes that worked for a smaller team often do not scale without adjustment. The team’s capacity their available time, attention, and cognitive bandwidth did not expand in line with expectations. Role ambiguity and overlapping responsibilities created duplication of effort and accountability gaps. Here came an opportunity to redesign the team’s capacity and learning ecosystem rather than simply redistribute tasks. Key interventions included: 🔍 Conduct a Capacity Audit: Every task, meeting, and reporting requirement was analyzed to identify bottlenecks, duplication, and low-value activities. 📌 Prioritize Strategic Work: Non-essential tasks were delegated or removed. Core responsibilities aligned with business impact were clearly highlighted. ⚙️ Redesign Processes: Reporting templates were streamlined, recurring meetings reduced, and approvals standardized to reduce friction. 💡 Embed Reflection and Learning: Weekly “team retrospectives” were introduced, where team members shared wins, challenges, and lessons learned, enabling process improvement and knowledge transfer. 🧩 Clarify Roles and Responsibilities: Each team member’s tasks and ownership were mapped, eliminating overlap and increasing accountability. The results were striking. Performance stabilized as team members could focus on fewer, high-impact activities. Engagement increased 💪 because individuals felt their work mattered, and they had the space to contribute strategically rather than simply execute. Teams are more than output machines they are human systems. Rapid expansion can overwhelm these systems if we fail to consider capacity, clarity, and reflection. Designing growth with empathy and learning in mind ensures that teams remain motivated, skilled, and aligned. Ultimately, success comes not from doing more, but from doing better, together 🤝. #microlearning #learningeveryday #learningwithhiral #LearningExperienceDesign #EmployeeEngagement #Leadership #TeamDevelopment #ContinuousLearning #TeamCollaboration #LeadershipDevelopment

🌱 Why Ethical and Reasonable Goals Matter When setting goals, it's easy to focus solely on ambition and outcomes, but have you considered the impact of how you achieve them? Adding Ethical and Reasonable to your goal-setting framework ensures you’re not just chasing results but doing so in a way that builds trust, protects integrity, and drives sustainable success. Ethical goals align with your core values, fostering fairness and long-term credibility with stakeholders. Reasonable goals ensure your ambitions are grounded in reality, preventing burnout, resource waste, or unattainable expectations. That’s where SMARTER's goals come in, building upon the traditional SMART framework by adding ethical and reasonable elements to the mix. Here’s how it works: ☑ Specific Clearly define what you want to achieve. Example: "Increase customer retention by 10% over the next quarter." ☑ Measurable Track progress with clear metrics. Example: "Monitor customer retention rates using our CRM system and aim for a 10% improvement." ☑ Achievable Set realistic goals, given your resources and constraints. Example: "Train the customer service team to improve communication skills within the next two months." ☑ Relevant Ensure the goal aligns with broader organizational objectives. Example: "This retention improvement supports our larger goal of boosting annual revenue by 15%." ☑ Time-bound Define a clear deadline for achieving the goal. Example: "Achieve the target by March 31, 2025." ☑ Ethical Uphold fairness and integrity in your approach. Example: "Improve retention rates without misleading marketing or unfair pricing tactics." ☑ Reasonable Balance ambition with feasibility, considering constraints and risks. Example: "Set a 10% improvement target instead of an unrealistic 30%, given the current team size and budget." Incorporating Ethical and Reasonable into your goals ensures that your strategy supports sustainable growth while aligning with your values and resources. It’s about achieving meaningful results in a way that benefits everyone involved. If this resonates with you, follow me for more practical insights like this 🙌

𝗬𝗼𝘂 𝘁𝘂𝗻𝗲 𝘆𝗼𝘂𝗿 𝗺𝗼𝗱𝗲𝗹 𝗽𝗲𝗿𝗳𝗲𝗰𝘁𝗹𝘆 – 𝗯𝘂𝘁 𝗶𝘁 𝗰𝗼𝗹𝗹𝗮𝗽𝘀𝗲𝘀 𝘄𝗵𝗲𝗻 𝗕𝗹𝗮𝗰𝗸 𝗙𝗿𝗶𝗱𝗮𝘆 𝗵𝗶𝘁𝘀.🧙♂️ “Demand forecasting” sounds like one problem. But it’s at least two – and they need different solutions. For example: 1. Daily demand forecasting for the complete product range. Thousands of items, every day, across all locations. We often use algorithms like gradient boosting, deep learning – and yes, even “standard” regressions. The challenge: include everything – price, seasonality, trends, stock levels – and keep it stable without overfitting. The risk? These models tend to learn the average. Peaks often get smoothed out or missed entirely. 2. Then there’s peak event forecasting for holidays, promos, or major events. Totally different game. We need models built to target the spikes – that recognize events and adjust dynamically. They might not be the best at modeling the average though! But they’re better at capturing outliers and extremes. Sometimes lightweight time series models do better here. Or quantile regressions combined with external signals. The goal: anticipate sales behavior when it breaks the usual patterns. My word of caution? Assuming the same model can handle both. This is a great reminder to check early what your business actually needs forecasting for. #ALDITechfluencer #DataScience #DemandForecasting

Your team missed forecast by $1.4M last quarter. You're about to put 2 reps on PIPs and post 3 new headcount reqs. That will cost you roughly $180K in recruiting, ramp, & severance. And none of it will fix the actual problem. Before you touch the roster, pull up four numbers. 1. Pipeline coverage by rep, trailing 90 days. If coverage is above 3x but conversion is dropping, your reps have enough at-bats. They're just swinging wrong, which is a COACHING deficit. Your manager is running pipeline reviews but skipping deal strategy sessions. They're asking "what's the update" instead of "where's this deal vulnerable and what's your plan for the CFO." 2. Avg ramp time for hires in the last 12 months versus the 12 months before that. If ramp is stretching, your onboarding infrastructure is degrading. Usually because the manager who used to ride along on every new hire's first 10 calls now has 11 reps instead of 7, and those ride-alongs quietly stopped around rep number 9. 3. Forecast variance by team (vs by rep). If one manager's team consistently calls their number within 5% and another's team swings 20%, that gap has nothing to do with talent distribution. The accurate team has a manager running structured deal reviews with exit criteria at every stage. The volatile team has a manager who asks reps "how confident are you" and writes down whatever they say. 4 Rep attrition by tenure. If you're losing people in months 8-14, they're leaving because they stopped getting developed. The first six months had structure. Ride-alongs, coaching cadences, weekly skill drills. After that, they got a weekly 1:1 that turned into a "lemme know if you need anything!" convo and nothing more. Four symptoms. All of them show up as rep performance problems on a dashboard. All of them trace back to manager capacity. If you think this sucks now, this will only get worse as you scale. Every rep you add without manager capacity compounds the degradation. - Your 8th rep gets 80% of the coaching your 4th rep got. - Your 12th rep gets closer to 40%. - And that 12th rep is the one who misses number in Q3 and becomes the PIP conversation that should have been a manager hiring conversation six months earlier. I'm not suggesting that replacing people doesn't have its place when fixing a team. It does. But IMO the first thing you should be doing is asking whether the person who's supposed to be building them has the bandwidth to actually do the job. Headcount solves a coverage problem, for sure. Manager capacity, meanwhile, solves everything else.

Demand forecasting errors silently bleed profits and cash. This document shows 7 red flags in demand forecasting and how to fix them: 1️⃣ Over-reliance on historical data ↳ How to fix: incorporate external data like market trends, competitor activity, and consumer sentiment to enrich forecasts 2️⃣ Ignoring promotions and discounts ↳ How to fix: build a promotions-adjusted forecasting model, considering historical uplift from similar campaigns 3️⃣ Forgetting cannibalization effects ↳ How to fix: model cannibalization effects to adjust forecasts for existing products 4️⃣ One-size-fits-all forecasting method ↳ How to fix: use demand segmentation (for example, high variability vs. stable demand); do not treat all SKUs equally 5️⃣ Not monitoring forecast accuracy ↳ How to Fix: track metrics like MAPE, WMAPE, bias, to improve over time 6️⃣ High forecast error with no accountability ↳ How to fix: tie accountability to S&OP (sales and operations) meetings 7️⃣ Past sales (instead of demand) consideration ↳ How to fix: make the initial predictions based on the unconstrained demand; not on sales that are impacted by cuts and out of stock situations Any others to add?

Nonprofit Department Heads - here's a critical mistake to avoid when submitting your department plan and budget this year. It's committing yourself - and your team - to work beyond your true capacity. Listen, ambition is important. We're out here trying to solve - or at least stem the tide on - big issues for the people and places we serve. Dreaming of a better world and working to make it a reality are hallmarks of our sector. But you know what doesn't work? → Overpromising and underdelivering. → Overworking your team because you focused more on the potential capacity represented by your new department org chart than on your team's actual capacity. → Setting goals based on "making the Board happy" rather than reality. → Using the "in a perfect world" approach to planning rather than one grounded in your current reality. Exacerbating staff turnover by overcommitting is destabilizing your team and putting you even further behind on your big goals. Failing to recognize and account for the ongoing high staff turnover rates in the nonprofit sector as you plan is a huge mistake. If you need help understanding your true capacity, start here: Know your vacancy rate. The vacancy rate is: • The number of vacant positions on your team • Divided by the total number of positions on your team • Multiplied by 100 So, if your department has: • 5 open positions • Out of 23 total positions • Your vacancy rate is 𝟮𝟮% Here's why it matters: When you're making your plans and budget, are you: • Creating workloads and investments with a full team in mind? • Or are you dialing it back by at least 22%? I say "at least" because there's still family and disability leave, onboarding time, mandatory training, collaboration across the organization, and other important elements that need to be factored in. As department heads, it's critical to align our aspirations with our capabilities - and to bring a practical understanding of the progress we can make with the people, tools, and resources we actually have. We need to care less about what looks good on paper and instead focus on what works well in real life. Knowing our team's true capacity - so we don't start from a place of overcommitment - is essential to creating plans and budgets we can actually deliver on. #nonprofit #leadership #management #OrganizationalEffectiveness #TeamEffectiveness ---- Hi, I'm Veronica LaFemina. As a strategic advisor to nonprofit CEOs and department leaders, this is one of the ways I help - enabling you to explore the bigger strategic questions facing your organization, avoid common pitfalls, and navigate the day-to-day complexities of nonprofit leadership. Ready to get the right support to help you and your team succeed? Send me a DM and we'll set up time to connect. If this post resonated with you, be sure to follow me here on LinkedIn, where I write about practical approaches to improving the ways we think, plan, and work.

Sprint Capacity Planning: Committing to What Your Team Can Actually Deliver Many Agile teams miss sprint commitments not due to poor execution, but because of unrealistic planning. Sprint Capacity Planning ensures teams commit to what they can deliver — not what they hope to deliver. → What Is Sprint Capacity Planning? It’s the process of calculating how much work a team can realistically complete in a sprint, considering: Team size and sprint duration Planned leaves and holidays Support activities and meetings Risk and contingency buffers The goal isn’t to maximize workload — it’s to make commitments that improve predictability, quality, and confidence. --Example (Simplified) Team Size: 7 developers Sprint Duration: 10 working days Leaves & Holidays: 4 days total Support Activities: 20% capacity Risk Buffer: 10% capacity 1) Total capacity = 7 × 10 = 70 person‑days 2) After leave/holidays = 70 − 4 = 66 person‑days 3) Reserve 20% for support = 66 − 13 = 53 person‑days 4) Add 10% risk buffer = 53 − 5 = 48 person‑days That’s the team’s realistic delivery capacity for the sprint. → Why It Matters for Business Analysts Business Analysts help ensure sprint commitments are achievable by: Clarifying requirements and acceptance criteria Identifying dependencies and risks Supporting prioritization discussions Aligning stakeholder expectations with team capacity A sprint should be planned based on availability and priorities, not pressure. → Benefits of Effective Capacity Planning Realistic sprint commitments Better predictability and delivery accuracy Reduced burnout and rework Improved stakeholder confidence Higher‑quality outcomes → Key Takeaway Sprint Capacity Planning isn’t about how much work a team can take on — it’s about how much they can successfully complete. Great Agile teams don’t commit to the maximum; they commit to the right amount based on capacity, priorities, and confidence. Plan with capacity. Deliver with confidence. #BusinessAnalyst #Agile #Scrum #SprintPlanning #CapacityPlanning #AgileTeams #BusinessValue #ContinuousImprovement #ProjectManagement #ProductDevelopment

See how easily you can project monthly volumes, predict your business's revenue patterns with precision and plan your production and budget accordingly. Understanding and calculating the seasonality of your revenue can transform how you manage your financial planning. Why Measure Average Volume Demand? Measuring the average volume demand helps you identify patterns in your demand over different periods. By recognizing these patterns, you can adjust your forecasts and budgets to reflect more accurate expectations, preventing potential issues like overcapacity or underproduction. Steps to Calculate Average Seasonality: 1. Collect Data: Gather historical revenue data for multiple years. 2. Calculate Monthly Averages: Determine the average revenue for each month across the years. 3. Compute Overall Average: Find the overall average revenue across all months and years. 4. Determine Seasonal Indices: Divide each monthly average by the overall average to get the seasonal index for each month. Benefits of Applying Seasonal Indices: • Prevent Overcapacity: By anticipating peak periods, you can manage resources better and avoid production bottlenecks. • Optimize Production: Ensure that production schedules align with demand, reducing waste and improving efficiency. • Enhanced Forecast Accuracy: More precise forecasts lead to better financial planning and decision-making. This technique is not only useful when creating monthly budgets and forecasts, but also when crafting long range plans. When we apply the monthly seasonality to the yearly projection, we are able to achieve a granularity that will show us more clearly other aspects of our plan that we are not able to see from the yearly perspective. The capacity constraint is one example. In this case, I have this insight even years ahead to either increase capacity, improve capacity distribution along the year (if possible) or even plan better the volume production. To help you get started, I've created an Excel template for calculating seasonality. You can download it from the link below and integrate it into your budgeting process. https://buff.ly/44WU3tV