Choosing the right chart is half the battle in data storytelling. This one visual helped me go from âðð¡ð¢ðð¡ ðð¡ðð«ð ðð¨ ð ð®ð¬ð?â â âðð¨ð ð¢ð ð¢ð§ 10 ð¬ððð¨ð§ðð¬.âð ððð«ðâð¬ ð ðªð®ð¢ðð¤ ðð«ððð¤ðð¨ð°ð§ ð¨ð ð¡ð¨ð° ðð¨ ðð¡ð¨ð¨ð¬ð ðð¡ð ð«ð¢ð ð¡ð ðð¡ðð«ð ððð¬ðð ð¨ð§ ð²ð¨ð®ð« ðððð: ð¹ ðð¨ð¦ð©ðð«ð¢ð¬ð¨ð§? ⢠Few categories â Bar Chart ⢠Over time â Line Chart ⢠Multivariate â Spider Chart ⢠Non-cyclical â Vertical Bar Chart ð¹ ððð¥ððð¢ð¨ð§ð¬ð¡ð¢ð©? ⢠2 variables â Scatterplot ⢠3+ variables â Bubble Chart ð¹ ðð¢ð¬ðð«ð¢ðð®ðð¢ð¨ð§? ⢠Single variable â Histogram ⢠Many points â Line Histogram ⢠2 variables â Violin Plot ð¹ ðð¨ð¦ð©ð¨ð¬ð¢ðð¢ð¨ð§? ⢠Show part of a total â Pie Chart / Tree Map ⢠Over time â Stacked Bar / Area Chart ⢠Add/Subtract â Waterfall Chart ðð®ð¢ðð¤ ðð¢ð©ð¬: ⢠Donât overload charts; less is more. ⢠Always label axes clearly. ⢠Use color intentionally, not decoratively. ⢠ðð¬ð¤: What insight should this chart unlock in 5 seconds or less? ððð¦ðð¦ððð«: ⢠Charts donât just show data, they tell a story ⢠In storytelling, clarity beats complexity ⢠Donât aim to impress with fancy visuals, aim to express the insight simply, thatâs where the real impact is ð¡ â»ï¸ Save it for later or share it with someone who might find it helpful! ð.ð. I share job search tips and insights on data analytics & data science in my free newsletter. Join 14,000+ readers here â https://lnkd.in/dUfe4Ac6
Supply Chain Management
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Forecasting is hard. Finding analysts who do it well is even harder. Too often, I see forecasting either: 1. Overcomplicated: Applying complex ML models just to predict a moving average (?!), or 2. Oversimplified: Running regressions without understanding what the coefficients even mean. I personally use 4 forecasting methods to model a range of outcomes, from conservative to aggressive: 1. ARIMA - Smooths time series data, w/o seasonality adjustment. 2. SARIMAX - Like ARIMA, but accounts for seasonality. Likely to be the safest and conservative forecast. 3. Prophet - Captures non-linear trends and seasonality. Often the most accurate. My favorite model for growth forecasts. 4. Manual Projection â aka Olga's secret, overly complicated manual projection. I plot every available metricâs historical D/D, W/W, M/M, and Y/Y % change and analyze their: (a) correlations and relationships (b) seasonal thresholds. It takes ages to complete, but it delivers the most precise forecast. If done right. If I can account for everything the teams are doing. Which is rarely the case. ð¬ When reporting, I typically present only Prophet alongside my Projection, keeping ARIMA and its variations for myself as checks. There are many time series models out there: MA, AR, ARMA, ARIMA, SARIMA, Exponential Smoothing, VAR, and more. Forecasts are fun.
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Yesterdayâs sales canât see tomorrowâs storm, But AI can ð Most manufacturers still build demand forecasts based on one thing: ð¡ð¢ð¬ðð¨ð«ð¢ððð¥ ð¬ðð¥ðð¬. Which is fine⦠until the market shifts. Or weather changes. Or a social post goes viral. (Which is basically always.) Thatâs why AI is changing the forecasting game. Not by making predictions perfectâjust a lot less wrong. And a little less wrong can mean a lot more profitable. According to the Institute of Business Forecasting, the average tech company saves $ðððð per year by reducing under-forecasting by just 1%, and another $ð.ðð by trimming over-forecasting. For consumer product companies, those same 1% improvements are worth $ð.ðð (under-forecasting) and $ð.ððð (over-forecasting). (Source: https://lnkd.in/e_NJNevk) And were are only talking 1 improvement%!!! Let that sink in... All that money just from getting a little better at predicting what customers will actually buy. And yes, AI can help you get there: ⢠By ingesting external signals (weather, social, events, IoT, etc.) ⢠By recognizing nonlinear patterns that Excel never will ⢠And by constantly learningâunlike your spreadsheet But itâs not just about tech. Itâs about process: ⢠Use Forecast Value-Added (FVA) to track which steps help (or hurt) ⢠Get sales, marketing, and ops aligned in S&OPânot working in silos ⢠Focus on data qualityâAI is only as smart as your ERP is clean ⢠Plan continuouslyâforecasting is not a set-it-and-forget-it task Bottom line: If youâre still relying on history to predict the future, youâre underestimating the cost of being wrong. Your competitors arenât. ******************************************* ⢠Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends ⢠Ring the ð for notifications!
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One of the biggest challenges in data visualization is deciding ð¸ð©ðªð¤ð© chart to use for your data. Hereâs a breakdown to guide you through choosing the perfect chart to fit your dataâs story: ð¦ ðð¼ðºð½ð®ð¿ð¶ðð¼ð» ððµð®ð¿ðð If youâre comparing different categories, consider these options: - Embedded Charts â Ideal for comparing across ð®ð¢ð¯ðº ð¤ð¢ðµð¦ð¨ð°ð³ðªð¦ð´, giving you a comprehensive view of your data. - Bar Charts â Best for fewer categories where you want a clear, side-by-side comparison. - Spider Charts â Great for showing multivariate data across a few categories; perfect for visualizing strengths and weaknesses in radar-style. ð ððµð®ð¿ðð ð³ð¼ð¿ ðð®ðð® ð¢ðð²ð¿ ð§ð¶ðºð² When tracking changes or trends over time, pick these charts based on your data structure: - Line Charts â Effective for showing trends across ð®ð¢ð¯ðº ð¤ð¢ðµð¦ð¨ð°ð³ðªð¦ð´ over time. Line charts give a sense of continuity. - Vertical Bar Charts â Useful for tracking data over fewer categories, especially when visualizing individual data points within a time frame.   ð© ð¥ð²ð¹ð®ðð¶ð¼ð»ððµð¶ð½ ððµð®ð¿ðð To reveal correlations or relationships between variables: - Scatterplot â Best for displaying the relationship between ðµð¸ð° ð·ð¢ð³ðªð¢ð£ðð¦ð´. Perfect for exploring potential patterns and correlations. - Bubble Chart â A go-to choice for three or more variables, giving you an extra dimension for analysis. ð¨ ðð¶ððð¿ð¶ð¯ððð¶ð¼ð» ððµð®ð¿ðð Understanding data distribution is essential for statistical analysis. Use these to visualize distribution effectively: - Histogram â Best for a ð´ðªð¯ð¨ðð¦ ð·ð¢ð³ðªð¢ð£ðð¦ with a few data points, ideal for showing the frequency distribution within a dataset. - Line Histogram â Works well when there are many data points to assess distribution over a range. - Scatterplot â Can also illustrate distribution across two variables, especially for seeing clusters or outliers. ðª ðð¼ðºð½ð¼ðð¶ðð¶ð¼ð» ððµð®ð¿ðð Show parts of a whole and breakdowns with these: - Tree Map â Ideal for illustrating hierarchical structures or showing the composition of categories as part of a total. - Waterfall Chart â Perfect for showing how individual elements contribute to a cumulative total, with additions and subtractions clearly represented. - Pie Chart â Suitable when you need to show a single share of the total; use sparingly for clarity. - Stacked Bar Chart & Area Chart â Both work well for visualizing composition over time, whether youâre tracking a few or many periods. ð¡ Key Takeaways - Comparing across categories? Go for bar charts, embedded charts, or spider charts. - Tracking trends over time? Line or bar charts help capture time-based patterns. - Revealing relationships? Scatter and bubble charts make variable correlations clear. - Exploring distribution? Histograms or scatter plots can showcase data spread. - Showing composition? Use tree maps, waterfall charts, or pie charts for parts of a whole.
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Imagine strolling down a street in China and spotting a small, bright-yellow electric van humming along - completely driverless! These so-called âLittle Yellow EVsâ are part of a new approach to last-mile delivery, the crucial (and often most expensive) final stretch of getting packages or meals right to your doorstep. Equipped with self-driving technology, these compact vehicles aim to cut labor costs, reduce delivery times, and shrink carbon footprints. But why is this such a big deal? Traditional delivery methods often involve multiple handoffs and extra steps that slow things down and add expenses. Autonomous vehicles operating on sidewalks or bike lanes can simplify the process, boosting efficiency and freeing up human couriers for more complex tasks. Plus, the use of electric power helps lower emissions - an increasingly important goal in busy urban areas. If these pilot programs continue to thrive, itâs likely that youâll start seeing similar driverless delivery vans in other cities around the globe. Of course, questions about safety, regulations, and public acceptance remain - technology moves fast, but communities need to keep pace with smart policies and trust-building measures. Have you come across any self-driving delivery vehicles in your neighborhood yet? #innovation #technology #future #management #startups
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As supply chain managers look towards manufacturing industry verticals that may see volume growth in 2024, in addition to looking at trends in industrial production indexes (which measure physical unit output), it is also useful to look at the Federal Reserve Boardâs capacity utilization indexes for these industries. The reason is simple: an industry that is operating at a high level of capacity utilization likely has little upward room for output growth given strains placed on equipment and labor. To illustrate, below are two capacity utilization indexes from the FRB. Thoughts: â¢The top index shows seasonally adjusted capacity utilization for plastics & rubber products manufacturing (https://lnkd.in/gPYc_GMb). Capacity utilization has fallen very sharply starting in Q4 2022 and has yet to recover. Looking at year-over-year industrial production (https://lnkd.in/gB3PaQKu), the decline has been about 5%. Thus, if conditions improve, we may could see an increase in output of ~5% as an upper bound (best-case scenario). â¢The bottom index shows capacity utilization for nonmetallic mineral product manufacturing (https://lnkd.in/gYe4piEh). Capacity utilization since early 2022 has been running 8-10 percentage points above 2018 and 2019 levels, suggesting a sector where we have little room for additional growth in output unless additional capacity is added. Thus, while industrial production has remained strong (https://lnkd.in/gtgD-YM4), I see fewer opportunities for output volume to grow in 2024 even if demand manifests (since these factories are likely to begin to encounter supply constraints). â¢For anyone interested, here is a link (https://lnkd.in/gy2ukdvr) to all the capacity utilization indexes the FRB publishes. Implication: augmenting industrial production data measuring output with capacity utilization data can provide a more comprehensive picture of how a manufacturing sector is performing and its potential to increase output if demand conditions were to improve. More free competitive intelligence data to incorporate into strategic decision making (whether as a trucking manager or sourcing professional trying to anticipate supplier lead times). #supplychain #supplychainmanagement #manufacturing #economics #freight #trucking
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ðððð¥ðððð¢ð§ð ð¨ð§ ðð¥ð¥ ðð¡ð ð¬ð®ð©ð©ð¥ð¢ðð«ð¬ ðâð¯ð ð¬ð¨ð®ð«ððð, ð¨ð§ð ðð¡ð¢ð§ð ð¢ð¬ ðð¥ððð«: ð©ð«ð¨ððð¬ð¬ ð¦ððððð«ð¬. Taking shortcuts can lead to wasted money and a world of headaches downstream. (ðð¢ðªð´ð¦ ðºð°ð¶ð³ ð©ð¢ð¯ð¥ ðªð§ ðºð°ð¶'ð·ð¦ ð¦ð·ð¦ð³ ð£ð¦ð¦ð¯ ð¢ð´ð¬ð¦ð¥ ðµð° ð§ð¢ð´ðµ-ðµð³ð¢ð¤ð¬ ððð ð³ð¦ð²ð¶ðªð³ð¦ð®ð¦ð¯ðµð´, ð°ð³ ð©ð¢ð¥ ðð¦ð¢ð¥ð¦ð³ð´ ð±ð¶ð´ð© ð§ð°ð³ ð¤ð¦ð³ðµð¢ðªð¯ ð´ð¶ð±ð±ððªð¦ð³ð´, ðªð¨ð¯ð°ð³ðªð¯ð¨ ð®ð¢ðµð¦ð³ðªð¢ð ð³ðªð´ð¬ð´?!) ðð¡ðð ð'ð¯ð ð¥ððð«ð§ðð: ð¡ ðð¤ððªð¨ ððð§ð¨ð©: Be specific about your needs in RFx docs. If youâre unclear, suppliers will be, too. Before going to RFP, always have quantifiable evaluation criteria finalized and approved by the Spend Owner. ð¡ ðð©âð¨ ð£ð¤ð© ððªð¨ð© ð¥ð§ððð: The cheapest option often costs the most in the long run. Prioritize value over price. Suppliers who price things materially lower than benchmark norms usually cut corners somewhere to meet margins. ð¡ ð¾ðððð ð§ðððð§ðð£ððð¨ ð©ðð¤ð§ð¤ðªððð¡ð®: Source independent references via your network. Past performance tells the real story. Ask the right questions and listen closely to the answers. ð¡ ðððð£ð ððððð: Can the supplier grow and evolve with your business? Are they innovative and flexible? Does their company culture and ways of working align with yours? ð¡ ðð£ð¤ð¬ ð©ðð ð§ðð¨ð ð¨: Most suppliers come with some level of risk, the key is understanding and managing it. Conduct due diligence on short-listed suppliers. Outputs should inform the down-selection process, with material deficiency action items included in the contract. ð¡ ð¾ðð¤ð¤ð¨ð ð¥ðð§ð©ð£ðð§ð¨, ð£ð¤ð© ð«ðð£ðð¤ð§ð¨: The best suppliers care about your long-term success and aligning with your goals.  Look at proposals holistically, thinking beyond the transaction and into value creation. ððð«ðâð¬ ðð¡ð ðð¡ð¢ð§ð : Looking back, Iâve been at firms in seasons where costs were prioritized over total value, often leading to short-term gains but long-term challenges. There were times I shouldâve taken a firmer stance about material supplier risks identified and bias in the selection process. As procurement peeps, we provide recommendations based on long-term value, risk management, and partnership potential. This includes having the courage to speak up with informed and actionable guidance when things don't pass muster. The goal is to ensure sourcing outcomes build a foundation for success, not just a quick win. ð¢ ð.ð. ðððð© âð¨ððð¤ð¤ð¡ ð¤ð ððð§ð ð ð£ð¤ðð ð¨â ð¨ð¤ðªð§ððð£ð ð¡ðð¨ð¨ð¤ð£ð¨ ð¬ð¤ðªð¡ð ð®ð¤ðª ð¨ððð§ð ð¬ðð©ð ð®ð¤ðªð§ ð®ð¤ðªð£ððð§ ð¥ð§ð¤ððªð§ðð¢ðð£ð© ð¨ðð¡ð?
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I watched a robot deliver food from a restaurant two blocks away. It was ridiculous and SO F**KING COOL! Who is shaping the future of autonomous food delivery? Coco: The new OpenAI partnership and fresh $122M in Series B funding for enhanced path planning lays the foundation for market dominance Manna Air Delivery: 3-minute drone deliveries are proving the speed advantage Wing: Multi-modal partnerships (see: Serve Robotics collab) are expanding their addressable market Nuro: Licensing pivot + deepening relationships with Uber highlights strategic focus to become the foundational autonomous vehicle technology provider Starship Technologies: With 8M+ deliveries; scaling from 50 campuses to 150 cities globally shows sustainable execution Zipline: Remains the drone delivery heavyweight with restaurant partnerships pushing beyond traditional medical deliveries Several key categories define the autonomous food delivery market: â Sidewalk Delivery Robots: Small autonomous robots designed for short-distance deliveries in pedestrian areas â Road-Based Autonomous Vehicles: Larger autonomous delivery vehicles capable of operating on public roads â Hybrid Remote-Operated Systems: Robotics solutions combining autonomous navigation with remote human oversight â Multi-Modal Delivery Platforms: Integrated systems combining various autonomous delivery methods with traditional logistics â Indoor/Controlled Environment Robots: Specialized robots for deliveries within buildings, hospitals, and controlled facilities â Drone Delivery Integration: Aerial autonomous delivery systems for rapid food delivery Market leaders in each category are emerging. But, while the market leaders are gaining commercial traction, winning key partnerships, and attracting funding, several players, including once-promising names are struggling to deliver (pun intended). In a market that once was betting on promise, execution is now table stakes. What recent highlights tell us about the evolution of the market: â³Market leaders are now making millions of deliveries with 99% autonomy; proving scalability â³Major platforms (Uber, DoorDash) are all-in with partnerships, driving adoption and revenue to fuel the next wave of innovation â³Tech advancements and maturation are enabling the market shift from confined, controlled pilots to complex urban deployments â³Investors are willing to write (big) checks to companies that are proving commercial traction with Nuro, Coco, Manna, and Neolix all raising fresh rounds this year We're witnessing the transition from âoh, look a robotâ to "scalable last-mile infrastructure." 2025 is shaping up to be the year your Uber Eats or DoorDash driver isnât a driver at all. P.S. Want more insights on the companies building the future of food delivery? Comment "insights delivered" below for *free* access to CB Insights' data and insights on the autonomous food delivery markets.
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Hiring good people is just the start. Onboarding well is the key to keeping them. The truth about weak onboarding: ⳠIt costs you 2-3x more in the long run ⳠCreates unnecessary imposter syndrome ⳠBreeds preventable mistakes ⳠKills momentum before it starts What strong onboarding actually looks like: 1. Structured First 90 Days ⢠Clear milestones and wins ⢠Regular check-in rhythm ⢠Progressive responsibility increase 2. Support System That Works ⢠Dedicated mentor assignments ⢠Cross-team introductions ⢠"Stupid question" channels 3. Resources Ready Day 1 ⢠Updated documentation ⢠Tool access pre-configured ⢠Team processes explained 4. Learning Built Into The Schedule ⢠Protected learning blocks ⢠Practice environments ⢠Feedback loops Stop expecting people to "figure it out." Start investing in their success. The best companies know: A slow start beats a false start. What was your best (or worst) onboarding experience? ⻠Share if you believe in better onboarding
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âHappy (Product) Returnsâ - new cartoon and post https://lnkd.in/gvumTSby âFree Returnsâ has become the new âFree Shipping,â which is creating a massive logistical headache for retailers, particularly in the weeks after Christmas. This year, shoppers in the US returned 14.5% of the items they purchased, valued at $743 billion. Thatâs nearly double the return rates of pre-pandemic 2019. One third of shoppers now make returns part of their shopping strategy, buying multiple items, knowing theyâll return some later. As Gartner retail analyst Tom Enright put it in the WSJ a few days ago, âweâre headed for a trillion dollar problem here.â The economics of product returns are brutal. The WSJ reported that only 30% of all returned items are resold and Enright estimates that retailers are losing 50% of their margins on returns. A recent survey from logistics company goTRG found that 49% of retailers believe product returns are a âsevere problemâ, up from 2% just a few years ago. That has led some retailers like Zara and H&M to start cracking down on returns this year with shorter return windows, return fees, and even âkeep itâ policies. This will be a tricky challenge for brands and retailers to navigate. When shoppers have been trained to expect âFree Returnsâ as table stakes, itâs hard to pull back. And the returns process is every bit a part of the customer experience as the purchase. Like unsustainable price promotions, I think that âFree Returnsâ are emblematic of the ârace-to-the-bottomâ dynamic in retail. What starts as a point of difference becomes background noise. This is a good reminder for brands and retailers to think about what they stand for beyond the lowest price or biggest deal. For related cartoons and all the links in this post, click here: https://lnkd.in/gvumTSby To sign up for my weekly marketoon email newsletter, click here: https://lnkd.in/g9DBM6tD #marketing #cartoon #marketoon