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Hello world !

 The story of "Hello, World!" is deeply tied to the history of programming and computer science education. Here's a quick rundown of its origins and significance: 1. Origins in Early Programming The phrase "Hello, World!" first appeared in programming literature in the 1970s. It was popularized by Brian Kernighan in his book The C Programming Language (1978), co-authored with Dennis Ritchie , the creator of the C language. However, Kernighan had already used it in an earlier 1972 internal Bell Labs tutorial for the B programming language, a precursor to C. The first recorded "Hello, World!" example in B looked like this: main() { printf("hello, world\n"); } 2. Why "Hello, World!"? Simplicity : It's a small, easy-to-understand program that demonstrates basic syntax. Testing : It's often the first thing programmers write when learning a new language. Debugging : It ensures that the compiler and en...
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From Inventory Optimization to Profit: Accounting KPIs, Working Capital and Explainable Recommendations

A forecast that is accurate and an inventory policy that is efficient are still not the same as a business that is profitable. This second part adds the missing layer: turnover and accounting KPIs to measure the financial impact of inventory decisions, and explainability to show managers why the system recommends what it recommends. We continue with the same supermarket example. What you will learn The difference between sales turnover and inventory turnover, and how GMROI, inventory days, and working capital relate to each other. Why fill rate and cycle service level are different, with a worked example. How accounting numbers (margin, cost, salvage value) set the newsvendor critical fractile. How to compare policies financially, including marginal returns on extra inventory. How to explain forecasts (SHAP), order decisions, and financial trade-offs. Sales, stock, purchasing, accounting Demand forecast quantiles Simulate and optimize policies Financial and service KPIs Expla...
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Beyond Point Forecasts: A Step-by-Step Guide to Probabilistic Demand Forecasting and Inventory Simulation (a clear hook for data scientists)

Most demand forecasts end with a single number. Most inventory decisions depend on how wrong that number might be. This guide connects the two, step by step, using one running example: a supermarket deciding how many units of a product to order each day. What you will learn Why a point forecast is not enough for ordering decisions. How quantile regression predicts demand percentiles, and what pinball loss does. How the newsvendor formula turns a quantile into an order quantity. How Monte Carlo simulation compares inventory policies, and how to optimize them. Collect data sales, prices, stock Forecast demand quantiles of D Choose a policy (R, S) rule Simulate Monte Carlo Optimize search R and S Validate backtest, pilot Predict Decide Forecasting turns data into a distribution; simulation and optimization turn it into an order rule. Figure 1. The roadmap. The first two steps predict demand; the rest decide what to do about it. Part 1. The business problem Every day, a superma...
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