Welcome to Royfactory

Introduction TL;DR: Price elasticity measures how demand responds to price changes, but naive models fail due to endogeneity. Causal and ML-based designs estimate more accurate effects, and scenario simulations help evaluate pricing decisions across demand, revenue, and inventory. (본문은 위 한국어 구조에 대응해 영문으로 동일하게 구성) References (Dynamic modeling and forecasting of price elasticity based on time series analysis and machine learning, 2025)[https://eurekamag.com/research/100/036/100036654.php] (Introduction to price elasticity of demand, 2026-02-14)[https://lilys.ai/notes/1075036] (Price elasticity definitions, accessed 2026-02-14)[https://contents.kocw.or.kr/KOCW/document/2015/korea_sejong/kimmyeongki/04.pdf] (Dynamic Pricing - Causal AI Solutions, accessed 2026-02-14)[https://economicai.com/en-PH/solutions/dynamic-pricing] (Adventures in Demand Analysis Using AI, accessed 2026-02-14)[https://arxiv.org/abs/2501.00382] (Machine learning and operation research based method for promotion optimization, accessed 2026-02-14)[https://www.sciencedirect.com/science/article/abs/pii/S1567422319300912]

Introduction TL;DR: Claude Cowork is a desktop agent mode that can access a user-approved local folder and tools, execute multi-step tasks, and produce real files (docs/spreadsheets/slides). As of 2026-02-11, it’s a research preview available on Claude Desktop (macOS + Windows x64) for paid plans (Pro/Max/Team/Enterprise); Windows arm64 isn’t supported. Why it matters: Agentic power means operational risk. Treat Cowork as a governed tool, not a chat upgrade. What Claude Cowork is (and isn’t) One-sentence definition Claude Cowork is an agentic desktop mode that turns prompts into planned, executed tasks with direct file outputs in a user-approved workspace. ...

Introduction Intermittent Demand Forecasting is a dedicated production track for SKUs with frequent zeros. You should start with Croston-family baselines (Croston/SBA/TSB), then expand to zero-inflated count time-series models only when the data-generating mechanism demands it. TL;DR: Define what “zero” means (true no-demand vs stockout/censoring vs missing), split the pipeline into an intermittent track, and validate with inventory KPIs (service level/cost), not just forecast scores. Why it matters: In intermittent SKUs, average accuracy can look fine while stockouts/overstock explode in a small subset of items. ...

Introduction AI Sales Forecasting often starts with feature-based ML (GBDT). This lesson shows when to move to deep learning and how to use foundation models as fast baselines. TL;DR: pick models based on covariate availability, rolling backtests, calibrated uncertainty, and cost/latency. Why it matters: Deep learning only pays off when it reduces decision risk (stockouts/overstock) at an acceptable operational cost. 1) Model landscape (train-from-scratch vs pretrained) Train-from-scratch: DeepAR, TFT, N-HiTS, TiDE, PatchTST Pretrained foundation models: TimesFM, Chronos, TimeGPT Why it matters: Pretrained models accelerate baselining; train-from-scratch can fit your domain more tightly. ...

Introduction AI Sales Forecasting succeeds in production only if you design the operating loop: monitoring → diagnosis → retrain/rollback. Most failures come from broken inputs and silent distribution shifts, not from model math. TL;DR: Monitor (1) data quality, (2) drift/skew, and (3) post-label performance; then release via a registry with canary and rollback. Why it matters: Forecast labels are often delayed. Drift + data-quality monitoring becomes your early warning system. ...

Introduction TL;DR: AI Sales Forecasting must stay consistent across planning levels (total/category/SKU). The common production pattern is (1) generate base forecasts, then (2) apply forecast reconciliation (e.g., MinT) to enforce coherence. For new items, “cold-start” is solved by borrowing signal from hierarchies and similar items (metadata/content/price tiers). Promotions should be designed either as model features or as a separate uplift (counterfactual) estimation pipeline (e.g., CausalImpact/BSTS). Why it matters: Without coherence, different teams will operate on different numbers, breaking replenishment and planning alignment. ...

Introduction TL;DR: AI Sales Forecasting becomes valuable only when it drives ordering decisions. Build a lead-time (or protection-period) demand distribution, pick the right service metric (CSL vs fill rate), and set reorder point/order-up-to levels using quantiles. Avoid “adding daily P95s” to get a lead-time P95—use sample-path aggregation. For reliable uncertainty, calibrate prediction intervals (e.g., conformal forecasting). Why it matters: Forecast accuracy is not the objective; meeting service targets at minimal total cost is. ...

Introduction TL;DR: AI Sales Forecasting with feature-based ML turns time series into a supervised regression problem using lags/rolling stats, calendar signals, and exogenous variables. The winning recipe is: feature taxonomy → point-in-time correctness → rolling-origin backtests → WAPE → quantile forecasts. Why it matters: This approach scales across many SKUs/stores and stays maintainable when your catalog grows. 1) What “feature-based ML” means for sales forecasting Definition, scope, common misconception Definition: convert time series into a feature table (lags/rollings/calendar/exogenous) and fit a regressor (GBDT). Misconception: “GBDT can’t do time series.” It can, if the feature pipeline and validation are correct. Why it matters: Most failures come from leakage and bad validation, not from the model class. ...

Introduction TL;DR: AI Sales Forecasting must be evaluated using genuine forecasts on unseen data, not training residuals. Use rolling forecasting origin (rolling-origin CV) with explicit choices: horizon, step, window type, and refit policy. Report WAPE + MASE (and pinball loss for quantiles) and compare everything against two fixed baselines: seasonal naive + ETS. In this lecture-style part, you’ll build a backtest setup that matches deployment conditions and produces a decision-ready report. ...

Introduction TL;DR: AI Sales Forecasting often fails due to data semantics (schemas, time meaning, leakage), not model choice. Model your sources as sales + calendar + price + promo + inventory/stockouts, then build a stable training/inference view. Enforce point-in-time correctness for time-series feature joins to prevent leakage. Treat stockouts as censored demand and track them explicitly. In this Part 2, you’ll get a practical data model and validation rules you can lift into a warehouse/lakehouse. ...