التنبؤ بالطلب على المخزون

المتطلبات تصف قدرات يحتاجها النمط في بيئتك، مو الموردين اللي لازم تشتريهم. أي نظام يملأ متطلباً يحقّقه — وهذا اللي يخلّي الكتالوج قابلاً للنقل عبر الذيل الطويل من أدوات الشركات الصغيرة والمتوسطة.

1. sales_history_corpus

   مطلوبقراءةدفعة

   Historical sales data at the granularity that drives forecasting: per SKU, per location, per time period.

   شكل البيانات

   Sales records with SKU, location, quantity, date, sale price, optionally customer segment.
   يُملأ عادةً بواسطة

   • sales transactions from the e-commerce platform
   • POS data from retail systems
   • ERP sales orders
   • subscription billing data for recurring products
2. inventory_state

   مطلوبقراءةطلب

   Current and historical inventory positions: what's on hand, what's on order, what's in transit.

   شكل البيانات

   Per SKU per location: on-hand quantity, allocated quantity, open POs with expected arrival.
   يُملأ عادةً بواسطة

   • inventory management system
   • ERP inventory module
   • warehouse management system
3. product_master

   مطلوبقراءةطلب

   SKU-level information that drives forecasting and reordering: lead time from supplier, MOQ, shelf life, cost.

   شكل البيانات

   Per-SKU: supplier, lead time, MOQ, unit cost, ABC classification, lifecycle stage.
   يُملأ عادةً بواسطة

   • product catalog in ERP
   • purchasing system with supplier and lead-time data
   • master product list maintained by operations
4. external_signal_inputs

   موصى بهقراءةمجموعة

   Things that affect demand but aren't visible in historical sales alone: known promotions, weather, holidays, planned events.

   شكل البيانات

   Calendar of events with date ranges and expected demand multipliers (positive or negative) per SKU group.
   إذا مفقود

   Forecasts work well for steady-state demand but miss promo bumps and seasonal anomalies. Accuracy 15-25% worse on event-driven products.
   يُملأ عادةً بواسطة

   • marketing calendar with planned campaigns
   • annual events the business runs
   • weather feed for weather-sensitive products
5. recommendation_destination

   مطلوبكتابةدفعة

   Where reorder recommendations land for the buying team to review and act on.

   شكل البيانات

   Per SKU recommendation: order quantity, suggested order date, forecast supporting it, confidence range, alternative scenarios.
   يُملأ عادةً بواسطة

   • review queue in the purchasing module
   • weekly recommendation report
   • dashboard with sortable recommendations
6. buying_outcome_loop

   موصى بهقراءةحدث

   Feedback on which recommendations the buyer accepted, modified, or rejected. Used to tune forecasts.

   شكل البيانات

   Per-recommendation: was it acted on, what was actually ordered, what was the buyer's reasoning if different.
   إذا مفقود

   Pattern can't learn from buyer expertise. Recommend at least minimal capture of accept/reject.
   يُملأ عادةً بواسطة

   • accept/reject log captured at recommendation time
   • PO data fed back from the purchasing system
   • weekly buyer feedback meeting captured in notes