نظرة سريعة
السياق، الحاجة، وما الذي يغيره الحل.
سياق العمل
عرض البيانات
الحاجة
Traditional AR reporting often treats the ledger balance as the complete receivables position.
That becomes misleading when collections include uncleared cheques, aging depends on due dates, cheque status changes over time and the same customer position must be understood across different accounts and business dimensions.
The core problem was therefore not building another receivables dashboard. It was creating a consistent financial model for exposure, collections and cheque lifecycle so every KPI could be traced back to the same business logic.
ما الذي يقدمه الحل
يتجاوز هذا المشروع فكرة عرض ارصدة العملاء فقط، ليبني طبقة تحليلية محكومة تساعد الادارة على فهم مكان تركز الذمم المدينة، وما هو متاخر منها، وما تم تحصيله، وما يزال مرتبطا بشيكات غير محصلة، وما هي الجهات والعملاء والعوامل التي تقف خلف هذا الوضع.
يتم دمج وتجهيز بيانات الحسابات والعملاء والشيكات والحسابات المالية باستخدام SQL Server وSSIS، بينما تتولى طبقة منطق مالي محكومة احتساب التعرض المالي، وتقادم الديون، ودورة حياة الشيكات، والتحصيل، والارصدة الدائنة، وCash DSO، وتحليل الشيكات المرتجعة.
ثم يتم تقديم نفس المنطق التحليلي من خلال Tableau وPower BI، بما يسمح للادارة بالانتقال من الصورة الاجمالية للذمم المدينة الى تحليل العملاء والمناطق والحسابات ومندوبي المبيعات وبقية العوامل المؤثرة في النتائج.
01
لماذا كانت هناك حاجة لهذا الحل
منظومة تحليلات الذمم المدينة والتحصيل | Tableau + Power BI
Traditional AR reporting often treats the ledger balance as the complete receivables position.
That becomes misleading when collections include uncleared cheques, aging depends on due dates, cheque status changes over time and the same customer position must be understood across different accounts and business dimensions.
The core problem was therefore not building another receivables dashboard. It was creating a consistent financial model for exposure, collections and cheque lifecycle so every KPI could be traced back to the same business logic.
02
كيف يعمل الحل
I treated the AR position as a financial state that had to be reconstructed from transaction history rather than read from a single balance field.
The model separates ledger exposure from uncleared instruments, evaluates aging by due date, follows cheque movement through its lifecycle and keeps collections, Cash DSO, bounced cheque rate, credit balances and debt-source analysis aligned to the selected reporting period.
The analytical logic is handled before the visualization layer so Tableau and Power BI do not create separate versions of the financial truth. Where repeated calculations would be expensive, prepared analytical structures and indexed snapshots reduce the work required at dashboard runtime.
The result is one governed AR and collections model that can support different BI experiences without redefining the business logic in each one.
I built the data foundation in Microsoft SQL Server and SSIS, integrating AR transactions, customer data, cheque activity, transaction types and ledger-account structures into finance-ready analytical data.
The implementation covers historical position logic, customer exposure, due-date aging, collections, customer credit balances, Cash DSO, bounced cheque analysis, cheque lifecycle and closing exposure by debt source.
The original management experience was developed in Tableau with period, region, classification, related-account and salesperson analysis, supported by exposure composition, aging, collection trends, top-customer analysis, cheque positions and dynamic performance breakdowns.
I later delivered the same AR and collections intelligence through Power BI, demonstrating that the reporting experience can change while the underlying financial definitions remain governed and consistent.
تقنيات داعمة
- Microsoft SQL Server
- SQL Server Integration Services (SSIS)
- ETL
- Tableau
- Power BI
03
ما الذي تغير
I built the data foundation in Microsoft SQL Server and SSIS, integrating AR transactions, customer data, cheque activity, transaction types and ledger-account structures into finance-ready analytical data.
The implementation covers historical position logic, customer exposure, due-date aging, collections, customer credit balances, Cash DSO, bounced cheque analysis, cheque lifecycle and closing exposure by debt source.
The original management experience was developed in Tableau with period, region, classification, related-account and salesperson analysis, supported by exposure composition, aging, collection trends, top-customer analysis, cheque positions and dynamic performance breakdowns.
I later delivered the same AR and collections intelligence through Power BI, demonstrating that the reporting experience can change while the underlying financial definitions remain governed and consistent.
