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دراسات الحالة تنسيق الذكاء الاصطناعي

تنسيق الذكاء الاصطناعي · المشتريات

أتمتة المشتريات على نطاق واسع عبر شبكة الإنتاج لمجموعة صيدلانية

AutomatedRequisitions created, checked and routed without manual entry, across the group’s production sites

العميل
مجموعة صيدلانية بعدة مواقع إنتاج
المنطقة
Europe
نوع المهمة
مهمة استشارية
النطاق
Source-to-Pay · Operational-demand integration · AI orchestration · Exception handling · Governance
Scientist beside a process development pilot plant

دراسة الحالة الكاملة متاحة حاليًا باللغة الإنجليزية.

01 — الوضع

The group manufactures on several regulated sites, each with its own maintenance planning, production scheduling and local purchasing habits. Consumables, spare parts, packaging components and laboratory supplies were requisitioned by hand: part numbers copied from equipment records and bills of materials, quantities estimated, delivery points typed.

Buyers spent their days converting requisitions into purchase orders and correcting them. Errors in part numbers, quantities and delivery points were frequent, urgent requests jumped the queue, and the quality system required every step to be traceable.

02 — النطاق

  • requisition-to-order for maintenance, production and laboratory materials across all sites, on the Source-to-Pay platform already in place.
  • Read operational demand where it originates: released maintenance work orders, production schedules and bills of materials.
  • Check contract coverage, stock and delivery point, create the purchase order and route it by value and risk, with human validation on exceptions only.
  • Respect the quality system and internal audit: decision rights, thresholds, validated steps and an audit trail for every automated action.

منهج أوريدجا

  1. 01

    Diagnostic

    Requisition and order flows analysed with process mining across the sites: volumes, touch points, rework and delays by category and by site.

  2. 02

    Design

    An orchestration layer between maintenance, production planning and the Source-to-Pay platform: demand reading, contract and stock checks, order creation and exception routing, with thresholds agreed with Finance and Quality.

  3. 03

    Pilot

    One site, one category, every automated order validated by a person for four weeks, then sampling. Exceptions reviewed daily and rules adjusted, with the validation documented in the quality system.

  4. 04

    Scale

    Site by site over two quarters, with the same rules and a shared exception queue, and buyers redeployed to supplier management, contracts and critical materials.

03 — الأثر

Orders now leave the platform within minutes of a work order or production run being released, with the right part, quantity and delivery point. People handle exceptions, and only exceptions.

Source-to-Pay runs as one governed flow from operational demand to payment. Buyers manage suppliers, contracts and critical materials; planners have stopped typing requisitions altogether.

04 — العائد والوفورات

  • RemovedManual requisition entryRequisitions created from operational demand; people validate exceptions only
  • One flowSource-to-PayDemand reading, contract check, approval and ordering in a single governed process on the existing platform
  • ImprovedProcess reliabilityPart-number, quantity and delivery-point errors caught as exceptions before ordering; every action traceable
  • RedeployedBuyer timeFrom transaction processing to suppliers, contracts and critical materials

Outcomes are stated qualitatively: no quantitative result is published for this programme.

تنسيق الذكاء الاصطناعي

  • Next: predictive replenishment of critical spares and consumables from equipment and production data.
  • Supplier risk signals on the materials most exposed to single sources.

ما شملته المهمة

  • Process mining
  • AI orchestration design
  • Source-to-Pay automation
  • Governance and controls
  • Change management

تنسيق الذكاء الاصطناعي

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حالات أخرى

المشترياتالطاقة والمرافق

8%Savings realised through AI-assisted sourcing and contract intelligence, against the utility’s own baseline

توريد مدعوم بالذكاء الاصطناعي لدى مرفق مياه

كان مرفق منظَّم يشتري الخدمات والمواد نفسها عبر مئات العقود التي لم يقرأها أحد كاملة. أخضعت أوريدجا قاعدة العقود لتحليل مدعوم بالذكاء الاصطناعي، وأعادت بناء استراتيجيات الفئات على الأدلة، وأتمتت إعداد المناقصات. تحققت وفورات بنسبة ثمانية بالمئة في الفئات المشمولة.

العميل
مرفق أوروبي للمياه والصرف الصحي
نوع المهمة
مهمة استشارية

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