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业务案例 AI 编排

AI 编排 · 采购

在制药集团的生产网络中实现规模化采购自动化

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

客户
拥有多个生产基地的制药集团
地区
Europe
项目类型
咨询项目
范围
Source-to-Pay · Operational-demand integration · AI orchestration · Exception handling · Governance
Scientist beside a process development pilot plant

完整案例目前提供英文版本。

01 — 背景

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 — 范围

  • 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.

OREDJA 方法

  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 — 成果

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 — 投资回报与节约

  • 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.

AI 编排

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

涉及的服务

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

AI 编排

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采购能源与公用事业

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

水务公司的 AI 辅助寻源

一家受监管的公用事业公司通过数百份无人通读的合同采购着相同的服务与物料。OREDJA 以 AI 辅助分析合同库,基于证据重建品类策略,并自动化寻源活动的准备。范围内品类实现了 8% 的节约。

客户
欧洲供水与污水处理公司
项目类型
咨询项目

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