Life sciences

Inventory Analytics with Databricks and dbt

PROVADIT’s contribution to a life-sciences data-platform modernization covers requirements, data modeling and implementation work for inventory and write-off analytics with Databricks and dbt. The work forms part of a wider delivery engagement.

Explore the case
Industry
Life sciences
Our role
Solution design and data-layer implementation within a wider delivery engagement
Focus & technology
Databricks · dbt · Data Modeling · Data Transformation
Scope
Inventory and write-off data layer · Requirements, models and implementation work

Client anonymized.

The starting point

How should inventory data be structured to support planning and valuation?

The case concerns a global life-sciences group’s transition from a data landscape shaped by SAP Business Warehouse toward a cloud-based Lakehouse. Inventory and write-off analysis required a data foundation connecting material movements, batches, inventory rules and financial valuation.

Inventory information crosses several boundaries: a physical movement, the batch involved, its business classification and its financial value. A new platform needs those relationships to remain intelligible. The work therefore starts with the meaning of the data and carries that meaning into models and transformations.

Our contribution

PROVADIT’s contribution

PROVADIT’s documented work covers the design and implementation of the inventory and write-off data layer using Databricks and dbt within a wider delivery engagement. The work combines business requirements, data-model design and SQL-based data transformation. Fabian Georg Rührnschopf authored requirements and model designs for material movements, batch master data, inventory classification and valuation information.

  1. Clarify the business logic

    Describe the information and rules needed for inventory analysis, including slow-moving stock and valuation adjustments.

  2. Design connected data models

    Specify material-movement, batch and valuation structures and how they relate to one another.

  3. Implement the data layer

    Build the transformations in dbt on Databricks, translating the agreed business rules into the inventory and write-off data layer.

Inside the work

Four connected views of inventory.

The design brings together the data domains needed for inventory and write-off analysis. The view below groups the documented scope; it is not a complete platform architecture.

  1. Movements

    Material-movement structures describe changes in inventory.

  2. Batches

    Batch master data adds the context needed to interpret those movements.

  3. Classification

    Inventory rules express the business interpretation, including slow-moving stock.

  4. Valuation

    Valuation information connects the physical stock view with its financial meaning.

Deliverables

The work delivered. The decisions it supports.

The engagement connects authored requirements and data models with implementation in Databricks and dbt. Its value as an example is the continuity between the business definition and the technical work. The case describes this defined contribution; the wider transformation has its own scope and responsibilities.

  • Documented business requirements for the data assets.
  • Data-model designs for material movements, batches and valuation information.
  • Implementation work on the inventory and write-off data layer in Databricks and dbt.

Engagement context

PROVADIT’s scope is the inventory and write-off data layer within the wider platform modernization. The broader finance transformation and overall platform rollout involve other teams and remain outside this case.

What this illustrates

A useful inventory model starts with agreed business definitions. Platform choices and data structures can then be assessed against the decisions they need to support.

Hands arranging planning documents and cards on a work table.

A related engagement

Inventory Optimization and Integrated Planning

Another case examines inventory steering and planning-tool design. It offers a complementary perspective on how reliable information supports planning decisions.

Read case study: Inventory Optimization and Integrated Planning

Related expertise

Explore all project examples

Build on clear inventory data definitions.

Discuss your inventory and valuation requirements, source systems and data-platform plans with our team.

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