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IBM Manufacturing Australia

Rinnai Australia

Real-time Analytics · Supply Chain Optimization

Rinnai Australia needed to simplify a fragmented finance environment built on multiple planning and reporting tools, spreadsheets, and manual processes so the company could speed reporting, improve collaboration, and prepare its data for AI use.

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10% targeted inventory reduction through demand planning
40 hours/week saved through automated reporting and consolidation
50% reduction in reliance on spreadsheets
cutting subsidiary reporting to 4 days

Reported impact

Business outcome

Rinnai reduced manual reporting work, cut spreadsheet dependence, improved visibility for finance and demand planning, and created an AI-ready planning platform that supports better inventory and working-capital decisions.

  • Cost Reduction
  • Productivity Improvement
  • Scalability

Solution

Rinnai selected IBM Business Partner Octane Software Solutions to modernize its finance platform with IBM Planning Analytics. The rollout consolidated month-end reporting, product-profitability analysis, demand planning, and a 2025 Budget Suite connected to sales, workforce planning, and capital expenditure into a single planning environment. That shared data model replaced siloed tools such as Cognos, TM1, Oracle Essbase, and Oracle Analytics Cloud, reducing spreadsheet dependence and giving finance teams a central source of truth for planning and consolidation. With the data foundation in place, Rinnai began using IBM watsonx for natural-language queries and outlier detection, and is applying predictive models to working capital and demand planning. The new architecture turns finance from a reporting bottleneck into a collaborative analytics function that can support faster decisions, inventory optimization, and future AI-driven forecasting improvements.

Data flow

Finance, sales, workforce, capital-expenditure, and demand-planning data are consolidated into IBM Planning Analytics, where shared models support reporting and budgeting; IBM watsonx then queries and analyzes that data for outlier detection and predictive finance scenarios.

Solution architecture

2 components · 2 layers
  1. Compute
    • IBM watsonx Adds natural-language querying and outlier-detection capabilities on top of Rinnai's modernized finance data foundation.
  2. Serving
    • IBM Planning Analytics Provides the shared finance-planning environment for reporting, profitability analysis, demand planning, and budgeting.

Architecture clues

  • A low-code approach kept internal teams engaged in adoption.
  • Rinnai is already using predictive models for working capital and outlier detection on the new data foundation.
  • The rollout was phased to deliver quick wins while minimizing risk.

Evidence from the source

40 hours of time savings per week
50% drop in reliance on spreadsheets
Rinnai is now leveraging predictive models for working capital, alongside IBM Planning Analytics and IBM watsonx for natural language queries and outlier detection.
The deployment has streamlined month-end reporting, product profitability analysis, demand planning, and introduced a 2025 Budget Suite integrated with sales, workforce planning, and capital expenditure.
Using IBM Planning Analytics, Octane led a phased rollout that delivered quick wins while minimizing risk.
Transparency Evidence & methodology 95/100 reference value · 89% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value95/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    4 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. IBM

    Rinnai Australia customer story

    First-party
    https://www.ibm.com/case-studies/rinnai
    Source type
    Vendor-published customer story
    Published
    Dec 16, 2025
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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