May 2026·13 min read

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# How Infoveave Built an Operational PSI and Forecast Commitment Platform for a Japanese B2B Electronics Distributor

PSI (Purchase-Sales-Inventory) planning is the operational framework Japanese B2B distributors use to balance incoming supply, outbound sales, and the inventory sitting between them. Forecast liability is the buyer's minimum contractual purchase commitment within that framework — the floor below which a buyer cannot reduce their purchases without financial consequence. This case study describes how Infoveave built an operational PSI platform: a full-cycle system that replaces spreadsheet-based planning with systematic liability and obligation tracking, multi-OEM inventory projection, and auditable approval governance.

A leading Japanese B2B electronics components distributor needed more than analytics — they needed an operational planning system. Managing forecast commitments and inventory positions across multiple OEM customers through disconnected processes left the organization with no unified view of their contractual exposure or supply risk. Infoveave built and operates the **Infoveave PSI system**: a full-cycle operational PSI platform handling daily SAP data ingestion, multi-OEM forecast entry and approval workflows, bilateral liability and obligation tracking, and real-time inventory projection — all within a single Infoveave deployment.

  
## Who Is This Case Study About?

The client is a trading organization for a major Japanese electronics components group, distributing components manufactured in Japan to a strong B2B customer base of leading companies that source raw materials through them. Get the forecast wrong and you pay twice: excess inventory ties up working capital, and a supply shortfall means disputed sales and missed delivery windows. The forecast-to-inventory cycle is where margins are won or lost.

They manage commercial relationships with multiple OEM customers simultaneously, each with independent forecast schedules, price agreements, and contractual commitment parameters. Components carry lead times of several months — meaning a planning error made today creates a fulfillment or inventory exposure problem that cannot be corrected until the next procurement cycle. Before Infoveave, tracking these relationships required separate processes per customer — a fragile setup for an organization where inventory planning errors translate directly to financial exposure.

## Business Challenge: The Limits of Spreadsheet-Based PSI Planning

PSI (Purchase-Sales-Inventory) planning is the operational backbone of Japanese B2B distribution. The PSI formula — `INV(this month) = INV(prev month) + PO_received − SO_forecast` — governs how inventory evolves over time and drives every procurement and fulfillment decision. When this calculation lives in spreadsheets, even small errors compound across months and across customers.

Six problems, each manageable on its own, compounding when combined:

Each OEM customer relationship was tracked in a separate process. There was no unified view of inventory exposure across all customers for the same model — to get the full picture, someone pulled numbers from multiple places and assembled them manually.

Liability was calculated by hand. Determining the buyer's minimum purchase commitment for each Model × Customer combination across the rolling 13-week window was time-consuming and inconsistent. Different people working from the same source data sometimes arrived at different figures.

Obligation wasn't tracked at all. The supplier's minimum supply commitment — the mirror image of liability — existed in the contracts but nowhere in the operational system. The client had no view of what they were contractually required to deliver, only what they were owed.

Forecast drift required manual archaeology. Understanding how a forecast had shifted from n-8 to n-1 meant pulling multiple versions and comparing them side by side. No single system showed the movement.

SAP data required assembly before any planning work could start. Sales results, goods received, goods issued, and month-end stock came from separate files — pulled together manually every cycle, for every customer, before any analysis was possible.

There was no approval record. Forecasts could be modified without audit trail, which meant planning cycles sometimes ran on uncommitted or disputed numbers without anyone knowing.

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## What Infoveave Built: An Operational PSI Planning System

Infoveave's role in this engagement is not a BI deployment on top of an existing system. Infoveave **is** the operational system — the platform where forecasts are entered, approved, computed, and reviewed. The Infoveave PSI system runs as the client's primary PSI planning tool.

  
![Infoveave operational PSI planning system for B2B electronics distributor](https://cdn.infoveave.com/success-stories-images/supply-chain-electronics.png)   

### SAP Data Ingestion and Daily Compute

Six active SAP file types are ingested from FTP daily and processed by a scheduled compute job with no manual intervention:

| SAP Data File | What It Contains | Used In the PSI System | |---|---|---| | Sales Result | Invoiced quantities and amounts by customer and material | Invoice & Billed QTY/AMT with credit/debit netting | | Customer Master | Customer full names and codes | OEM customer identification | | Goods Issued | Sales orders shipped (delivery-confirmed) | SO SAP QTY/USD actuals | | Goods Received | Purchase orders received from manufacturers | PO SAP QTY/USD actuals | | Material Master | Department codes and material group hierarchy | Category Levels 1–3 for filtering | | Month-End Stock | On-hand inventory at month end | L/M INV QTY/AMT |

All monetary values are normalized to USD using a Currency Master that stores date-ranged purchase and sales exchange rates per currency pair. A Price Master holds unit prices per Model × Customer × Currency, with user-editable overrides to handle price changes mid-cycle.

The daily compute job reads all six SAP sources plus the three master files and writes derived fields — including PSI inventory projections per OEM customer — to the central `Forecast_Editor_Details` datasource. A 3-hourly snapshot and daily incremental backup preserve the full history of every forecast state.

### The Forecast Editor: PSI Planning With Approval Governance

The Forecast Editor is a transactional workflow screen — not a dashboard — where users enter, review, and approve forecasts for each Model × Customer combination.

**Pre-filled from SAP**: PO SAP QTY/USD (goods received), Invoice & Billed QTY/AMT (sales results, credit/debit netted), SO SAP QTY/USD (goods issued), month-end inventory (L/M INV QTY/AMT), unit prices from Price Master with automatic currency conversion.

**User-entered** (current and future months only): PO Forecast QTY, SO Forecast QTY per OEM customer (SO1/SO2/SO3), and unit price overrides where current prices differ from master.

**PSI inventory projection** computed automatically per customer per month:

```
SO1-INV(this month) = SO1-INV(prev month) + PO_SAP_QTY + SO_SAP_QTY − SO1_FORECAST_QTY

```

SO2 and SO3 run the same formula independently — giving a separate inventory position per OEM customer while drawing from the same physical stock pool.

**Multi-OEM visibility**: Up to three OEM customers (SO1, SO2, SO3) are tracked simultaneously per model on a single screen. For the primary OEM customer, full P/S/I data is shown; for other customers of the same model, SO1 Forecast QTY is visible to show how their demand interacts with shared inventory.

**Approval workflow**: Submitted forecast combinations are locked until a Manager approves. Input Users can submit and request changes; Managers approve, reject, or grant change access. Email notifications fire per Customer × Model change request. All actions are timestamped and attributed — providing a complete approval record for every forecast commitment.

### Seven Analytical Dimensions Across Two OEM Customer Implementations

The Infoveave PSI system serves multiple OEM customer implementations. Two are described here, each revealing different dimensions of forecast commitment risk.

#### Implementation 1: Forecast Variance, Liability, PMix, Supply Plan Variance, Consumption Variance

**Forecast Variance (n-1 to n-8)**: Tracks how the forecast for a given delivery month has evolved across the 8 months preceding it. The variance from n-1 (one month before) to n-8 (eight months before) shows whether the customer is consistently revising upward or downward — a key signal for procurement confidence and liability exposure. Large variance at n-1 indicates instability in short-range commitments; large variance at n-8 indicates long-range planning unreliability.

**Liability (13-week rolling window)**: The buyer's minimum contractual purchase commitment, calculated across a rolling 13-week window. As the delivery period approaches, the liability parameters decrease — reflecting the reduced ability to absorb forecast changes as the window closes. The calculation takes the MINIMUM of the new and old liability values, establishing a floor below which the buyer cannot reduce their commitment. Liability tracking gives the distributor continuous visibility into the minimum they can count on purchasing from each OEM customer, protecting against late-stage forecast cuts.

**PMix (Product Mix)**: Tracks whether the product mix within a forecast is consuming as planned. A customer may hold their total forecast volume steady while significantly shifting the mix between models — a change that can create inventory imbalance even when aggregate numbers appear healthy. PMix analysis makes this visible before it becomes a fulfillment problem.

**Supply Plan Variance**: Measures the gap between planned incoming supply and actual goods received. Where forecast variance tracks demand-side changes, supply plan variance tracks supply-side execution. Both are needed to understand whether inventory projections are reliable.

**Consumption Variance**: Measures whether actual demand (invoiced quantities) is absorbing the supply that was planned. A gap between supply received and consumption confirms that inventory is building — which triggers working capital concerns and liability exposure review.

#### Implementation 2: Obligation, Forecast Accuracy, PSI Inventory, Bilateral Supply Corridor

**Obligation**: The supplier's minimum contractual supply commitment — the bilateral counterpart to liability. Where liability parameters decrease over time (protecting the supplier by setting a buyer purchase floor), obligation parameters increase over time (protecting the buyer by setting a supplier delivery floor). Obligation takes the MAXIMUM of new and old calculated values. Together, liability and obligation define a **contractual supply corridor**: both parties are bound to minimum commitments, in opposite directions.

**Liability vs Obligation — The Two Sides of the Forecast Commitment Window**

| Dimension | Protects | Parameter Direction | Calculation Rule | |---|---|---|---| | Liability | Supplier | Decrease over time (e.g. 100%→60%) | Takes MINIMUM of new vs old | | Obligation | Buyer | Increase over time (e.g. 100%→140%) | Takes MAXIMUM of new vs old |

Without tracking both, an organization sees only one side of the corridor. The first implementation had liability visibility. The second added obligation — giving the bilateral picture that represents how these contracts actually work.

**Forecast Accuracy (Actual vs N%)**: Distinct from forecast variance (how the forecast changed), accuracy measures how close the final forecast was to actual invoiced quantities. The second implementation tracks Actual vs N% (accuracy against the most recent forecast) and Actual vs N-1% (accuracy against the prior month's forecast) — a retrospective quality signal that identifies which customers and models consistently over- or under-forecast.

**PSI Inventory Integration**: The second implementation integrates PSI result data directly into the commitment tracking layer. The rolling PSI inventory calculation (INV = prev INV + PO received − SO forecast) provides the inventory position that contextualizes both the liability and obligation exposure — answering not just "what are we committed to buy?" but "what will our inventory position be if we do?"

### Forecast Summary, Reporting, and Infoboard Visibility

The system surfaces PSI planning data through three layers:

**Forecast Summary Screen**: A read-only view of all Forecast Editor data with half-yearly column aggregation for planning horizon visibility. PSI inventory values (SO-INV) are displayed as point-in-time figures — not summed — because they carry forward from prior months and summing them would produce a meaningless number.

**Reports**: A customer-specific Forecast Report provides full detail per OEM customer relationship. A Forecast Without Customer report provides the model-level view of all PO, SO, and inventory values across customers — used when the aggregate supply picture matters more than the customer breakdown.

**Infoboard Widgets** (with Model, Customer, and Category Level 1 filter controls):

* PO(SAP) + Invoice & Billed + INV QTY — volume position
* PO(SAP) + Invoice & Billed + INV AMT — USD value position
* PO Forecast + SO1 Forecast + SO1-INV QTY — forward supply plan
* Sum of PO + SO + INV — consolidated PSI position

## Business Outcomes

The client moved from managing each OEM customer in a separate process to a single platform with consistent calculation logic across all of them. That consolidation was the prerequisite for everything else.

Most significant was what consolidation made possible: bilateral commitment tracking for the first time. Liability and obligation are now calculated simultaneously. Both sides of the contractual supply corridor are visible in one place — not one tracked and one ignored.

Forecast history across the full n-1 to n-8 window is now a standard view, not something you produce by pulling versions and comparing them. Accuracy tracking was added alongside variance: how much the forecast changed versus how accurate it ultimately was. Those are different questions. Before, they were conflated because both required manual effort to answer.

Governance is now auditable. Every forecast commitment has a named approver, a timestamp, and a change record. Uncommitted or disputed values cannot propagate through the system. The six SAP source files arrive daily and are processed automatically — the prepare-the-data step before any planning conversation is gone.

Two gaps that weren't in the original brief also closed. Product mix consumption is tracked against forecast (PMix), so inventory imbalance at the SKU level shows up before it becomes a fulfillment problem rather than after. And up to three OEM customers per model run simultaneously, giving the distributor visibility into how independent demand signals from different customers draw on the same inventory pool.

## Why an Operational PSI System Rather Than a BI Layer?

Most analytics deployments sit on top of an existing system — reading from it, visualizing it, alerting on it. This one didn't. Infoveave is the operational system here: the place where forecasts are entered, approved, computed, and reviewed.

That distinction matters for a distributor where the planning process itself was the problem. Better visibility into a spreadsheet-based process doesn't fix the spreadsheet. What the client needed was a system that could handle the calculation complexity, the multi-OEM structure, and the governance requirements that spreadsheets couldn't. Forecast entries, PSI projections, bilateral commitment calculations, SAP reconciliation, approval workflows — all of it runs in Infoveave. That's what was built.

---

**Related resources:**

* [Forecast Liability and Obligation Management](/solutions/industry/supply-chain/forecast-liability-management)
* [Supply Chain Analytics Solutions](/solutions/industry/supply-chain)
* [Digitizing Shopfloor Analytics with a Unified Data Platform](/resources/success-stories/digitizing-shopfloor-analytics-with-unified-data-platform)
* [Unified Data Management for Manufacturing](/resources/success-stories/unified-data-management-manufacturing)

### About the Authors

Anusha Morbad is a member of the Infoveave Product and Solutions Team — specialists in Unified data platforms, agentic BI, and enterprise analytics. Infoveave (by Noesys Software) helps organizations unify data, automate business processes, and act faster with AI-powered insights.

[Visit infoveave.com](https://infoveave.com)[Follow us on LinkedIn](https://www.linkedin.com/showcase/infoveave/)

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## Frequently Asked Questions

##### What is PSI planning and why does it matter for B2B distributors?

##### What is forecast liability in supply chain management?

##### What is forecast obligation and how does it differ from liability?

##### How does Infoveave handle multi-OEM supply planning?

##### What SAP data does the Infoveave PSI system ingest?

##### How does Infoveave's approval workflow support forecast governance?

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