# Tensor Analytics

> GrepEye modules by Tensor Analytics: Supply Chain for demand and inventory planning, Invo for invoice management, and DealerPulse for dealership intelligence.

## Site index

- [llms.txt](https://www.tensoranalytics.ai/llms.txt): Concise site summary for LLMs
- [sitemap.xml](https://www.tensoranalytics.ai/sitemap.xml): Machine-readable URL index
- [robots.txt](https://www.tensoranalytics.ai/robots.txt): Crawler policy

## Pages

- [Tools and checklists](https://www.tensoranalytics.ai/resources): Finance mix calculator, downloadable templates, and operational buying guides.
- [Editorial policy](https://www.tensoranalytics.ai/editorial-policy): Authorship, sources, AI assistance, examples, updates, and corrections.
- [Home](https://www.tensoranalytics.ai/): Tensor Analytics and GrepEye overview.
- [GrepEye Supply Chain](https://www.tensoranalytics.ai/grepeye): Supply chain planning, forecasting, inventory, and S&OP.
- [GrepEye Invo](https://www.tensoranalytics.ai/grepeye/invo): Invoice capture, finance review, approval, and configured accounting connections.
- [GrepEye DealerPulse](https://www.tensoranalytics.ai/grepeye/dealerpulse): Dealership sales, finance, inventory, teams, and operations on iOS and Android.
- [Security & Trust Center](https://www.tensoranalytics.ai/security): RBAC, audit trails, encryption, compliance roadmap, and data handling.
- [Solutions](https://www.tensoranalytics.ai/solutions): Data infrastructure, ML forecasting, S&OP console, rules and AI agents.
- [Industries](https://www.tensoranalytics.ai/industries): Discrete manufacturing, pharma, food and beverage, consumer goods.
- [Work](https://www.tensoranalytics.ai/work): Illustrative planning and inventory workflows; not verified customer outcomes.
- [prompt/ed](https://www.tensoranalytics.ai/prompt-ed): Knowledge base, cookbook, learning track, and community.
- [prompt/ed Cookbook](https://www.tensoranalytics.ai/prompt-ed/cookbook): Step-by-step planning recipes with worked examples.
- [prompt/ed Learning Track](https://www.tensoranalytics.ai/prompt-ed/learn): Structured course on planning foundations for manufacturing.
- [About](https://www.tensoranalytics.ai/about): Tensor Analytics mission, team, and principles.
- [Contact](https://www.tensoranalytics.ai/contact): Book a 45-minute GrepEye walkthrough.
- [Privacy Policy](https://www.tensoranalytics.ai/privacy): How Tensor Analytics handles personal data.
- [Terms of Service](https://www.tensoranalytics.ai/terms): Terms for using tensoranalytics.ai and GrepEye.
- [Cookie Policy](https://www.tensoranalytics.ai/cookies): Cookie and tracking technology policy.

## Content pillars

- [Demand forecasting](https://www.tensoranalytics.ai/prompt-ed/pillar/forecasting): Articles about demand forecasting.
- [S&OP & IBP](https://www.tensoranalytics.ai/prompt-ed/pillar/sop): Articles about s&op & ibp.
- [Inventory & supply](https://www.tensoranalytics.ai/prompt-ed/pillar/inventory): Articles about inventory & supply.
- [AI in operations](https://www.tensoranalytics.ai/prompt-ed/pillar/ai-ops): Articles about ai in operations.
- [Data & integration](https://www.tensoranalytics.ai/prompt-ed/pillar/data): Articles about data & integration.
- [The AI Hype Hangover](https://www.tensoranalytics.ai/prompt-ed/pillar/hype-hangover): Articles about the ai hype hangover.
- [Invoice management](https://www.tensoranalytics.ai/prompt-ed/pillar/invoices): Articles about invoice management.
- [Dealership operations](https://www.tensoranalytics.ai/prompt-ed/pillar/dealerships): Articles about dealership operations.

## Articles

Guides cover demand planning, S&OP, inventory, invoice workflows, dealership reporting, AI in operations, and data integration.

### Tally invoice integration: a checklist for finance teams
- URL: https://www.tensoranalytics.ai/prompt-ed/tally-invoice-integration-checklist
- Type: concept | Pillar: Invoice management | Date: 2026-09-15
- Author: Tensor Analytics | Read time: 3 min
- Tags: Invoice management, Finance operations, India
- Summary: Scope an invoice-to-Tally connection: company mapping, ledgers, voucher rules, approvals, duplicate prevention, failure recovery, and reconciliation.
### Supply netting vs reorder point planning: a worked example
- URL: https://www.tensoranalytics.ai/prompt-ed/supply-netting-vs-reorder-point-planning
- Type: concept | Pillar: Inventory & supply | Date: 2026-09-15
- Author: Tensor Analytics | Read time: 3 min
- Tags: Inventory planning, Supply netting
- Summary: Understand how supply netting and reorder points differ, with an inventory example covering usable stock, receipts, allocations, and order multiples.
### Offline invoice capture for field teams: design the handoff
- URL: https://www.tensoranalytics.ai/prompt-ed/offline-invoice-capture-field-teams
- Type: concept | Pillar: Invoice management | Date: 2026-09-15
- Author: Tensor Analytics | Read time: 3 min
- Tags: Invoice management, Finance operations, India
- Summary: Plan a mobile invoice capture workflow for patchy connectivity, with clear submission states, preserved originals, finance review, and practical pilot checks.
### Multi-company invoice approvals: routing and authority checklist
- URL: https://www.tensoranalytics.ai/prompt-ed/multi-company-invoice-approval-workflow
- Type: concept | Pillar: Invoice management | Date: 2026-09-15
- Author: Tensor Analytics | Read time: 3 min
- Tags: Invoice management, Finance operations, India
- Summary: Design invoice approval responsibilities across businesses, with a sample routing matrix, second-approval checks, and accounting handoff controls.
### A multi-branch dealership performance scorecard
- URL: https://www.tensoranalytics.ai/prompt-ed/multi-branch-dealership-performance-scorecard
- Type: concept | Pillar: Dealership operations | Date: 2026-09-15
- Author: Tensor Analytics | Read time: 3 min
- Tags: Dealership analytics, Branch reporting, India
- Summary: Build a comparable outlet scorecard across sales, finance mix, collections, inventory, and team actions, with consistent definitions and weighted group totals.
### Invoice OCR vs invoice approval automation: what is the difference?
- URL: https://www.tensoranalytics.ai/prompt-ed/invoice-ocr-vs-invoice-approval-automation
- Type: concept | Pillar: Invoice management | Date: 2026-09-15
- Author: Tensor Analytics | Read time: 3 min
- Tags: Invoice management, Finance operations, India
- Summary: Learn where invoice data extraction ends and finance approval begins, with a practical review workflow and examples of errors OCR alone cannot resolve.
### Invoice management software in India: from capture to approval
- URL: https://www.tensoranalytics.ai/prompt-ed/invoice-management-software-india
- Type: concept | Pillar: Invoice management | Date: 2026-09-15
- Author: Tensor Analytics | Read time: 3 min
- Tags: Invoice management, Finance operations, India
- Summary: Compare invoice management workflows for Indian finance teams: mobile capture, OCR review, approval controls, business routing, and Tally handoffs.
### Forecast pilot data checklist: what to prepare before a demo
- URL: https://www.tensoranalytics.ai/prompt-ed/forecast-pilot-data-checklist
- Type: concept | Pillar: Demand forecasting | Date: 2026-09-15
- Author: Tensor Analytics | Read time: 3 min
- Tags: Demand planning, Software evaluation
- Summary: Prepare demand history, item-location masters, inventory, open orders, lead times, and approval rules for a demand forecasting pilot.
### Duplicate invoice checks: a practical finance review checklist
- URL: https://www.tensoranalytics.ai/prompt-ed/duplicate-invoice-detection-checklist
- Type: concept | Pillar: Invoice management | Date: 2026-09-15
- Author: Tensor Analytics | Read time: 3 min
- Tags: Invoice management, Finance operations, India
- Summary: Review suspected duplicate invoices using supplier identity, document numbers, dates, amounts, original images, and accounting references.
### Demand planning software for manufacturers: a buyer’s guide
- URL: https://www.tensoranalytics.ai/prompt-ed/demand-planning-software-manufacturers-buyers-guide
- Type: concept | Pillar: Demand forecasting | Date: 2026-09-15
- Author: Tensor Analytics | Read time: 3 min
- Tags: Demand planning, Software evaluation
- Summary: Evaluate demand planning software using your planning grain, forecast horizon, inventory constraints, approval workflow, and a measurable pilot.
### Demand planning ERP integration: a practical checklist
- URL: https://www.tensoranalytics.ai/prompt-ed/demand-planning-erp-integration-checklist
- Type: concept | Pillar: Data & integration | Date: 2026-09-15
- Author: Tensor Analytics | Read time: 3 min
- Tags: ERP integration, Data quality
- Summary: Plan a reliable ERP handoff for demand forecasts and inventory decisions, including identifiers, approvals, acknowledgements, retries, and reconciliation.
### Dealership management dashboards in India: what to measure
- URL: https://www.tensoranalytics.ai/prompt-ed/dealership-management-dashboard-india
- Type: concept | Pillar: Dealership operations | Date: 2026-09-15
- Author: Tensor Analytics | Read time: 3 min
- Tags: Dealership analytics, Branch reporting, India
- Summary: Evaluate a dealership dashboard across sales, finance mix, commissions, receivables, inventory, and branch performance without losing metric context.
### Dealership inventory ageing dashboards: from buckets to action
- URL: https://www.tensoranalytics.ai/prompt-ed/dealership-inventory-ageing-dashboard
- Type: concept | Pillar: Dealership operations | Date: 2026-09-15
- Author: Tensor Analytics | Read time: 3 min
- Tags: Dealership analytics, Branch reporting, India
- Summary: Define vehicle inventory ageing consistently, compare unit counts and value, and turn ageing buckets into a branch-level action queue.
### How to calculate dealership finance penetration
- URL: https://www.tensoranalytics.ai/prompt-ed/dealership-finance-penetration-calculation
- Type: concept | Pillar: Dealership operations | Date: 2026-09-15
- Author: Tensor Analytics | Read time: 3 min
- Tags: Dealership analytics, Branch reporting, India
- Summary: Calculate in-house finance penetration correctly, distinguish it from finance share of all sales, and handle cash, unknown categories, and zero denominators.
### Dealership finance commission and receivables: a reconciliation guide
- URL: https://www.tensoranalytics.ai/prompt-ed/dealership-commission-receivables-reconciliation
- Type: concept | Pillar: Dealership operations | Date: 2026-09-15
- Author: Tensor Analytics | Read time: 3 min
- Tags: Dealership analytics, Branch reporting, India
- Summary: Separate expected dealership finance commission from receipts, reconcile adjustments, and build a practical follow-up queue with clear ownership.
### DealerPulse vs DMS reporting: choosing the right layer
- URL: https://www.tensoranalytics.ai/prompt-ed/dealerpulse-vs-dms-dealership-reporting
- Type: concept | Pillar: Dealership operations | Date: 2026-09-15
- Author: Tensor Analytics | Read time: 3 min
- Tags: Dealership analytics, Branch reporting, India
- Summary: Understand the difference between dealership transaction systems and a mobile leadership dashboard, and evaluate how DealerPulse fits your existing setup.
### Module 8: Audit, compliance, and the board pack
- URL: https://www.tensoranalytics.ai/prompt-ed/module-8-audit-compliance-board-pack
- Type: module | Pillar: AI in operations | Date: 2026-07-08
- Author: Kislay S | Read time: 13 min
- Tags: Learning track, Foundations, Audit trail, Compliance, Board pack
- Summary: The final module of the Planning Foundations course. How to close the planning cycle with a defensible record. Audit trails, regulatory compliance, and the board pack that communicates the plan to leadership.
### Module 7: AI agents and human-in-loop
- URL: https://www.tensoranalytics.ai/prompt-ed/module-7-ai-agents-human-in-loop
- Type: module | Pillar: AI in operations | Date: 2026-07-01
- Author: Kislay S | Read time: 12 min
- Tags: Learning track, Foundations, AI agents, Human-in-loop, Automation
- Summary: The seventh module of the Planning Foundations course. How AI agents assist with the planning cycle, what human-in-loop actually means, and why the agent proposes and the planner disposes.
### Module 6: Scenario planning and what-ifs
- URL: https://www.tensoranalytics.ai/prompt-ed/module-6-scenario-planning-what-ifs
- Type: module | Pillar: S&OP & IBP | Date: 2026-06-24
- Author: Kislay S | Read time: 12 min
- Tags: Learning track, Foundations, Scenario planning, What-if, Overlays
- Summary: The sixth module of the Planning Foundations course. How to model what-if scenarios without disrupting the baseline plan. The overlay approach, comparison, and promotion.
### Module 5: Supply netting and inventory policy
- URL: https://www.tensoranalytics.ai/prompt-ed/module-5-supply-netting-inventory-policy
- Type: module | Pillar: Inventory & supply | Date: 2026-06-17
- Author: Dhiraj S | Read time: 13 min
- Tags: Learning track, Foundations, Supply netting, Safety stock, Inventory
- Summary: The fifth module of the Planning Foundations course. How to turn the locked demand plan into replenishment orders. Supply netting, safety stock, reorder points, and the gap analysis that prevents stockouts.
### Module 4: The S&OP cycle and sequential gates
- URL: https://www.tensoranalytics.ai/prompt-ed/module-4-sop-cycle-sequential-gates
- Type: module | Pillar: S&OP & IBP | Date: 2026-06-10
- Author: Kislay S | Read time: 14 min
- Tags: Learning track, Foundations, S&OP, Sequential gates, Consensus
- Summary: The fourth module of the Planning Foundations course. How the S&OP process turns a forecast into a published plan. Sequential gates, consensus, cycle lock, and why most S&OP cycles take three days when they should take four hours.
### Module 3: Forecast accuracy metrics
- URL: https://www.tensoranalytics.ai/prompt-ed/module-3-forecast-accuracy-metrics
- Type: module | Pillar: Demand forecasting | Date: 2026-06-03
- Author: Dhiraj S | Read time: 13 min
- Tags: Learning track, Foundations, MAPE, WMAPE, Bias
- Summary: The third module of the Planning Foundations course. MAPE, WMAPE, bias, and forecast value added. How to measure accuracy so the number actually reflects reality.
### Module 2: Statistical forecasting models
- URL: https://www.tensoranalytics.ai/prompt-ed/module-2-statistical-forecasting-models
- Type: module | Pillar: Demand forecasting | Date: 2026-05-27
- Author: Kislay S | Read time: 14 min
- Tags: Learning track, Foundations, Statistical models, ETS, ARIMA
- Summary: The second module of the Planning Foundations course. Exponential smoothing, ARIMA, and seasonal decomposition. What each model actually does, when to use it, and when it will fail.
### Module 1: Foundations of demand planning
- URL: https://www.tensoranalytics.ai/prompt-ed/module-1-foundations-of-demand-planning
- Type: module | Pillar: Demand forecasting | Date: 2026-05-20
- Author: Kislay S | Read time: 12 min
- Tags: Learning track, Foundations, Demand planning, Course
- Summary: The first module of the Planning Foundations course. What demand planning actually is, who owns it, how it connects to the rest of the business, and the vocabulary you need before the rest of the course makes sense.
### Your ERP data is stale. Here is how to fix it without a multi-year migration.
- URL: https://www.tensoranalytics.ai/prompt-ed/erp-data-stale-fix-without-migration
- Type: concept | Pillar: Data & integration | Date: 2026-05-13
- Author: Dhiraj S | Read time: 9 min
- Tags: Data quality, ERP, Master data management, Import
- Summary: ERP data quality is the root cause of most planning failures. The fix is not a new ERP. It is a data layer that validates, deduplicates, and versions every record before it reaches your planning tool.
### Human-in-the-loop is not a checkbox. It is a system property.
- URL: https://www.tensoranalytics.ai/prompt-ed/human-in-the-loop-system-property
- Type: concept | Pillar: AI in operations | Date: 2026-05-06
- Author: Keshab S | Read time: 9 min
- Tags: AI agents, Human-in-loop, Compliance, Audit trail
- Summary: Most vendors claim human-in-loop. Very few implement it. The difference is whether the human has real choice at the decision point, with enough information and time to make a different decision. Here is how to tell the difference.
### Audit trails for AI: what DPDP, CCPA, and the EU AI Act actually require
- URL: https://www.tensoranalytics.ai/prompt-ed/audit-trails-ai-dpdp-ccpa-eu-ai-act
- Type: concept | Pillar: AI in operations | Date: 2026-04-29
- Author: Keshab S | Read time: 12 min
- Tags: Compliance, Audit trail, DPDP Act, CCPA, EU AI Act
- Summary: Three regulatory frameworks now require audit trails for automated decisions. The requirements overlap but are not identical. Here is what each requires, where they differ, and what to implement if you operate in all three jurisdictions.
### The safety stock formula is not wrong. It is misused.
- URL: https://www.tensoranalytics.ai/prompt-ed/safety-stock-formula-misused
- Type: concept | Pillar: Inventory & supply | Date: 2026-04-22
- Author: Dhiraj S | Read time: 10 min
- Tags: Inventory, Safety stock, Reorder points, Service level
- Summary: Every supply chain textbook prints the safety stock formula. Most practitioners apply it without checking whether its assumptions hold for their SKUs. When the assumptions fail, the formula produces confident nonsense.
### Why scenario planning fails: the overlay problem
- URL: https://www.tensoranalytics.ai/prompt-ed/scenario-planning-overlay-problem
- Type: concept | Pillar: S&OP & IBP | Date: 2026-04-15
- Author: Kislay S | Read time: 7 min
- Tags: S&OP, Scenario planning, What-if analysis, Planning
- Summary: Most scenario planning tools copy the entire baseline plan and let you edit the copy. This breaks the moment the baseline changes. The fix is sparse overlays, and almost no tool does it correctly.
### The 4-hour S&OP cycle is not a goal. It is a consequence of design.
- URL: https://www.tensoranalytics.ai/prompt-ed/four-hour-sop-cycle
- Type: concept | Pillar: S&OP & IBP | Date: 2026-04-08
- Author: Kislay S | Read time: 8 min
- Tags: S&OP, Process design, Cycle time, Consensus
- Summary: Most S&OP cycles take three days because they are designed to take three days. The cycle time is a function of process architecture, not effort. Change the architecture and the time collapses.
### The M5 competition settled the ensemble question. Most planners haven't noticed.
- URL: https://www.tensoranalytics.ai/prompt-ed/m5-competition-ensemble-question
- Type: concept | Pillar: Demand forecasting | Date: 2026-04-02
- Author: Dhiraj S | Read time: 10 min
- Tags: Forecasting, Ensemble, M5 Competition, Machine Learning, N-BEATS
- Summary: The M5 forecasting competition was the largest controlled study of forecasting methods ever run. The results, published in 2022, are clear. Ensembles win. Here is what they showed, and why most planning tools still ship single-model forecasting.
### Forecasting intermittent demand: what Croston got right, and what came after
- URL: https://www.tensoranalytics.ai/prompt-ed/forecasting-intermittent-demand-croston
- Type: concept | Pillar: Demand forecasting | Date: 2026-03-25
- Author: Dhiraj S | Read time: 11 min
- Tags: Forecasting, Intermittent demand, Croston, SBA, Statistical models
- Summary: Croston's method has been the default for intermittent demand since 1972. It is still useful. But three decades of research have produced better alternatives. Here is what to use when.
### What MAPE actually tells you (and what it hides)
- URL: https://www.tensoranalytics.ai/prompt-ed/what-mape-actually-tells-you
- Type: concept | Pillar: Demand forecasting | Date: 2026-03-18
- Author: Dhiraj S | Read time: 9 min
- Tags: Forecasting, MAPE, Accuracy metrics, Demand Planning
- Summary: MAPE is the most quoted forecast accuracy metric in supply chain. It is also the most misread. Here is what the number actually means, what it conceals, and what to track alongside it.
### Why One Forecast Model Is Never Enough
- URL: https://www.tensoranalytics.ai/prompt-ed/why-one-forecast-model-is-never-enough
- Type: concept | Pillar: Demand forecasting | Date: 2026-03-10
- Author: Kislay S | Read time: 8 min
- Tags: Forecasting, Technical, Ensemble, Demand Planning
- Summary: Every SKU has a different demand fingerprint. A single model applied across the range hides the signal. The answer is not a better model. It is a blend of models, picked per series.
### AI Regulation in Industry 5.0: Why Ethical AI Is Harder Than It Sounds
- URL: https://www.tensoranalytics.ai/prompt-ed/ai-regulation-in-industry-5
- Type: editorial | Pillar: The AI Hype Hangover | Date: 2026-02-13
- Author: Ayushi P | Read time: 10 min
- Tags: AI Hype Hangover, Editorial, Regulation, Ethics, Industry 5.0
- Summary: There's a widening gap between what the tech world promises and what's actually being built on factory floors. Why ethical AI still means looking past the glossy presentations at industry conferences.
### When the Emperor's Code Has No Clothes
- URL: https://www.tensoranalytics.ai/prompt-ed/when-the-emperors-code-has-no-clothes
- Type: editorial | Pillar: The AI Hype Hangover | Date: 2025-12-24
- Author: Kislay S | Read time: 7 min
- Tags: AI Hype Hangover, Editorial, Enterprise AI, ROI
- Summary: Discover why most enterprise AI projects are struggling to deliver ROI in 2025. From high-profile layoffs to the funding bubble, here's the harsh truth about GenAI ROI and where the actual value sits.
## Frequently asked questions

### What is GrepEye?
GrepEye is Tensor Analytics' family of operational modules: Supply Chain, Invo, and DealerPulse. Supply Chain has eight capability pillars: data infrastructure, rules and automation, analytics, integrated business planning, AI agent orchestration, forms, access control, and config packs.

### Who is Tensor Analytics for?
Manufacturers and distributors evaluating planning workflows; finance teams managing invoice capture and approvals; and dealership leaders reviewing branches and group performance.

### How do I book a demo?
Request a walkthrough at https://www.tensoranalytics.ai/contact or email teams@tensoranalytics.ai.

## Contact

- Sales and pilots: teams@tensoranalytics.ai
- General enquiries: info@tensoranalytics.ai
- Entity: Tensor Analytics LLC
- Evidence: Illustrative examples are not verified customer outcomes. Confirm configuration, integrations, and commercial terms for your environment.