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Operations

Supply chain and inventory analytics

Supply chain analytics and inventory analytics built on your ERP, warehouse and supplier data, so operations and finance decide what to buy, hold and expedite.

See how we work
Twelve slow-moving items hold a third of stock valueInventory dashboard: KPI tiles and a bar chart of the top 40 items where twelve items with no sale in 90 days hold 33% of stock value.Twelve slow-moving items hold a third of stock valueStock value by item, top 40 items, at month endDemo dataStock value$6.3MDays on hand81Fill rate94.2%$0K$100K$200K$300K12 slow movers: 33% of stock valueNo sale in 90+ days28 active itemsClearing or returning the slow movers releases cash without hurting serviceTwelve slow-moving items hold a third of stock valueInventory dashboard: KPI tiles and a bar chart of the top 40 items where twelve items with no sale in 90 days hold 33% of stock value.Demo dataTwelve slow-moving itemshold a third of stock valueStock value by item, top 40 items,at month endStock value$6.3MDays on hand81Fill rate94.2%$100K$200K$300K$0K12 slow movers: 33% of stock valueNo sale in 90+ days28 active itemsClearing or returning the slow moversreleases cash without hurting service

You run on ERP data but still plan in spreadsheets. Our supply chain analytics fixes that for mid-size manufacturers, distributors and project-based businesses. We join inventory, purchasing, supplier, order and delivery data in one model. Then we build the dashboards that show where cash sits in stock, which suppliers put service at risk and how demand is moving.

You do not need a control tower budget or new software. We work with the systems you have, such as NetSuite, Odoo, Zoho or QuickBooks, and add a data layer and dashboards on top. Every view serves two people: the COO who runs operations and the CFO who funds them.

Our edge is the finance view. Alongside service levels, every view shows working capital, carrying cost and cash tied up in stock.

Work starts with advisory on your decisions and data. Training and a fixed-scope build follow when your team is ready, with a fractional data team as an option afterwards. Book a consultation to agree where to start.

What it is

Supply chain analytics turns scattered operations data into decisions

Supply chain analytics means collecting, joining and analyzing data from purchasing, production, inventory, warehousing and delivery to make better decisions. It answers plain questions. How much stock do we hold, and is it the right stock? Which suppliers deliver late or short? Where do orders get stuck? What will demand look like next quarter, and can we meet it?

Supply chain management is the work of planning and running those flows. Supply chain analytics is the measurement layer on top. It tells managers whether the plan is working and where to act. Without it, decisions rest on experience, partial reports and spreadsheets that are stale by the time they are shared.

The right start is not advanced modeling. It is a clean, joined view of stock, orders and suppliers, with written definitions for metrics such as fill rate and inventory turns. Forecasting and scenario planning only work once that foundation exists. Skipping it is a direct route to a project that disappoints.

The decision it supports is concrete: what to buy, what to hold, what to expedite and what to clear. Every dashboard we build is tied to one of those four choices.

It also gives buyers, planners and finance one shared vocabulary, so a late delivery or a stockout means the same thing in every meeting.

Benefits

Supply chain analytics improves service and releases cash at the same time

Supply chain analytics is often framed as an operations topic. That misses half the value. The same data that helps the COO hit service levels also shows the CFO how much cash is locked in inventory.

Carrying cost covers storage, insurance, handling, shrinkage, obsolescence and the cost of capital. So even a modest cut in excess stock shows up in profit. At the same time, better visibility means fewer stockouts on the items that drive revenue.

Three conflicting plans become one shared service and cash viewThree spreadsheets report stock between $6.1M and $6.9M; one shared dashboard shows fill rate, on time in full, stock value and days on hand.Demo dataToday: three plans, three stock valuesOps planStock value$6.9MFill rate93%Finance forecastStock value$6.1MFill raten/aPurchasing sheetStock value$6.5MFill rate96%One sharedmodelShared S&OP dashboardServiceFill rate94.2%On time in full89%CashStock value$6.3MDays on hand81Operations and finance stop debating whose number is rightThree conflicting plans become one shared service and cash viewThree spreadsheets report stock between $6.1M and $6.9M; one shared dashboard shows fill rate, on time in full, stock value and days on hand.Demo dataToday: three plans,three stock valuesPlanStock valueFill rateOps plan$6.9M93%Finance forecast$6.1Mn/aPurchasing sheet$6.5M96%One shared modelShared S&OP dashboardServiceFill rate94.2%On time in full89%CashStock value$6.3MDays on hand81Operations and finance stop debatingwhose number is right

We design every dashboard for both audiences, so operations and finance stop debating numbers and start agreeing on actions. That is the shift: from monthly reconciliation to weekly decisions.

The working capital angle is easy to miss. Days of inventory on hand, excess stock and purchase commitments are finance numbers as much as operations numbers. When the CFO sees them weekly, cash planning improves and purchasing decisions get a second, informed view.

The service side matters just as much. Customers judge you on whether orders arrive complete and on time. Supply chain analytics shows which items, suppliers and locations cause misses, so planners fix the cause rather than chase each late order by phone.

The benefits compound. Clean stock data makes reorder points reliable. Reliable reorder points reduce costs from emergency freight and expediting. Less expediting frees planners to work on supplier performance, which shortens lead times and lets you hold less safety stock.

Each benefit below names the decision it improves.

  • Release cash from stock

    Spot slow, excess and obsolete items by location, and set reorder points on real consumption.

  • Ship more orders complete

    Track fill rate and backorders, then rebalance stock toward high-revenue items.

  • Hold suppliers to facts

    Score lead time, on-time delivery and quality, and see the true cost of unreliable suppliers.

  • Give the CFO early warning

    Show days of inventory and purchase commitments next to cash plans.

  • Run calmer planning meetings

    Sales, operations and finance work from one set of demand and stock figures.

  • Fewer write-offs at year end

    Aging views flag stock before it becomes obsolete, so it can be sold or returned in time.

Types

Supply chain analytics types build on each other, from descriptive to prescriptive

Supply chain analytics is usually described in four levels. Each one depends on the level below it. That is why the data foundation always comes first.

Start with descriptive and diagnostic supply chain analyticsFour rising steps from descriptive to prescriptive analytics with a demo example each; the first two are marked as a practical starting point.Each level builds on the one belowDemo data1 DescriptiveWhat happenedStock by site,last 12 months2 DiagnosticWhy it happenedSupplier B drovethe fill rate drop3 ForecastingWhat comes nextQ3 demand +6%on baseline4 PrescriptiveWhat to do2 weeks safety stockgives 97% serviceA practical starting pointMore value per level, but only on clean historyStart with descriptive and diagnostic supply chain analyticsFour rising steps from descriptive to prescriptive analytics with a demo example each; the first two are marked as a practical starting point.Demo dataEach level builds onthe one belowA practical starting point1 DescriptiveWhat happenedStock by site, last 12 months2 DiagnosticWhy it happenedSupplier B drove the fill rate drop3 ForecastingWhat comes nextQ3 demand +6% on baseline4 PrescriptiveWhat to do2 weeks safety stock gives 97% serviceMore value per level, but only on clean history
Type 1

See what happened

Descriptive: stock levels, orders shipped and supplier performance over time, by item and location.

Type 2

Find out why

Diagnostic: drill from a KPI to its cause, such as which supplier drove a drop in fill rate.

Type 3

Estimate what comes next

Forecasting: baseline demand and lead time estimates built on clean history.

Type 4

Compare your options

Prescriptive: what-if scenarios, such as safety stock levels against service and cost targets.

Descriptive analytics tells you what happened. Diagnostic analytics tells you why. Forecasting estimates what is likely next, based on clean history. Prescriptive analytics compares options and suggests what to do.

The first two levels are a practical place to start. They are quick to build, easy to trust and answer the questions managers ask every week. Forecasting and scenario models add value once item, supplier and order history is clean and consistent.

We build the first two levels and the scenario models that support planning. We also prepare the clean, documented history that any forecasting tool needs, so your next step is ready when you are.

The practical rule is simple. Do not buy a forecasting tool until your history is clean. Do not build scenarios until your KPIs are agreed. Each level pays off only when the one below it is solid.

That is why our first deliverable is always a trusted descriptive view, built on joined and cleaned data.

Real-time visibility is a separate question from the level of analytics. A daily or hourly refresh fits purchasing and planning decisions; a faster feed helps only where someone acts within the hour, such as dispatch.

Components

Five data sources must be joined before any KPI can be trusted

Supply chain data is spread across systems that were never built to work together. Supplier dates sit in email. Carrier status sits in a portal. Stock sits in the ERP.

How five data sources become one trusted KPI

Select a stage, use Next or the arrow keys

Map sources. List where data lives: ERP, inventory module, supplier files, carrier portals and the CRM pipeline.

Step 1 of 5

The first task in every project is to map these sources, agree definitions and join them in one model, usually in a data warehouse or a governed Power BI model. Where suppliers send regular files, we automate the import so nobody retypes data.

Joining the data is also where definitions get settled. What counts as a late delivery? Is a partial shipment on time? Which unit of measure is the standard? We agree these answers with operations and finance before any dashboard is built.

The result is one model where every KPI traces back to its source. When a number looks wrong, anyone can see where it came from.

Demand forecasting inputs belong here too. Sales history, open orders and the CRM pipeline are joined at item level, so any later demand forecasting starts from one clean record of what customers actually ordered.

The model is documented in plain English, so your team can add a new warehouse or supplier without rebuilding it.

This step also exposes gaps early, before dashboards are built.

Component 1

Orders and costs in one place

ERP: items, bills of materials, purchase and sales orders, receipts and costs.

Component 2

Stock by location

Warehouse or inventory module: on-hand stock, movements and cycle counts.

Component 3

Supplier promises tracked

Supplier data: lead times, confirmations, delivery dates and quality results.

Component 4

Delivery visible end to end

Carriers and logistics providers: shipment status, delivery dates and freight costs.

Component 5

Supply that follows sales

CRM pipeline and customer forecasts that feed demand planning.

KPIs we track

Six inventory analytics KPIs tell you if stock is working

Every metric is defined in writing, with its formula and source, so operations and finance read it the same way. These are the core KPIs in our inventory analytics and supply chain dashboards.

GoalService levels met with less cash in stock
  1. Stock efficiency
    • Inventory turnover
    • Days on hand
  2. Customer service
    • Fill rate
    • On-time in-full (OTIF)
  3. Supplier reliability
    • Supplier lead time
    • Lead time variability
  4. Cash at riskReview first
    • Excess and obsolete stock value
Goal, drivers, metrics. The highlighted driver is the one to review first.

Stock that sells faster

Demo

Inventory turnover: cost of goods sold divided by average inventory value.

Fewer days of cash in stock

Demo

Days on hand: average inventory divided by daily cost of goods sold.

More orders shipped complete

Demo

Fill rate: share of order lines shipped in full from stock first time.

Promises kept

Demo

On-time in-full (OTIF): orders delivered on the promised date and quantity.

Safety stock set on facts

Demo

Supplier lead time and its variability, by supplier and item.

Cash released from dead stock

Demo

Excess and obsolete stock: value above target cover or without recent movement.

Targets are set with you, by category, because a fast-moving consumable and a spare part should never share one target.

Together, these KPIs answer two questions at once. Are we serving customers well? And how much cash does it take? A rise in fill rate that needs a large rise in stock is not a win. Seeing both sides on one page is what lets operations and finance agree on the right balance.

Each KPI on the dashboard shows its trend, its target and its owner. When a value moves outside its range, the owner sees the items behind the change and decides whether to reorder, rebalance or expedite. Formulas sit in a glossary, so any change in method is logged.

Sample dashboards

Three supply chain dashboards cover stock, suppliers and demand

These samples use demo data to show the structure and the decision each view supports. They are not client results. In a real project, the same views run on your ERP and supplier data and refresh on their own.

Three supply chain dashboards: stock, suppliers and demand

Demo data
Stock value$6.3M▼ -4% vs last quarter
Days on hand81▼ -6 days
Excess and obsolete$2.1M▲ +$0.2M
Inventory turnover4.5x▲ +0.3x

Excess stock is concentrated in one warehouse

Excess and obsolete stock value by warehouse, $K

Central$1,220KNorth$400KSouth$300KWest$180K$1,220K in Central: Most excess sits hereCentral$1,220KNorth$400KSouth$300KWest$180K$1,220K in Central: Most excess sits here

So what: Stop reorders for flagged items at Central and rebalance before buying more.

Item groupStock valueDays on handExcess value
Raw materials$2.7M92$920K
Components$2.0M76$760K
Finished goods$1.6M68$420K
View 1

Decide what to stop buying

Inventory health: stock value by item, location and age, with excess and obsolete items flagged.

View 2

Decide which suppliers to review

Supplier scorecard: OTIF, lead time spread, quality issues and price variance over time.

View 3

Decide what to promise customers

Demand and service: orders and backorders against your current forecast, with fill rate by segment.

Each dashboard is built for a named role and a recurring meeting. The inventory view serves the weekly operations review and the monthly finance review. The supplier view serves quarterly supplier meetings. The demand view serves the monthly planning meeting where sales, operations and finance agree the next quarter.

Every chart title states the insight, not the metric. Drill-through takes buyers straight from a flagged item to its open purchase orders, so they can act on the same screen.

The outcome is fewer spreadsheets and faster decisions. Buyers know what to stop ordering, planners know what to expedite and finance knows how much cash is in stock.

Each dashboard also has an owner. That person decides when a view needs to change, so the dashboards evolve with the business instead of going stale.

We review each dashboard with its owner after the first month of use and refine it.

Refresh timing follows the decision. Inventory and supplier views refresh daily from the ERP; a warehouse or dispatch view can refresh more often where the team acts within the day. Access is set by role and site.

Use cases

Wherever goods move, supply chain analytics finds cash and delay

Supply chain analytics applies wherever goods, materials or equipment move through a business. Common use cases are practical and quick to put to work.

Manufacturers use it to balance raw material stock against production plans and to track supplier reliability. It links directly to capacity planning, so materials and machine time are planned together.

Every use case feeds the same purchasing cash forecastMatrix of five business types against five uses: manufacturers use stock levels and supplier reliability; distributors and wholesale use stock by site or channel; service firms use spare-part stock; construction uses supplier reliability and material cost per job; all five feed cash for purchasing.IllustrativeEvery use case feeds the same purchasing cash forecastWhere each type of business starts with supply chain analyticsStock levelsSupplierreliabilityStock by siteor channelMaterial costper jobCash forpurchasingManufacturersDistributors and retailersWholesale and e-commerceService and maintenanceConstruction and projectsStart with the use case that costs you most today; finance plans purchasing cash on the same dataEvery use case feeds the same purchasing cash forecastMatrix of five business types against five uses: manufacturers use stock levels and supplier reliability; distributors and wholesale use stock by site or channel; service firms use spare-part stock; construction uses supplier reliability and material cost per job; all five feed cash for purchasing.IllustrativeEvery use case feeds the samepurchasing cash forecastWhere each type of business starts with supplychain analyticsManufacturersStock levelsSupplier reliabilityCash for purchasingDistributors and retailersStock levelsStock by site or channelCash for purchasingWholesale and e-commerceStock levelsStock by site or channelCash for purchasingService and maintenanceStock levelsCash for purchasingConstruction and projectsSupplier reliabilityMaterial cost per jobCash for purchasingAll five feed one purchasing cash forecastfor finance.

Distributors and retailers use it to move stock between locations, avoid stockouts on high-margin items and clear slow movers before they become obsolete.

Wholesale and e-commerce businesses use it to compare demand by channel and decide where to hold stock for each one.

Service and maintenance businesses use it to plan spare parts against service contracts and response times.

Construction and project businesses use it to track materials ordered, delivered and used per project, and to spot supplier delays before they stop a site.

In every case, finance uses the same data to forecast cash for purchasing and to report inventory value with confidence at month end.

Start with the use case that costs you most today. One focused dashboard can make the case for the next.

Project-based firms can also compare planned and actual material cost per job, which shows where margin is lost before the final invoice.

manufacturingconstructioncapacity planning
Common challenges

Supply chain analytics fails on data and adoption, not technology

A supply chain analytics project can disappoint even with good software. It fails when the data was scattered, the master data was messy, teams defined metrics differently or nobody used the dashboards.

Each problem has a known fix. We build those fixes into the plan from day one, rather than discovering them after launch.

Each common supply chain analytics failure has a known fixFive problem and fix pairs: scattered data, messy master data, differing KPI definitions, unused dashboards and oversized platforms.Why projects failFix built into the planData scattered across ERP, files and portalsAll sources in one automated modelMessy item and supplier codesClean records plus rules for new onesTeams define KPIs differentlyOne KPI glossary approved by ops and financeDashboards nobody opensOne view per role and per meetingAn oversized platform projectBuild on your existing ERP and BI toolsEach common supply chain analytics failure has a known fixFive problem and fix pairs: scattered data, messy master data, differing KPI definitions, unused dashboards and oversized platforms.Why projects failFix built into the planData scattered across ERP, filesand portals→ All sources in one automated modelMessy item and supplier codes→ Clean records plus rules for new onesTeams define KPIs differently→ One KPI glossary approved byops and financeDashboards nobody opens→ One view per role and per meetingAn oversized platform project→ Build on your existing ERP andBI tools
  1. 1

    All sources, one model

    We map ERP, supplier files and carrier portals, then automate imports with n8n, Make or Zapier.

  2. 2

    Item and supplier records you can trust

    We clean codes, units and supplier records, and add rules for new ones.

  3. 3

    Everyone reads KPIs the same way

    We write a KPI glossary that operations and finance approve before building.

  4. 4

    Dashboards people open

    Each view is built for one role and one meeting, then refined after real use.

  5. 5

    No oversized platform project

    We build on your existing ERP and BI tools and add only what the data shows you need.

Here is a common failure pattern. A company buys a planning tool. The tool needs clean item and supplier data, which does not exist. Teams keep their spreadsheets. A year later, the tool is used by two people.

We reverse the order. Clean, joined data comes first. Agreed definitions come second. Dashboards built for named roles come third. Tools come last, and only where they clearly help. This order costs less and sticks.

Adoption needs the same care as data. Each dashboard has a named owner, a meeting where it is used and a short review after the first month. If a view is not opened, we find out why and fix or retire it.

The outcome is a supply chain analytics setup that people use every week, built on data they trust, at a cost that fits a mid-size company.

Each fix is part of the fixed scope, agreed in writing, so nothing is left for a later phase by surprise.

Tools

We build on the supply chain analytics software you already own

We are tool-agnostic. There is no platform to sell you, so we start from what you have.

Your ERP as the sourceNetSuite, Odoo, Zoho and QuickBooks for items, orders, purchasing and costs.
Dashboards by rolePower BI with security by site or team.
Exceptions tracked simplyAirtable for supplier actions and project materials.
No more manual uploadsZapier, Make and n8n for supplier files and portals.

If you already own specialist supply chain analytics software, we bring its data into the same model, so leaders see one picture.

Every tool choice is documented with its running cost, so finance can see what the stack costs each year.

operations automation
How we work

Supply chain analytics consulting delivers a working dashboard early

Our supply chain analytics consulting follows four steps. You see a working dashboard early, and each step ends with a clear decision on what comes next. We propose the build only when the review shows your data and team are ready; if they are not, we continue with advisory or training first. Scope, timeline and price are fixed in writing before the build starts.

Supply chain analytics consulting in four steps

Select a stage, use Next or the arrow keys

Discover. Agree the decisions first. We review systems and reports, interview operations, purchasing and finance, and list the decisions to support.

Step 1 of 4

The first dashboard is usually the inventory health view, because it shows cash and service at once. It becomes the test of the whole approach. If it changes a decision in its first month, we build the next one.

Each step is short, so you never wait long to see results. Timelines are agreed in writing after the systems review and depend on the number of sources and the state of master data.

AI readiness assessmentdata modernization projectsanalytics as a servicestrategic planning advisoryBook a consultation
Frequently asked questions

Supply chain analytics: questions buyers ask before choosing tools or services

The four types are descriptive (what happened), diagnostic (why it happened), forecasting (what is likely next) and prescriptive (what to do about it). Each depends on the one before. A practical start is descriptive and diagnostic supply chain analytics on clean, joined data, then forecasting and scenarios.

Supply chain management is planning and running purchasing, production, stock and delivery. Supply chain analytics measures how well that works and explains why. It gives managers the facts to adjust plans, negotiate with suppliers and release cash, instead of relying on experience and stale spreadsheets.

A typical setup has three layers: source systems such as an ERP, a data layer that joins and cleans the data, and a BI tool such as Power BI for dashboards. Automation tools move data from supplier files and portals. Specialist planning software can come later, once the data is reliable.

Revenue grows when the right products are available at the right time. Analytics cuts stockouts on high-demand items, improves on-time delivery for key customers and frees cash from slow stock to invest in items that sell. It also shows which customers and products are most profitable once supply costs are included.

Our supply chain analytics services include a data and reporting assessment, KPI definitions, source integration, master data cleanup, a data model, dashboards for operations and finance, training and ongoing support. Work starts with advisory and an initial assessment; fixed-scope projects or a fractional data team follow when your team is ready.

Not always. If most data sits in one ERP, a governed Power BI model may be enough to start. When data comes from several systems, suppliers and carriers, or you need long history, a data warehouse becomes the better foundation. We recommend the simplest option that works.

It shows where money leaks: excess and obsolete stock, emergency freight, expediting and supplier delays. With those visible by item and supplier, buyers order closer to real consumption, planners expedite less and finance sees carrying cost fall in the same report as service levels.

Yes. We build on NetSuite, Odoo, Zoho or QuickBooks data as it is, and add a data layer and Power BI dashboards on top. If item, supplier or unit data is inconsistent, we clean it during the project, so the ERP stays the system of record.

One assessment shows which supply chain KPI to fix first

Start with a short assessment of your supply chain data. We review your ERP, stock and supplier data, pick the KPIs that matter most for operations and finance, and propose a first dashboard with a clear scope. You leave with a practical plan, whether or not you continue with us. Book a consultation, or see how this fits our wider operational analytics work.

We reply within one business day with a proposed agenda for the first call.

There is no obligation and no sales pitch.

operational analytics
See how we work