What does Kadmoon do?

We are a Power BI and Microsoft Power Platform consultancy. We take scattered raw data to the dashboards your leadership actually opens, on a governed foundation of Microsoft Fabric, Azure Synapse, and Azure. That covers the data engineering underneath, the semantic model with one definition per KPI, the reports on top, and the Power Platform apps and automations around them. Everything is built in your tenant and owned by you.

Do you only work in the Microsoft ecosystem?

Yes, on purpose. We go deep in Power BI, Microsoft Fabric, Azure, Power Platform, and Dataverse rather than spreading thin across five stacks. That focus is why we can move fast, govern by default, and support what we ship. If your data lives in other systems, we connect to them, but the analytics and BI layer we build and run is Microsoft.

How much does a Power BI project cost?

It depends on scope: how many sources we connect, the state of the data underneath, how many dashboards and KPIs you need, and whether governance and a data platform are part of the work. A focused set of dashboards on clean data is a small fixed-scope project. A full data platform on Fabric with governance is larger. We scope it with acceptance criteria and milestone billing, so the price maps to something you can verify rather than an open estimate.

We already have Power BI but it is a mess. Can you help?

Yes, this is common and it is a lot of our work. We inventory the existing reports, find where KPIs disagree, and rebuild on a governed semantic layer so revenue, margin, and churn mean one thing everywhere. We add row-level security, workspace structure, and Purview where they are missing. You keep working the whole time; we fix the foundation underneath rather than starting over.

How does a project start?

With a one to two week discovery. We start from the decisions you need to make, not the data you happen to have, and map the business questions, the sources, the indicators, and the owner of each number. You leave discovery with a diagnosis and an architecture plan. First working dashboards are typically live by week two, then validated with each area every cycle.

What about tenant-to-tenant migration?

It is a signature practice for us. When you move to a new Microsoft tenant, after a merger, divestiture, or restructure, your Power BI reports, datasets, workspaces, and Power Platform environments do not come along by default. We inventory everything, remap dependencies, and cut over in phases so you do not lose years of BI and automation history or leave users without reports mid-move.

Which regions and company sizes do you serve?

We are based in Austin, Texas and work with clients across the United States. We deliver remotely with in-person sessions when a project calls for it. We work with mid-market companies and enterprise teams; the common thread is that data matters enough to govern it. We are a fit whether you need a first governed dashboard or a full Fabric platform with managed support.

Supply Chain

S&OP and demand planning

A demand and supply planning view that measures forecast accuracy by family and horizon, so the S&OP cycle argues from one set of numbers.

Power BIMicrosoft FabricAzure Data FactoryDAX

Forecast accuracy: 78.4%; 1 - value-weighted MAPE
Average coverage: 34 days; stock over forecast demand
Supplier OTIF: 91.2%; on time and in quantity
Inventory capital: $84.6M; all families

Illustrative figures · fictitious client · under NDA

The challenge

An S&OP process ran on disconnected spreadsheets, so forecast accuracy was unknown and every function came to the meeting with a different number.

What we built

We built a governed planning model and Power BI dashboards that track demand, supply, and forecast accuracy by product family and horizon, giving the S&OP cycle one shared view.

A governed planning model over demand and supply data.
Forecast accuracy by family and planning horizon.
A shared S&OP dashboard for every function.

Outcomes

One set of numbers for the S&OP cycle.
Forecast accuracy measured instead of assumed.
Demand and supply lined up by horizon.

The dashboard we delivered

Demand and Supply Planning · S&OP cyclegold_sop · FabricRefreshed 07/08 05:20Illustrative data

Report pages

Filters

Cycle

S&OP Aug/2026▼

Horizon

M+1 to M+6▼

Curve

A, B and C▼

Hierarchy

Family

Supplier

Item

Family · consolidated

Forecast accuracy+6.2 p.p.

78.4%

1 - value-weighted MAPE

Average coverage-5 days

34 days

stock over forecast demand

Supplier OTIF+3.8 p.p.

91.2%

on time and in quantity

Inventory capital-$6.1M

$84.6M

all families

Class A items

1,284

Input stockout

3.4%

Obsolete stock

$4.2M

Average lead time

21 days

Forecast accuracy

1 - MAPE for the cycle, against the 80% target

AccuracyTarget

84.8%77.5%70.4%63.4%

SepOctNovDecJanFebMarAprMayJunJulAug

Inventory capital by family

Click to open by supplier

Imported inputs account for 5% of value and 31% of shortages: risk concentrated in lead time.

Items in the slice

One row per item planned in the cycle

8 of 160 rows

Item

Supplier

Stock

Coverage

Accuracy

Lead time

Curve

IT-44030 · PBR palletQuímica Andrade

Química Andrade

$184K

53 d

56.9%

38 d

IT-33573 · Bearing 6205Química Andrade

Química Andrade

$172K

60 d

91.3%

7 d

IT-39732 · Gray epoxy paintDC Cajamar

DC Cajamar

$136K

44 d

85.5%

6 d

IT-88491 · Hydraulic oil ISO 68DC Cajamar

DC Cajamar

$124K

48 d

61.2%

45 d

IT-84707 · PP homopolymer resinCartonagem Vale

Cartonagem Vale

$76K

15 d

58.7%

49 d

IT-30910 · Bearing 6205Cartonagem Vale

Cartonagem Vale

$69K

28 d

84.6%

34 d

IT-57117 · Cardboard box 40x30Ferramentaria Norte

Ferramentaria Norte

$25K

72 d

77.1%

17 d

IT-64120 · Adhesive label 90mmFerramentaria Norte

Ferramentaria Norte

$33K

59 d

89.5%

40 d

Illustrative dashboard with the structure delivered on the project. Client names and figures are fictitious and under NDA.

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More case studies

Business IntelligenceQlikView to Power BI migrationA full legacy BI estate moved to Power BI with inventory, dependency mapping, and a rebuild on a governed model, with no information blackout.RetailA single source of truth on Microsoft FabricSales, inventory, and margin from stores and e-commerce consolidated in OneLake and served to Power BI through Direct Lake.ManufacturingExecutive OEE and cost BIMES and ERP data crossed for OEE, loss, and cost per line and product, refreshed for the people on the shop floor.