Practical AI for supply chain and operations, starting with your data

This work is for manufacturers and growing businesses that have data in ERP systems, spreadsheets, machines, quality systems or shared folders but lack a trusted decision view. It supports leaders who want better visibility before choosing a platform, a dashboard or an automation investment.

• Operations and finance teams reconciling different versions of the same KPI.
• Plants that need a role-based KPI dashboard for daily or weekly decisions.
• Planners who want a statistical baseline to review and adjust.
• Teams exploring practical AI or workflow automation with clear human ownership.

• Reports take too long to assemble and still prompt basic data questions.
• Definitions for output, downtime, quality, inventory or service vary by team.
• Important exceptions are buried in email, spreadsheets or manual checks.
• AI ideas are discussed before the process, data, risk or owner is clear.

Defined pieces of work with named deliverables. Fixed-price, scope confirmed after a free call.

Vendors and analysts are loud about AI, but the evidence points to starting small and fixing data first:

• Gartner said in March 2026 that current immaturity and data issues limit full automation to low-risk decisions for now.
• In Netstock’s 2026 survey of small and mid-size businesses, data integrity and security is the top AI concern at 39%.
• In PwC’s 2026 survey of 767 US operations and supply chain leaders at $100M+ companies, 73% agree data does not need to be perfect to drive value.

That is why every AI engagement here starts with the data and a named human owner.

We start from the ERP exports and spreadsheets you already have. Where a shared, refreshable view is needed, we build prototypes in standard BI tools such as Power BI or Tableau, and we recommend platforms only after the KPI definitions and data owners are clear.

A focused engagement is typically three to five weeks once scope, access and users are confirmed.

Goals include faster access to a shared KPI view, clearer metric ownership, better exception conversations and a more responsible path for BI and AI automation. No dashboard, forecast or automation guarantees a business result. Value depends on data quality, user adoption, process discipline and the decisions made from the information.

Is AI required?

No. We consider AI only where it solves a defined problem and can be governed. A simpler report or workflow may be the better choice.

Do you replace our ERP or BI platform?

No. We start with decisions and data reality, then help define the smallest useful improvement and platform requirements if a change is justified.

Can you work with spreadsheets?

Yes. Spreadsheets can be a useful starting point when their definitions, ownership and refresh expectations are explicit.

Is our data good enough?

Often it is enough to start. We identify the gaps and fix the ones that matter for the decision first. Small or messy data sets may not support complex models, and we will say so.

Will the forecast pilot beat our current method?

We cannot promise that. The pilot compares a model with your current method on one product family so you can see the difference before deciding anything.

How long does a prototype take?

A focused prototype and adoption plan is typically three to five weeks after scope, access and users are confirmed.

Use AI and data work to support Operations, including Lean and OEE, or Supply Chain, including inventory and S&OP. Related standalone offers: the Landed-Cost & Tariff What-If Dashboard and the Supplier Traceability & Compliance Evidence Pack. You can also read how we work or browse all services.