Supply Chain& Logistics

Stockouts, overstock, and missed delivery windows are symptoms of fragmented supply chain data.

We build demand forecasting, inventory optimization, and OTIF tracking on top of your WMS, TMS, and ERP data so supply chain leaders stop firefighting and start planning ahead.

40%
stockout reduction

Replace fragmented spreadsheets with a unified view of OTIF, inventory turns, supplier performance, and demand accuracy across the full chain.

The Challenge

The operational reality of supply chain

01

Stockouts and overstock from monthly forecasts

Demand forecasts run monthly but inventory decisions need to happen daily. By the time the monthly forecast updates, the situation on the ground has already changed.

02

OTIF drops without visible root cause

On-time in-full performance slips but the root cause is buried across carrier data, warehouse throughput, and order management systems nobody connects.

03

Inventory carrying costs accumulate silently

Slow-moving SKUs pile up in warehouses, tying up working capital without anyone tracking the cost per SKU or flagging the buildup in time.

04

Fleet and warehouse utilization invisible

Fleet utilization and warehouse productivity are guesses until the end of quarter analysis. Optimization decisions get made with the wrong numbers.

Key Metrics

The KPIs we surface for supply chain firms

On-time in-full (OTIF)
Inventory turnover
Days inventory outstanding
Forecast accuracy
Order fill rate
Carrying cost as % of revenue
Fleet utilization
Warehouse productivity
Stockout rate
Perfect order rate
Capabilities

How we help supply chain firms

01

Daily-refresh demand forecasting models

SKU and location-level demand models that update every day. Reduce stockouts and overstock simultaneously with forecasts that actually keep up with the business.

02

Inventory optimization across SKU and location

Safety stock, reorder points, and ABC analysis for every SKU across every location. Reduce carrying cost while maintaining service levels.

03

OTIF root cause analytics

Drill into OTIF failures by carrier, route, warehouse, and SKU. Know exactly where on-time performance is breaking down and why.

04

Fleet and route optimization

Route optimization models that improve delivery performance and reduce cost per mile. Fleet utilization dashboards that show idle time, load factor, and driver performance.

05

AI agents for procurement and exception handling

AI agents that monitor inventory levels, flag replenishment needs, handle routine procurement decisions, and escalate exceptions for human review.

Integrations

Some of the systems we connect with

These are some of the platforms we connect. We integrate with almost any system. If you run it, we can likely work with it.

SAP S/4 HANA
Oracle SCM
NetSuite
Manhattan Associates
Blue Yonder
Coupa
Descartes
Kinaxis
Anaplan
SAP S/4 HANA
Oracle SCM
NetSuite
Manhattan Associates
Blue Yonder
Coupa
Descartes
Kinaxis
Anaplan
Use Cases

Where this applies

01

Distributor: daily demand forecasting replaces monthly model

A regional distributor ran a monthly demand forecast in Excel that was outdated by week two. Stockouts occurred 3 to 4 times per month, causing service failures with key accounts.

Outcome

We built a daily-refresh demand forecasting model using SAP sales history and external signals. Stockouts dropped by 38% in the first quarter. Safety stock levels were reduced by 15%.

02

3PL: OTIF root cause by carrier and route

A third-party logistics provider had declining OTIF scores but could not identify which carriers, routes, or product categories were driving the problem.

Outcome

We built an OTIF root cause dashboard connecting TMS, carrier, and order data. The provider identified 3 under-performing carriers and renegotiated SLAs. OTIF improved from 88% to 94% in 60 days.

03

Manufacturer: inventory optimization across 6 DCs

A manufacturer held inventory across 6 distribution centers with no unified view of stock levels or demand by location. Overstock at one DC coexisted with stockouts at another.

Outcome

We built a cross-DC inventory optimization model. Inter-DC transfers reduced stockouts by 40% without increasing total inventory investment.

Results

Outcomes you can expect

40%
Reduction in stockouts
22%
Improvement in OTIF
18%
Reduction in inventory carrying cost
25%+
Forecast accuracy improvement
FAQ

Frequently asked questions

Get Started

Stop managing supply chain blind.

Book a free call and we will show you what supply chain analytics can deliver in 60 days.