Decisions AI Saves Global Electronics Giant $12.6M in Revenue Forecasting with Real-Time Decisions AI

Introduction

A multi-billion-dollar global electronics company, operating in the consumer and enterprise technology sectors, runs a complex supply chain serving enterprise customers through direct-to-customer shipments. In the U.S., this company handles approximately 10,000 full truckload (FTL) shipments per quarter as part of its B2B operations. These shipments directly impact revenue recognition, especially toward the end of financial quarters.  

The Challenge: Visibility Blind Spots in In-Transit Revenue   

Toward the end of each quarter, the CFO must provide accurate, audit-ready revenue forecasts by quarter-end. To do so, a precise understanding of which orders will be delivered by 11:59 p.m on the final day of the quarter was needed. However, the Chief Supply Chain Officer failed to provide visibility into order delivery status, so they can predict the revenue that will be recognized in the financial period. Overall, the challenge was zero confidence in forecasting revenue from in-transit shipments. This blind spot introduced risks across: 

• Financial Reporting 

• Operational Stress 

• Investor Trust 

However, the company faced a major gap: while it was easy to quantify revenue from orders already delivered, there was little to no visibility into shipments that were still in transit. This blind spot made it nearly impossible to predict which of the remaining orders would arrive before quarter-end, severely impacting revenue forecasting accuracy. They chose Decklar to solve their challenge. 

Manual Workarounds & Their Limitations

Without real-time transparency, the supply chain and finance teams scrambled toward quarter-end. This meant they manually tracked shipments via phone calls, spreadsheets, and outdated system data. This last-minute effort was not only resource-intensive but also error-prone. It led to:  

• Missed or misclassified revenue leading to under or over-reporting on compliances such as ASC 606 or IFR 15  

• Extra buffer stock and expedited shipping to meet deadlines, increasing costs  

• Stress and productivity loss for key teams  

What Solutions They Tried & Why They Didn’t Work

1. Carrier Aggregated Visibility Providers: 

           These solutions provided visibility into shipment locations but were limited by key gaps: 

• • Delayed or Missing ETAs: They provided only basic tracking information, often with missing or delayed estimated times of arrival (ETAs) due to carrier reporting dependencies, which hindered the ability to make accurate revenue predictions.  

• • Lack of Contextual Visibility Data: These tools did not integrate SKU-level or inventory-specific information. While they could show where shipments were, they didn’t provide enough context to connect transit status with financial outcomes or operational decisions. This made it impossible to accurately forecast revenue from in-transit shipments.  

2. Track-and-Trace Providers: 

            Track-and-trace systems were limited in the following ways:  

• • Basic “Dot on the Map” Visibility: These systems simply showed where the shipment was on a map, but they failed to provide the predictive insights needed to act on those locations especially with respect to revenue realization.  

• • Operational Hassles of Scale Deployment: Getting these tracking solutions operational was often cumbersome and required significant manual effort. While they could track shipments, they also couldn’t translate that data into actionable business decisions related to revenue forecasting or inventory management, leaving users with download data and analyzing visibility on spreadsheets.  

3. ERP/TMS/WMS/Planning Tools (e.g., SAP, Oracle, etc.):

           Existing tools (ERP, TMS, WMS, and planning systems) were designed to manage inventory and supply chain planning but lacked critical real-time visibility:  

• • Outdated Transit Time Information: These systems used static and outdated transit times, which didn’t provide real-time updates on the actual delivery status of shipments.  

• • No Real-Time Visibility: While they had SKU and inventory data, these tools couldn’t provide real-time visibility into shipments, nor could they integrate that data with transit time changes. As a result, forecasting and operational decisions were made based on outdated information. 

Enter Decklar’s Real-Time Decisions AI With Visibility

Decklar was brought in to address these exact pain points. We eliminated revenue blind spots by combining real-time visibility with actionable decision intelligence. Using its real-time decisions AI platform with visibility, Decklar enabled the client to:  

• Track every B2B direct shipment in real time using smart labels  

• Predict ETAs with high confidence based on 10+ years of lane-level data 

• Alert teams on potential delays or delivery exceptions  

• Forecast revenue more accurately at the shipment level – whether it was a container, pallet, or a package 

Day 1: Immediate Operational Transparency as the Value  

• The first visible win was operational. Decklar enabled rapid deployment of visibility across multiple modes of transport whether it was FTL, LTL, containers, pallets, or air shipments for expedited goods. The client was able to get visibility at every level of the shipment journey, ensuring they had real-time insights at scale without the need for operational overhauls. This helped them gain immediate visibility and confidence in tracking across all transportation modes, both domestically and globally. It also eliminated dependence on manual updates and calls. 

Day 2: Decisions AI in Action  

The second-order value came from combining real-time visibility with Decisions AI, which provided actionable insights. The client was able to:  

1. Identify On-Time Shipments: Recognize shipments that were on track and forecast revenue for these shipments accurately. 
2. Manage Delays: Recognize shipments that were delayed and recommend actions (e.g., expedite, prioritize unloading) to ensure the goods reach their destination as quickly as possible, thus avoiding unnecessary detention costs. 
3. Predict Future Shipments: Use historical transit data, including 10+ years of visibility intelligence on lanes, nodes, and carriers, to predict the most efficient shipping options for future shipments ensuring they could prioritize which orders to ship to meet their revenue goals by the end of the quarter.

Why Decisions AI With Visibility Were Needed Together  

The combination of Visibility and Decisions AI was essential because:  

• Visibility Alone only provides status updates (where the shipment is), but it doesn’t show how it impacted financials, or which shipments should be prioritized.  

• Decisions AI Alone would have been ineffective without visibility. While AI could predict the best shipping routes or pinpoint potential delays, it needed real-time data to make those decisions actionable and accurate.  

Together, the solution gave the client the ability to both see and act on their shipments in real time maximizing efficiency, reducing operational hassle, and driving accurate revenue forecasts. 

ROI Summary  

Approximate Quarterly Investment in Decklar: $1,000,000 (10,000 shipments × $100 per shipment)  

Total Estimated Quarterly Direct Benefits:  

• Staff Productivity: $450,000  

• Operating Capital Efficiency: $3.75M–$6M  

• Compliance Risk Avoidance: $1.25M  

• Regulatory Risk Avoidance: $625,000  

• Inventory Efficiency: $2.5M–$4M

• Total Direct Benefits: $8.575M–$12.625M  

Total Indirect Benefits:  

• Customer Satisfaction (OTIF Delivery): $750,000–$1.2M  

• Market Cap Protection (Attributed): ~$5,000,000  

ROI Calculation:  

• Direct ROI (Low Case): ($8.575M – $1M) ÷ $1M = 757% ROI  

• Direct ROI (High Case): ($12.625M – $1M) ÷ $1M = 1,262% ROI  

Indirect ROI (Strategic Value):  

• Market Cap Protection: $5,000,000 in valuation protection  

• Customer Satisfaction Uplift: $750,000–$1.2M uplift in repeat business  

Overall ROI (combining both direct and indirect):  

• Low Case: ~757% direct ROI + strategic value (customer satisfaction and market cap protection) 

• High Case: ~1,262% direct ROI + strategic value (customer satisfaction and market cap protection)  

Summary

By implementing decision AI with real-time visibility, the client transformed its end-of-quarter revenue forecasting process. What was once an opaque, stressful, and error-prone exercise is becoming a predictable, data-driven operation across Finance, Logistics, and Supply Chain teams. The solution not only improved financial performance but also strengthened customer relationships, operational agility, and compliance posture.  

This engagement demonstrates how visibility and AI-powered decisions can unlock both top-line growth and bottom-line efficiency in high-stakes B2B supply chains.  

Decklar’s Decisions AI transformed quarter-close forecasting chaos into an intelligent, data-driven capability.  

[Request a Personalized Demo] to see it in action.