How a Leading Global Chocolatier Saved $9.3M+ & Retained Market Share with Decisions AI

Introduction

A leading global chocolatier operating across the world relies on a complex cold chain network to ensure the quality of its products from factories to distribution hubs and retail stores. With over 24,000 temperature-sensitive shipments per month spanning 92 cold chain transport lanes in South Asia, the company faced mounting risks from inconsistent transporter practices, environmental exposures, and limited visibility especially in such key developing market corridors. 

When faced with just 57% cold chain compliance on these key lanes, company leadership confronted a critical decision: either initiate a multimillion-dollar overhaul of its transport infrastructure or find a smarter way to close the gaps at scale while preserving market share and brand promise. The company chose to close the gaps with Decklar’s Decisions AI with real-time visibility. 

The Challenge: Protecting Product Quality Without Costly Overhauls  

The chocolatier needed precise, real-time control across its cold chain to reduce: 

• Cold Chain Violations & Enroute Stoppages: Non-adherence to temperature thresholds especially in high-temperature zones and during truck stoppages risked spoilage, retailer rejections, and potential brand damage.
• Manual Monitoring & Audits: Quality teams at distribution centers manually opened shipments to inspect conditions, introducing delays, labor costs, and contamination risks.
• Unenforceable SLAs on Quality & Transit Times: The lack of granular, lane-level insights made it difficult to define and enforce SLAs with transporters. Inconsistent adherence to temperature controls and schedules led to variability in compliance, delays, and service quality. 

What They Tried & Why It Didn’t Work

1. ERP & Planning Systems (e.g., Oracle, SAP): These systems held static records of cold chain compliance and transit times but couldn’t validate accuracy or trace root causes, making performance improvement reactive and unreliable.

2. Carrier Aggregators: These platforms offered basic location data but lacked temperature visibility or actionable insights for SLA or compliance forecasting. 

3. Track & Trace Solutions: While some solutions combined location and temperature data, they failed to analyze lane- or node-level risk, predict quality release confidence, or establish chain-of-custody and root cause during violations. 

Enter Decklar’s Real-Time Decisions AI with Visibility 

Instead of rebuilding its transport infrastructure, the chocolatier adopted Decklar’s real-time Decisions AI platform, tagging each Full Truck Load (FTL) for: 

• Temperature, Light, and Location Monitoring 

• TOR, MKT, and Alert Response Scoring 

• Lane-Level Compliance Scoring and POI Intelligence 

• Vendor Benchmarking and Advisory Notifications 

• Automated Quality Release at Receiving Sites 

Day 1: Operational Visibility Across Cold Chain 

In the first quarter of implementation, the chocolatier: 

• Gained Full Visibility: Into transporter behavior, cold chain compliance, and unscheduled stoppages. 

• Enabled Proactive Monitoring: Replacing post-facto temperature logs with real-time exception alerts to the control tower. 

Day 2: Decision AI at Scale 

From the fourth month onward, Decklar’s decision intelligence accelerated the desired outcomes: 

• Identified 10 Critical Lanes for Transportation Overhaul: Heatmaps and violation patterns pinpointed lanes requiring systemic intervention, enabling targeted infrastructure investments. 

• Proactive Risk Anticipation Across the Remaining 82 Lanes: Decision AI flagged high-risk Points of Interest (POIs) using real-time and historical trends, offering minimal yet precise alerts to control tower teams. 

• Automated Quality Release: Real-time shipment condition data enabled automatic GRN and QA clearance, cutting dock processing time and labor. 

• Elevated Cold Chain Compliance from 57% to 90%+: Achieved through targeted interventions, stronger vendor accountability, and agile control tower execution without overhauling the broader infrastructure. 

Why Decisions AI with Visibility Were Needed Together 

The combination of Decisions AI and Visibility was essential because: 

• Visibility Alone helped locate shipments but could not explain why temperature excursions were occurring, nor could it identify high-risk patterns across lanes and vendors. 

• Decisions AI Alone lacked the ground truth! It couldn’t anticipate spoilage risk or trigger corrective action without real-time environmental and geolocation data. 

Together, they empowered the leading chocolatier to move from manual cold chain audits to automated, predictive compliance. This boosted performance from 57% to over 90% without a full transport overhaul and enabled intelligent, automated quality release decisions at the receiving dock door. 

ROI Summary  

Direct Benefits: 

• Reduced Rejections & Spoilage Loss: $1M/year 
• Quality Team Productivity Gains: $300K/year 

Indirect Benefits: 

• Improved Dock Turnaround Time 
• Fewer Retailer Rejections 
• Retained Market Share through improved brand promise and retailer confidence 

Transportation Overhaul Cost Avoided: 

• Estimated $400 saved per shipment across 20,000 shipments annually by enabling standard carriers to meet premium reefer standards, totaling $8M in annual savings 

Total ROI: 

• $9.3M in direct annual savings, plus retained market share 

Outcome: Precision Compliance Without the Price Tag

By selecting Decklar over a capital-heavy overhaul, the chocolatier elevated compliance by 33 percentage points and saved more than $9.3M annually. The company modernized quality control, automated decisions at the receiving dock, and strengthened brand equity in one of the world’s most temperature-sensitive product categories. 

Decklar is now central to smarter growth, trusted delivery, and cold chain excellence across every lane. 

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