When shipping teams at a major European packaging manufacturer implemented their new AI-powered TMS last January, the promise was clear: 30% faster routing decisions, 25% fewer manual tasks, and smarter carrier selection that adapts to real-time market conditions. Six months later, dispatchers were still overriding 80% of AI recommendations, operations
A mid-sized German automotive parts manufacturer spent €800,000 on their TMS implementation, only to discover six months later that their new system couldn't handle their complex carrier network across 12 countries. The integration with their existing ERP systems broke down completely, requiring a full platform re-implementation. Sound
Your TMS operation just went dark. Labels aren't generating. Tracking updates stopped 15 minutes ago. You check the logs and see those dreaded error messages: "Rate limit exceeded" and "429 Too Many Requests." Sound familiar? Over 90% of organizations report downtime costs exceeding $300,
Your TMS was handling 500 shipments per day just fine last quarter. Now you're hitting 2,000 daily and response times crawl to minutes instead of seconds. Sound familiar? The TMS scalability problems you're experiencing aren't technical glitches—they're capacity walls. As
Your TMS workflow automation has more potential to fail in the first 90 days than any other critical period. Only 26% of organizations report being ready for automation implementation, yet 91% plan to expand automation investments. The difference between those who succeed and those who struggle? A systematic approach to
Your TMS went live six months ago. The initial rollout looked successful—carriers were connected, rates loaded, labels printed. But now you're dealing with webhook backlogs, users working around the system, and performance complaints from operations. Sound familiar? Up to 40 percent of TMS implementations fail by some
Multi-modal shipments create tracking nightmares that single-mode TMS configurations simply can't handle. Container-to-truck handoffs fail 23% of the time because your ocean freight API speaks different languages than your trucking carrier API. Rail-to-road transitions? Even worse when port delays cascade through every downstream mode. You need a TMS
When your FedEx rate lookup fails at 3:42 PM on Black Friday, over 90% of organizations report downtime costs exceeding $300,000 per hour. Every minute spent troubleshooting carrier API authentication errors costs your operations team revenue and customer satisfaction. The issue manifested as intermittent 401 responses during peak
Your TMS analytics dashboard shows perfect numbers. On-time delivery sits at 98%, carrier spend looks optimized, and route efficiency metrics hit industry benchmarks. Data quality issues directly impact business decisions and operational efficiency. From inaccurate decisions to failed audits, the cost of poor-quality data compounds quickly across operations, analytics, and
Poor TMS data quality costs more than you think. Poor data quality costs businesses an average of $12.9 million per year, and reduces overall shipping costs up to 15% annually when addressed properly. Your routing algorithms fail when product dimensions are wrong. Your carrier contracts become disputes when BOL
Your TMS data migration just failed. Three months of planning, hundreds of thousands invested, and now you're facing Monday morning with broken rate calculations, missing customer records, and a warehouse team that can't print labels. Sound familiar? You're not alone. Research shows that 30%
Your TMS shows a green checkmark for every shipment. Your dashboard claims 98% on-time performance. Then you get the call: three critical shipments disappeared somewhere between Chicago and Denver, and your biggest customer wants answers you don't have. Most TMS platforms promise real-time visibility but deliver something closer