Rechercher | Babafig 🇷🇪

a partagé un lien

2026-05-21 16:15:23

SCM Champs Turns Failing SAP EWM Into Real ROI

Most SAP EWM implementations quietly fail after go-live — no hypercare, undertrained super users, and a Phase 2 that never arrives. SCM Champs fixes what your original implementation left behind. From post-go-live stabilization and process gap closure to EWM rescue and operational realignment, SCM Champs helps US warehousing leaders extract real, measurable value from SAP EWM — long after cutover. Don't let your EWM become an expensive spreadsheet. SCM Champs delivers the Phase 2 you were promised.

URL: https://www.linkedin.com/feed/update/urn:li:activity:7447919345965510656

WWW.LINKEDIN.COM

SAP EWM Implementation Failures: Causes and Fixes | SCM Champs Inc. posted on the topic | LinkedIn

I've seen 7-figure SAP EWM implementations turn into glorified Excel replacements within a year. What went wrong? The same pattern every time: → No post-go-live hypercare plan → Super users undertrained and overloaded → Critical process gaps pushed to “Phase 2” (which never came) → KPIs measured at go-live… and never reviewed again SCM Champs helps organizations derive real operational value from EWM Go-live. Post-go-live stabilization Structured hypercare & user enablement Process gap closure Operational realignment EWM rescue & optimization Deliver the Phase 2 that never happened 👉 Fix it before the cost of doing nothing gets higher. #SupplyChainManagement #SCMChamps #WMS #OperationalExcellence #CostReduction #EWM #WarehouseOperations

1.8k

a partagé un lien

2026-03-03 10:02:35

🔍 Are you making the right choice in visual anomaly detection?

Choosing the right model for detecting visual anomalies can make or break your operational efficiency. This insightful article dives into whether you should learn from defects or normality, emphasizing that your decision should hinge on the potential cost of errors. It compares two approaches in an industrial case, providing actionable advice tailored to your operational constraints.

Reflecting on my own experiences, I've seen how the wrong model can lead to costly mistakes. Understanding the nuances of these approaches can empower you to enhance your quality control processes.

Make informed decisions that drive success in your operations!

Read more here: https://blog.octo.com/octo-article-de-blog-10
#VisualAnomalyDetection #QualityControl #DataDriven #MachineLearning #OperationalExcellence

Détection d'anomalies visuelles automatique : faut-il apprendre du défaut ou de la normalité ?

Comment bien choisir son modèle de détection d'anomalie visuelles ? La réponse dépend du coût de vos erreurs. Cet article compare deux approches sur un cas industriel, et guide votre choix selon vos contraintes opérationnelles.

3.6k