How DCMM Supports Digital Transformation and Data Governance at XCMG Mining Machinery Co., Ltd.

Introduction The presentation shares how DCMM (Data Center Maturity Model) assists XCMG Mining Machinery in its digital transformation journey.

Background XCMG Mining Machinery, a subsidiary of XCMG Group, faces urgent digital transformation needs due to complex supply chains and the demand for scalable, lean operations. National policies such as the 2007 “Informationization drives industrialization” and the 2020 “14th Five‑Year Plan” further motivate the shift. The company adopted DCMM and CMMM standards to guide a tiered, standards‑driven transformation.

Key Data Challenges 1. Unstandardized master data (materials, customers, equipment). 2. High data‑security risk due to low‑quality data. 3. Unclear data responsibilities throughout the data lifecycle. 4. Poor data timeliness, completeness, and accuracy. 5. Data silos across SAP, MES, CRM, WMS, etc. 6. Inconsistent data definitions.

DCMM‑Driven Goals Through DCMM assessment, the company aims to: fully map data assets, improve data quality across the lifecycle, ensure data security compliance, break data silos, enable rapid data readiness for analysts, and continuously release data value.

Implementation Process 1. Data Strategy – Planning : Adopt a business‑collaborative, data‑driven, process‑quantified approach to achieve leading operational indicators by 2025. 2. Data Strategy – Principles : Follow the “two‑integration” philosophy (industry‑integration + capability‑integration), emphasizing efficiency, data‑driven decision‑making, intelligent transformation, and continuous improvement. 3. Data Governance – Organizational Structure : Establish a Data Asset Information Department and a cross‑departmental Digital Specialist team to bridge business units and data initiatives. 4. Data Governance – Institutional System : Create 26 policies covering data asset management, classification, quality, security, and application, and conduct extensive training. 5. Data Architecture – Asset‑Oriented Platform : Integrate heterogeneous sources (SAP, MES, CRM, WMS) into a unified data warehouse and develop a material master‑data expansion system linked to SAP. 6. Data Quality – Continuous Improvement : Publish monthly data‑quality reports, set targets, and close the loop with KPI tracking. 7. Data Standards – Material Master Governance : Issue standards and procedures, automate data entry via the new expansion system, and improve data accuracy. 8. Data Application – End‑to‑End Support : Build a unified indicator system, management cockpit, IoT platform, and a smart‑mine management solution to provide real‑time insights and support decision‑making. 9. Data Security – Management, Strategy, and Auditing : Form a Confidentiality Committee, define encryption policies for design software, and implement comprehensive audit logs for data access and decryption.

Experience Summary - Using DCMM as an evaluation‑driven catalyst raised digital awareness across the organization. - Focusing on point‑of‑entry master‑data improvements delivered quick ROI and avoided over‑investment. - Leveraging a KPI‑based indicator system helped identify data‑source and quality issues. - Continuous training, incentives, and cross‑functional teams sustained momentum.

Future Outlook By 2023 the company aims for full‑chain digitalization; by 2025 it targets industry‑leading upstream‑downstream collaboration, positioning data as a core production factor and evolving into a benchmark digital factory.

Q&A Highlights - Digital transformation began before DCMM certification, but DCMM provided a systematic governance framework. - Organizational construction succeeds through top‑down leadership combined with bottom‑up digital specialists. - Indicator decomposition will eventually reach atomic metrics, aligning data‑warehouse design with granular KPI requirements.