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Achieving the "dual carbon" goals has now become a core agenda for national development. This presents the bioenergy industry with unprecedented strategic opportunities while simultaneously imposing higher demands on the precision and agility of its internal management. In this process, the value of Enterprise Performance Management (EPM) is being redefined. Leading companies are no longer satisfied with merely using Excel to create reports and conducting fixed annual budgeting. They are focusing on integrating data flows across production, costing, supply chain, and other segments to build a real-time, transparent digital panorama. This enables flexible budget response, precise cost control, and proactive risk warning in a rapidly changing market.
As the flagship and reform pioneer of the SDIC Group in the bioenergy field, the rapid development of SDIC Bioenergy Science and Technology Investment Co., Ltd. (referred to as "SDIC Bioenergy") serves as a vivid illustration of this trend. With the gradual establishment of its national production capacity layout and the continuous expansion of its business scope, the traditional management model is facing multiple challenges: data is scattered like isolated islands, group-level control sometimes struggles to grasp the subtle nuances of business operations, while demands from regulators and the market for transparency and efficiency are becoming increasingly urgent. In this context, building a unified, intelligent data governance and reporting platform holds significance far beyond a mere technical upgrade. It will become the core hub for the company to connect its management arteries and achieve a holistic view. It is not only the digital engine supporting its role as a "Dual Hundred Enterprise" in deepening reforms but also an intrinsic need for it to fulfill its national energy strategic mission and navigate steadily in the industry's new journey.
▌ SDIC Bioenergy: Data Dilemma Under Multiple Co-existing Systems
The SDIC Bioenergy Data Governance and Reporting Platform Construction Project was implemented by Changchun Jiliang Tianyu Bioengineering Co., Ltd., a subsidiary of SDIC Bioenergy Jilin Company, responsible for providing unified construction and service support for SDIC Bioenergy Science and Technology Investment Co., Ltd. and all its subsidiary and controlled enterprises. Prior to the project's initiation, to support rapid business development, SDIC Bioenergy had successively built and put into operation multiple business systems including the NC financial system, expense control and reimbursement system, OA office system, electronic imaging system, and one-card system. These systems adequately met the needs for basic business processing and process control in their respective domains. However, with the continuous improvement in management refinement requirements and the sustained expansion of business scale, the original decentralized system architecture and the data processing model heavily reliant on manual operations gradually became bottlenecks hindering the release of data value and the enhancement of management efficiency.
● Proliferation of Systems, Difficult to Form a Global View
The NC, expense control, OA, and other systems built earlier by the company operated like independent "data silos," each running with its own standards. Financial data, business data, and operational data were segregated in different systems, unable to flow and integrate smoothly. For management to obtain a comprehensive cross-system business analysis report, it often required manual data extraction from multiple systems followed by tedious splicing and verification. This process was time-consuming and laborious, and it was difficult to guarantee data timeliness and consistency, failing to support rapid, comprehensive insights and decision-making regarding operational status.
● Proliferation of Manual Reports, Compromised Data Quality and Efficiency
In addition to system data, a large number of reports within the company still relied on manual creation in Excel and circulation via email. These manual reports not only took a long time to compile but were also prone to human errors introduced through repeated copying and pasting, leading to inconsistent data definitions and uneven quality. More critically, the manual processes caused severe data delays. Reviewing last month's data at the beginning of the current month became the norm, meaning decisions were often based on "past information," unable to respond agilely to market changes and operational issues, thereby embedding risks of management lag and decision-making bias.
● Lack of Deep Data Mining, Insufficient Strategic Support Capability
Due to scattered data and difficult-to-guarantee quality, the company's data analysis mostly remained at the level of simple statistical summaries, unable to conduct effective deep mining and correlation analysis. For example, it was impossible to perform linkage analysis between production cost fluctuations and multi-dimensional data such as raw material purchase prices, production line efficiency, and energy consumption, making it difficult to accurately pinpoint key areas for efficiency improvement. It was also impossible to build predictive models based on historical data to provide forward-looking insights for budget preparation and market strategy formulation. The value of data was far from being fully tapped, failing to effectively transform into core momentum driving business optimization and strategy implementation.
● Urgent Need for Integration and Expansion, Original Architecture Struggling to Support
With the continuous expansion of SDIC Bioenergy's business scope and the exploration of new business models, higher demands were placed on the breadth and depth of data integration. The existing decentralized system architecture lacked flexibility and scalability. Each new business or reporting requirement potentially faced complex system integration challenges, with long development cycles and high costs. The company urgently needed a unified, platform-based data foundation to provide stable, agile data support capabilities for the rapid iteration and innovation of future business.
▌ Focusing on Business-Finance Integration, Connecting and Optimizing Data Systems
To address the challenges above, SDIC Bioenergy initiated the Data Governance and Reporting Platform Construction Project, collaborating with Intcube to jointly build an enterprise data sharing platform. The project takes data governance as the entry point, unifies data standards, constructs management mechanisms and systems for data sharing, data quality, and data security, and promotes the establishment and optimization of SDIC Bioenergy's data asset control system. It unifies and standardizes various data definitions and standards, advances business-finance integration, strengthens and clarifies data management work, and enhances the data management and application level of SDIC Bioenergy and its holding companies. The specifics are as follows:
● Data Governance work primarily targets various management data (including data source systems currently in operation and systems planned for future implementation) managed by the SDIC Bioenergy Intelligent Management Service Center, aiming to clarify data standards, control systems, and norms.
● Data Sharing and Exchange Platform (Data Warehouse) primarily receives business and financial data from various information systems of SDIC Bioenergy. Unified data management is conducted within the data warehouse to build an overall decision analysis platform for SDIC Bioenergy, connecting to services such as the state-owned assets supervision system and the SDIC Group system. It also optimizes the construction of leadership cockpits focused on enterprise decision analysis planning and analysis themes.
SDIC Bioenergy, in the midst of its digital transformation journey, has always valued the construction of an enterprise data sharing platform. On the basis of achieving unified digital management, the company deeply mines data value to provide strong support for its continuous optimization and development. Based on this consensus, SDIC Bioenergy and Intcube jointly formed a project management team. Centered around three core layers—data governance, business construction, and analysis presentation—and aligned with SDIC Bioenergy's specific management needs, they established a project implementation plan of "overall planning, phased implementation." The team will take data governance as the starting point, focus on business-finance integration, connect and optimize data links across systems, achieve full-platform data analysis, and thereby construct standardized management processes and a comprehensive group management system.
Establish a closed-loop data governance process – Build a process encompassing data quality issue verification, feedback, correction, and tracking.
Establish management processes and monitoring points – Set up management processes and monitoring points for the pre-event, in-process, and post-event stages to efficiently enhance the timeliness of document submission by each enterprise.
Establish fixed document specifications – Formalize the roles, functions, and workflows in the data management process into standardized documents.
Strengthen data integration and real-time linkage – Build a data warehouse to effectively integrate and process data from existing business systems and other internal data resources of the company. Based on the analysis needs of various business departments, form data marts that meet different analysis scenarios and design flexible models for addition and modification.
Implement mining functions to enrich management decisions – Build a reporting platform, leveraging a mature multi-dimensional database system and the SDIC Group's existing reporting tools as application support. Realize functions such as data query, statistics, mining, and presentation to unleash data value and empower management decisions.
▌ Project Outcomes: Comprehensive Advancement of Data Governance and Analytical Decision-Making
The SDIC Bioenergy Data Governance and Reporting Platform Construction Project successfully built a group digital management system ranging from a basic data platform to a business analysis platform and further to a decision management platform through three core steps: unifying data standard structures, building multi-dimensional data models, and designing analysis themes. Through the collaborative efforts of the project teams from SDIC Bioenergy and Intcube, the objectives of this project phase have been fully achieved and put into application. The smooth implementation of the project has not only solidified the foundation for the enterprise's digital transformation but also propelled data into becoming a core productive force, comprehensively enhancing the enterprise's data governance level and analytical decision-making efficacy.
● Strengthened Decision Support Capability: Built an analytical cockpit, precisely matching senior management's data-driven decision analysis needs, helping the enterprise reduce costs and increase efficiency, and allowing managers to obtain more intuitive and clear data insights.
● Improved Data Governance Quality: Relying on systematic data governance, fundamentally improved and resolved data issues, effectively ensuring enterprise data quality, and significantly enhancing data usability, integrability, security, and ease of use.
● Promoted Data Openness and Sharing: Eliminated data inconsistencies, established standardized data application standards, achieved widespread data sharing, deeply applied data as a core asset to business operations, management optimization, and strategic decision-making, and fully released the value of data assets.
● Enhanced Risk Control Level: Based on a domestic multi-dimensional database system, constructed a rigorous data security mechanism, reduced data risk exposure, and strictly ensured data security.
● Driven Management Model Innovation: By standardizing and optimizing business processes and resource allocation, effectively enhanced the enterprise's business management capability.
● Optimized Business Management Processes: Previously, various business systems of SDIC Bioenergy often operated independently. After the implementation of data governance functions, traditional data silos were successfully broken down, the value of data flow was strengthened, a complete data control process was constructed, business management efficiency was improved, driving the enterprise's digital transformation and further expanding business boundaries.
Against the backdrop of the new era, SDIC Bioenergy continuously innovates its business models. Taking the bioenergy industry as its development foundation, it is gradually expanding into the bio-environmental protection and bio-chemical fields. While deepening its core business, facing the wave of informatization and digitalization, the SDIC Group maintains an open mindset, actively embraces change, and continuously improves its enterprise informatization construction.
The successful completion of this SDIC Bioenergy Data Governance and Reporting Platform Construction Project in collaboration with Intcube is a key step for the SDIC Group in driving enterprise digital transformation with data, opening up a new landscape for its management work. The project closely adhered to the SDIC Group's overall needs of "integrated control, systematic output, and digital operation," combined with the implementation requirements of the sector's overall informatization plan. It strengthened the unified collection and centralized management capabilities of business data for SDIC Bioenergy and its subordinate enterprises, established key enterprise indicator systems, provided multi-dimensional data presentation and risk warning support for group production operations and decisions, and assisted SDIC Bioenergy in optimizing its product layout and development path based on digitalization in the new economic environment. It leveraged the advantages of digital and intelligent management to the fullest, providing strong support for the optimization of the group's subsequent bio-chemical business layout and the planning of its implementation path.
