AI Drives Intelligent Integration of Engineering Material Management

In the rapidly developing digital economy era, the rise of artificial intelligence (AI) has brought unprecedented opportunities to industries. As a global leader in communication and information technology solutions, ZTE closely tracks AI technology trends and actively explores innovative applications in material delivery for communication network engineering.

In communication network delivery projects, equipment assets serve as the core carriers of contracts and orders. Traditional asset management and delivery methods often involve multiple turnover points, scattered storage locations, various product types, unbalanced supply and demand, and mismatched supply and demand plans, all of which significantly affect project delivery efficiency. Therefore, ZTE proposes an innovative solution that intelligently integrates digital and physical aspects. Through digital business transformation, automated process orchestration, AI inference models, and machine learning algorithms, this solution achieves full-cycle management of material delivery. It includes accurate delivery, efficiency improvement, inventory reduction, intelligent prediction, and intelligent decision-making. Furthermore, this solution supports decision analysis and optimal estimation of production and delivery plans before orders are put into production, enabling complete, highly transparent, and fast-response material management services.

Digital Material Delivery Process

For global delivery projects, ZTE has deployed digital capabilities related to material requirements, physical material transfer, and project material delivery flows on the iEPMS digital delivery platform. This deployment aligns with the material delivery process and key user roles in communication engineering projects.

• Automatic Orchestration of On-Site Material Requirements

To address the high-frequency and low-efficiency issue in traditional manual material requisition processes, ZTE has deployed two functions on the iEPMS platform: engineering bill of material (EBOM) and electronic delivery order (EDO).

In managing EBOM operations, ZTE implements extended data design and supports customized configuration to accommodate various service scenarios and requirements. The extended data design encompasses different warehouses and storage locations, different packaging modes, material substitution relationships, contracts, and priority distribution rules. These features provide the system with conditions for automated data analysis.

When managing EDO operations, ZTE integrates inventory information, contract order scope, and EBOM list from upstream service links. Using automated orchestration and completeness analysis algorithms, it achieves batch automatic DO generation across multiple sites, and automatically splits bills based on different warehouses, material sources, and sites. Intelligent orchestration and optimization throughout the material requisition, outbound allocation, and site resource allocation processes enhance efficiency in preparation and picking for front-line engineering and warehouse personnel.

• Modular Construction for On-Site Material Transfer and Collection

Combining with the key path of on-site material transfer in communication projects, ZTE has independently developed a series of functions including site receipt (SR), warehouse receipt (WR), delivery return (DR), delivery moving (DM), stocktaking (ST), reconciliation, inventory query, and transaction query. These functions enable material data collection through mobile apps, and integration of packing details, ex-warehousing documents and total design quantities. This allows operators to perform self-signing, self-checking, immediate review and correction, and discrepancy checking at the construction site.

Moreover, upstream contract configurations, delivery batches, and turnover records are meticulously traced to achieve real-time visualization and precise global material turnover tracking. Embracing an intelligent and simplified approach, ZTE ensures meticulous and efficient management of on-site material flows, facilitating automated project delivery, acceptance and on-site risk management and control.

Visualized Material Delivery Cycle and Predicted Application of Machine Learning

During the full cycle of material order delivery, in addition to on-site delivery management, ZTE also manages and deploys material delivery through the supply chain management (SCM) cloud platform to ensure timely and complete delivery, smooth order performance, and prevent inventory redundancy.

ZTE divides the configuration, production scheduling, warehousing, delivery, customs affairs, delivery, and other services related to order delivery into different operational data languages. It uses "material code" as the master data, converting it into other operational languages to establish a data mapping relationship that connects the entire lifecycle of materials and visualizes their complete delivery process (Fig. 1).

To further support the reasonable formulation of project delivery plans and reserve a sufficient handling period for risks, ZTE utilizes the linear regression algorithm to model global historical logistics routes. It continuously trains and optimizes the prediction model to accommodate varying logistics periods across different countries around the world, thereby predicting more accurate arrival time. This helps in formulating reasonable project plans and dealing with material shortage or surplus risks promptly, ensuring the achievement of project delivery objectives and meeting customer requirements.

AI Operations in Decision Analysis for Delivery Planning Scenarios

The uncertainty in production scheduling, freight, and customs clearance periods may lead to inconsistencies between material supply and the engineering delivery plan. Therefore, ZTE employs scenario-based splitting and applies AI to accommodate both long and short delivery periods in the engineering and material supply plans.

• Short-term and small-cycle scenario: ZTE predicts supply and demand balance within one to three months. Based on specified site delivery requirements and estimated arrival time, ZTE intelligently forecasts material needs using a time series model. It uses the decision tree to solve the resource allocation problems across various projects and sites, thereby reducing labor costs associated with on-site material shortage predictions, complete delivery plans, and batch adjustment plan diagnoses.
• Long-term and large-cycle scenario: The objective is to deal with material preparation, inventory turnover, and scheduling over a period of three to six months or longer. Building upon the overall supply and demand balance algorithm, ZTE incorporates various uncertainties and potential input variables. It forecasts long-term material requirements using associated material regression prediction and multi-variable time series analysis. Furthermore, it supports decision-making regarding material production, demand, delivery, and optimal plan recommendations based on operational algorithms. ZTE is also exploring the continuous training and optimization of AI models for multi-variable scenarios to enhance the accuracy of prediction and optimal values.

As shown in Fig. 2, through the mutual nesting of large and small cycle scenarios, ZTE can address inventory turnover and material scheduling issues at various time scales throughout the project lifecycle, thereby improving the efficiency and resilience of material delivery.

At present, ZTE’s intelligent integration solution for engineering materials has been applied in more than 45,000 projects worldwide, serving over 2 million users. It manages a cumulative total of 800 million on-site delivered materials, significantly enhancing on-site material delivery efficiency while reducing consumption and costs.

In the future, ZTE will continue to explore innovative applications of the AI technology in the communications field, accurately identify service scenarios, improve the delivery efficiency throughout the project lifecycle, inject new vitality into communications network construction, and help customers continuously enhance network value.