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A leading domestic catering enterprise currently has over 10000 restaurants nationwide, with multiple well-known chain catering brands.
The vast multi-level supply network, rapid store growth plans, and complex data system require supply chain teams to achieve refined operations through digital means and management processes upgrading, and help enterprises continuously reduce costs and increase efficiency.
The store coverage relationship remains to be optimized. The current network coverage relationship of customers is influenced by market-oriented administrative divisions, and often distribution centers within the province could meet the demand for stores within the province. With business expansion and a growing number of stores, many "seek far and neglect what lies close at hand", resulting in high end distribution costs.
The existing network is highly complex. With the increasing number of stores and warehouse density, manual estimation and experience can no longer meet customers' needs for more refined management, and supply chain resilience and risk resistance are also facing challenges.
Data scale and quality issues. The data size has reached hundreds of billions level, and since data of different links comes from different systems, in many cases the statistical caliber of system data is inconsistent.
By comprehensively considering factors such as distance, transportation rates, warehouse costs, and operation capabilities, the project team establishes an optimization model, re-examines the rationality of network coverage, selects, optimizes and adjusts the area with the greatest opportunity, and assists customers in promoting the implementation of the plan.
The project team uses the "Cyberverse" product to model at a fine granularity level, and compares the differences between multiple versions through what-if analysis. Meanwhile, the project team aims to enhance the solution capabilities of customer supply chain team through knowledge transfer and meet their needs for refined operation and continuous optimization of the supply chain.
The project team uses Python, SQL, and other tools to clean, verify, and analyze data, timely communicates with customers and confirms key assumptions regarding missing or poor quality data. Meanwhile, the project team cleans and defines data quality issues and improvement actions during implementation, assisting customer business and IT teams in optimizing data governance.
By applying digital solutions, the project team optimizes customer supply chain network relationships with finer granularity, which is expected to help customers save millions of end distribution costs. Meanwhile, customer teams can continue to use digital models to cope with future business dynamic changes, and truly help customers reduce costs, increase efficiency, and achieve high-quality business growth.