Supply Chain and Supply Chain Design Concept
A supply chain, also known as a supply and demand chain, is defined by the national standard logistics terminology (Mohammed & Duffuaa, 2022). It will include suppliers of the chain, the online shopping supply chain mainly serves online shopping consumers, and is based on the e-commerce platform. The online shopping supply chain has relatively few nodes, mainly including suppliers, manufacturers, and end users (Chen et al., 2022).
Supply chain design refers to rationalizing the selection and setting of nodes in the supply chain and the corresponding flow changes through a certain modelling method, ultimately maximizing the operational efficiency of the entire supply chain, quickly meeting consumer needs, and improving the ability to serve consumers (Zheng et al., 2022).
From the 1980s to the present, scholars who study supply chain design problems through mathematical programming models have continuously improved the relevant conditions of the planning models to be more in line with the actual production conditions of enterprises. From the linear programming model of supply chain design to the integer supply chain planning model, modelling is becoming more and more perfect and complex.
In the transaction process, goods pass from manufacturers to distributors, to retailers, and finally reach consumers, forming the so-called forward logistics, while reverse logistics is the delivery of goods from consumers to retailers and then to distributors or manufacturers. The main reasons for the reverse process and reverse logistics are the broad return policy and market competition.
SI-label-specific features (LSFs) technology has the following advantages in personalizing consumer demand: using large-scale data to construct label-specific features to capture consumer demand and behavioural patterns; accurately selecting relevant features through independent feature selection to avoid overfitting; reducing noise interference to improve prediction accuracy, especially for complex demand; and efficiently constructing features to optimize computational efficiency when processing large-scale data.
By analysing consumer buying behaviour and preferences, as well as historical data, companies can discover the correlation between specific target characteristics and sales performance. This insight helps optimize inventory management, promotional strategies, and product positioning to improve supply chain efficiency. At the same time, identifying consumers' individual needs and communicating them to supply chain teams can help adjust production plans, warehousing strategies, and logistics and distribution methods to better meet consumer demand. By analysing market trends and sales data, companies can quickly respond to market changes, adjust product mix, and predict popular products in advance, thus enhancing supply chain flexibility and adaptability. Ultimately, sales data and supply chain operation data are combined to optimize supply chain processes, reduce costs and improve delivery on-time rates to enhance customer satisfaction, increase sales, and improve enterprise competitiveness.