Benefits of Big data in supply chain

The application of big data analytics runs through the supply chain from raw material source to after-sales service. Most applications we know today are focused on market development, and the leading applications are represented in the intelligent applications developed in the market. At the same time, in the field of logistics and distribution, big data analysis has been used for recent years in conventional transportation management and vehicle scheduling, and in the operation of the supply chain, from inventory and labour configuration to optimize the operation of the entire supply chain. In this article, we will explore some benefits of Big data in supply chain.

Benefits of Big data in supply chain

The application of big data analytics in various fields can provide supply chain managers with a deeper perspective on how to effectively apply big data analytics in supply chain management at all levels of management

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Optimizes the decision-making system of the supply chain

The application of big data analysis can enhance the competitive advantage of the decision-making system of the whole supply chain in the aspects of marketing in the target market, optimization of supply chain inventory, and evaluation of supply chain operational risk. The supply chain generates a lot of information in the course of operation, and the members of the supply chain can easily obtain a lot of information from their POS, GPS, RFID and other channels, and turn this information into business intelligence through big data analysis.

The number of supply chains or businesses that use big data analytics to deliver accurate profits is growing, but most companies are not yet able to leverage big data analytics to grow their supply chains. The reason for this is that these companies don’t rush to spend a lot of money on new technologies and software applications without understanding the return on investment. Many companies are aware of the importance of big data, but they don’t know how big data analytics can drive supply chain operations. As a result, with the exception of a handful of large companies, such as Linkedin, Facebook, and Google, which have advanced information technology standards, most still don’t know what to do and how to do big data analytics.

Accurate Demand forecasting

Demand forecasting is an essential component of the entire supply chain. The barometer of market demand fluctuations, the sensitivity of sales forecast is directly related to inventory strategy, production arrangements, product out of stock and end-customer order delivery rate. A product shortage or OOS (out of stock) will bring huge losses to businesses. They need to specify accurate demand forecasting plans through effective qualitative and quantitative forecasting analysis and modelling tools combined with historical demand data and safe inventory levels.

For example, in the automotive industry, after the application of data analysis platform for accurate prediction, you can decide when to sell, when to promote, and a series of information, from design and development, manufacturing, after-sales marketing, logistics management and other aspects of optimization, to achieve efficiency improvement, and to bring customers a better user experience.

Inventory optimization

Mature replenishment and inventory coordination mechanisms eliminate excess inventory and reduce inventory holding costs. Through comprehensive consideration of demand changes, safety inventory levels, purchase advances, maximum inventory settings, purchase order volume, procurement changes, etc., supervision and optimization of inventory structure and inventory levels can be achieved.

Logistics efficiency

Optimization of transport schedules and routes.

Big data analytics is used to optimize inventory, determine the best distribution center and supply chain route selection, minimize transportation costs, and more. Among them, the most used is the choice of transport scheme and transport route. GPS can make the use of big data, from path optimization to transportation optimization. On the one hand, through the analysis of big data, the resource efficiency will be fully developed, with regular maintenance of machinery and equipment, regulate the behavior of transport workers, arrange the driving route of vehicles, thereby ultimately improving the output efficiency of the logistics industry.

Network design and optimization

For investment and expansion, the costs, capacity and changes that companies analyze from a supply chain perspective are more intuitive, richer, and more reasonable. Organizations need to apply enough scenario analysis and dynamic cost optimization models to help them make distribution integration and line-setting decisions.

Production effeciency

The fastest growing part of big data analytics is applications in the field of production resources. For most manufacturing organizations, the majority of the cost of the organization is used for the consumption of production resources, accounting for about 50% to 90% of sales revenue, therefore, in the field of production resources to carry out big data analysis of the main demand is to significantly reduce procurement costs.

With the help of big data analytics, most manufacturing companies optimize the source channels of raw materials and further unify suppliers into the integrated operations of their own company. In addition, according to the management needs, under the guiding ideology of raw material strategy and cost risk balance, firms can also effectively group raw material suppliers according to some key indicators in the results of big data analysis, and adopt different cooperation strategies according to different grouping situations.

For example, with the help of big data analysis, Amazon can clearly understand the replenishment decision of various products, the co-replenishment decision of suppliers and the supply of single-resource goods, effectively realize the further expansion of enterprises, strengthen the efficient management of inventory, and improve the efficiency of logistics allocation, and jointly promote the management process of the entire supply chain.

Strengthening supplier relationship

Better relationship with suppliers based on adequate consumer preference analysis and consumer buying behavior. By analyzing the data from the factual category of consumer behavior, the company can better influence the judgment and decision-making of suppliers and promote the smooth progress of the negotiations between the two sides. For example, companies with big data can use price and transaction information to win negotiations on core products, and big data analytics can be used to develop and extend sales channels, automatically track product expansion, and get the best input-output ratio in the supply chain.

Summary

Big data analytics plays an important role in supply chain management. Big data analytics drives companies to work together throughout supply chain management to accomplish their strategic mission and to develop, plan and implement strategic decisions.

Although the development of big data analytics is still at an initial level, further analytical capabilities will have to be enhanced by the coordination and cooperation of the various functions of the supply chain. The application of big data analytics at all stages of the supply chain provides businesses with continuous and in-depth insights. If big data analytics results can be communicated and shared within or across supply chains, the overall benefits will be huge and far-reaching.