The lack of understanding of the supply chain has caused millions of Vietnamese farmers to lose out on distribution, benefiting a percentage of the final retail value. The following article will provide you with new trends in the world – Big data analysis application in inventory management and commodity supply chain.
We are on the verge of a major change in the way inventory is managed. This advancement is the result of large amounts of real data being regularly updated on the Internet on a regular basis and through the corporate world of enterprise software and smart products. In order to effectively utilize new data and compete with other companies, managers need to redesign the supply chain.
Big data analysis helps link all product interactions (including orders, inventory, and acceptance by potential customers) and deals generated by suppliers and competitors associated with the product through Internet websites and cloud-computing portals. This type of data is used by the material management system to help manage orders and distribute products through an extended supply chain of a company. In addition, any data that is consistent with the interaction of the product, derived from the external influencing factor of the company, can also be accessed and linked.
How big data analysis works?
Optimization algorithms can help to find and exploit the observed patterns, relationships and relationships between data elements and supply chain decisions – such as when to place them, how many spare parts should be placed, where to put them, etc. Such algorithms can be trained and tested using old data. They can later be used to implement and evaluate for robust performance based on the actual certification of customer needs. For example, how does the use of data acquisition and control tools help reduce costs / boost customer service?
This approach extends the view of rule analysis, which is seen as a method of leveraging big data analysis. Rule analysis, however, has evaded most users of big data analysis so far. There are some notable exceptions in sectors such as online clothing retail, where companies can see in real time, customers decide to buy (for example, to buy or not to buy) and also can change the price of each product often at a negligible cost. Online retailers, however, know very little about the probability that consumers will buy at every price it sets but can explore dynamically the expected demand from sales data.
Challenges and prospects
While this remains a challenge, it is clear that a new approach to exploiting all the data that is becoming available is inevitable, for connectivity, capacity, and transparency of resources. The data along with the ability to calculate and store large amounts of data is available at a low cost. Like all planning systems, evidence will be in the result, when smart systems based on this method are applied in practice.
Changes are coming to the world of inventory management and those embracing this change will advance ahead of the race. Successful implementation of big data analysis will require the active participation of many functions within the company along with a high degree of coordination with the upstream and downstream supply chain partners and commitments with customers.