Interaction Data Science and Supply Chain

In nowadays there a huge of researchers which concern in the intersections both of data science and supply chain management.

Domain knowledge and a broad set of quantitative skills are required by data scientist, even still there a lack in the literature in this topics. Intersection supply chain management with data science, predictive analytics and big data (DPB) have enormous topics that attract researcher and professional leader to dig up deeply. The combination of resources, tools and applications are growing rapidly and it has implication in the area of supply chain management. Moreover, the production of data growth fastly and it can be reproduced as information to create a decision in several sectors such as; medical practice, public policy, business decision and relation with supply chain management.

The conventional methods will be not able to handle big data. Based on this reason, the new methods and tools have been developed. The big-data revolution can be realised when supply chain researchers and managers understand how DPB works and contributes to help for creating a decision in the supply chain. Intersection DPB in business logistics and supply chain management will drive developing the new tools and transform the design of supply chain network. Traditional methods will need to be re-invented and some of steps or procedures will change.

Data Science as a Powerful Tools

Data science defined as the problem solver through the application of quantitative and qualitative methods. In this part, the domain knowledge is more important than how to realise it. One of the challenges in data science that in this field is not agnostic on the domain. The domain knowledge is more demanding than what we want to know.

Furthermore, the analytical skills and the business and management understanding are needed not only for academicians but also for professionals. The domain experiences and analytical capabilities are helpful to create a decision. The theoretical knowledge has an important role in the area of supply chain management along with the capability to implement analysis technique from a range of variety in quantitative disciplines which same with business fields.

In the forecasting and optimisation discipline, supply chain management scientist will be more useful if they understand how qualitative and quantitative methods work and the methods to optimise in the numerical case. The character of big data is unique because of the volume, variety, and velocity of data, which is easy to be accessed and stored. Moreover, this condition will drive for increasing demand for professionals with competencies in DPB. They can work in the several industries even in a carrier, manufacturer or retailer. The difference role will depend their positions. Furthermore, exploring the possibility about how big data can improve supply chain design requiring for further development.

The article was published: https://datascience.or.id/article/How-The-Data-Science-Transform-Supply-Chain-Design-5a8fa6e6