What Is Supply Chain Analytics
In the past, organizations primarily used ERP systems for data tracking and collaboration across the entire business. Many organizations do their planning with a mix of spreadsheets and ERP systems, and others use cloud-based planning platforms.
But that soon becomes disjointed, chaotic, and disconnected. The supply chain of yesterday was sequential, linear, and static. From raw material to the consumer, the path was monolithic and predictable.
Today’s multi-dimensional supply chain change constantly. So, companies must move as fast as the markets.
Supply Chain Management ensures that you convey suitable items to the client while working with a variety of items in shifting amounts. It helps in giving things to the correct place at an adequate time. By the Supply Chain, it can lessen the working expense of an article that incorporates purchasing cost, production cost, and entire supply chain cost.
Therefore, the whole process decreases the estimation of fixed resources and augments the money inflow of the business resulting in high profit.
Automation in the supply chain, it had dramatically reduced human error to some extent. It has always been an incredible process that enables large scale organizations to make informed decisions according to the analysis output & suggestions. Because of this, the acquisition of items or services can be upfront and keep away from the hazard.
Today, many shipments and packages are being moved across the world with never-ending flows of supply chains. These supply chains are serving as the backbone of the world economy. In the age of growing technology, data is the fuel that drives an organization, regardless of the size.
To make the right decisions, they should have access to not only the right data, but they’ve to understand that data to make it useful.
Having analytics in a place represents the ability to make data-driven decisions based on- data, visualization, graphs, charts, and more. Supply chains usually generate enormous amounts of data.
Supply chain analytics helps to make sense of all this data — discovering patterns and generating crucial insights.
Different types of supply chain analytics strategies include
Descriptive analytics — It offers complete visibility and a single source of truth across the entire supply chain both internally and externally from the data.
Predictive analytics — It helps the companies to understand the outcomes, future scenarios, and business implications to mitigate disruptions and risks.
Prescriptive analytics — It assists organizations in solving issues and collating maximum business value. It also helps in business collaboration with logistic partners to save time in mitigating disruptions.
Cognitive analytics — It is very beneficial for organizations as it provides an answer to complex questions through NLP (Natural Language Processing) and assists companies to go through cumbersome issues to improve and eradicate the problems of the supply chain.
Supply chain analytics is the base for applying cognitive technologies like- AI to the supply chain process.
Like humans, these technologies — understand, interact, and learn, but at an enormous capacity and speed.
Conclusion
So, supply chain analytics is ushering in a new era of supply chain optimization. It can automatically go through massive amounts of data to help an organization improve forecasting, identify inefficiencies, drive innovation, respond better to customer needs, and pursue breakthrough ideas.
Other blog related to supply chain use cases: https://www.polestarllp.com/top-5-manufacturing-supply-chain-analytics-use-cases
