Thursday, April 17, 2014
Points to remember for building a Big Data supply chain
Big data can have a large impact on the supply chain and that is exactly how the majority of supply chain executives think about it. Using the different sales data, product sensor data, market information, events and news happening in the world, competitor data and weather conditions can give insights in the expected demand of products used or required in the supply chain. Using predictive algorithms the inventory can be optimized for Just-in-Time delivery and inventory based on real-time demand forecasts. Collaboration with different players within the supply chain can help to shape demand for all organizations within the supply chain to deliver a better B2B and B2C experience.
Following are the few points to remember:
1. Identify business goals
No one should deploy big data without an overall vision for what will be gained. The foundation for developing these goals is your data science and analytics team working closely with subject matter experts. Data scientists, analysts, and developers must collaborate to prioritize business goals, generate insights, and validate hypotheses and analytic models.
2. Make big data insights operational
It's imperative that the data science team works in conjunction with the devops team. Both groups should ensure that insights and goals are operational, with repeatable processes and methods, and they communicate actionable information to stakeholders, customers, and partners.
3. Build a big data pipeline
The data management and analytics systems architecture must facilitate collaboration and eliminate manual steps. The big data supply chain consists of four key operations necessary for turning raw data into actionable information.
These include:
- Acquire and store: Access all types of data from any platform at any latency through adapters to operational and legacy systems, social media, and machine data, with the ability to collect and store data in batch, real-time and near-real-time modes.
- Refine and enrich: Integrate, cleanse, and prepare data for analysis, while collecting both technical and operational metadata to tag and enrich data sets, making them easier to find and reuse.
- Explore and curate: Browse data and visualize and discover patterns, trends, and insights with potential business impact; curate and govern those data sets that hold the most business value.
- Distribute and manage: Transform and distribute actionable information to end-users through mobile devices, enterprise applications, and other means. Manage and support service-level agreements with a flexible deployment architecture.
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