Real-Time Supply Chain Analytics with Machine Learning, Kafka, and Spark
Supply chain analytics refers to the use of data, statistical and quantitative analysis, and other techniques to understand and optimize the performance of a company’s supply chain. This can include analyzing data on suppliers, production processes, logistics, and customer demand to identify bottlenecks, reduce costs, and improve efficiency. Supply chain analytics can also be used to identify risks, such as potential disruptions to the supply chain, and to develop strategies to mitigate those risks.
Analytics and Big Data Quotes
- “If we have data, let’s look at data. If all we have are opinions, let’s go with mine.” ~ Jim Barksdale
- “The temptation to form premature theories upon insufficient data is the bane of our profession.” ~ Sherlock Holmes, fictional detective
- “Not everything that can be counted counts, and not everything that counts can be counted.” ~ Albert Einstein
- “The goal is to turn data into information, and information into insight.” ~ Carly Fiorina, Former CEO of HP
- “The world is one big data problem.” ~ Andrew McAfee
- “The value of an idea lies in the using of it.” ~ Thomas Edison
- “The important thing is not to stop questioning.” ~ Albert Einstein
- “Listening to the data is important… but so is experience and intuition. After all, what is intuition at its best but large amounts of data of all kinds filtered through a human brain rather than a math model?” ~ Steve Lohr
