Today, all of these forms of data are a problem to the supply chain. Tomorrow they offer great promise.
The traditional supply chain was designed to use structured, clean data. But it turns out the most important data for the supply chain is often unstructured data. (For example, customer service call center data, Twitter data, warranty and return information, customer rating and review data.) In the next five years, supply chain processes will need to listen, test and learn based on the sensing and pattern recognition from big data technologies. It will dramatically change supply chains to improve a wide range of conditions including food safety, biologic drug efficacy and sensing the customer response for new product launch acceptance.
New product introductions are the largest contributor to inventory obsolescence and forecast error. Shortening the time to sense true customer acceptance offers great promise to sense true channel demand and reduce the time to respond to improve product availability during the launch phase.
Analytics offers promise to connect the extended supply chain. It also offers great promise to enable real-time benchmarking. Today, the supply chain is blind to its potential. Traditional benchmarking techniques are difficult because they are static and are the inputs lack common data models and data definitions to enable comparisons. Not so with cloud based technologies. Yes, the cloud will make technology deployments easier; but more importantly it will allow real-time sensing on benchmarking data.
Incorporating advanced data analytics methods into the forensic accounting process enables us to expand our scope to a broader population of transactions. Usually when we are trying to help a company identify fraud, waste or abuse in their supply chain, data analytics helps us uncover even more details such as, for example, invoice charges that contradict the contract. This better enables us to help the client recover lost funds.
Our team of experts in Big Data technologies and BI will help to identify your Big Data problem and assist you in setting up a proof of concept in your datacenter or on the cloud.
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