Predicting the Best Procurement Option: Supply Chain

The Business Challenge:

A Leading Aircraft and Diesel Engine Manufacturer wanted to bring improvements to their Inventory Management by making data driven decisions. Specifically, they wanted to get an in-depth view into the drivers of Procurement, how could they reduce the cost of Procuring of goods by way of identifying the best location and the best price.

The Solution:

Using Multiple Regression Models built within DSF, a predictive forecasting model was developed which allowed the manufacturer to input data into the model, and develop specific forecasts for different categories of products purchased from overseas suppliers.

What the data revealed:

The client was surprised to learn that they could reduce more than 30% of their procurement cost by mapping of the right location at the right price.


Based on the analysis, Yottasys Consulting Team provided the client with actionable recommendations and corresponding procurement projections. The new approach helped one of their division to reach, reduced Production cost goals by following a much enhanced just in time (JIT) approach

Data Center Outages:

The Problem Statement:

There is a cluster of Servers. Whenever there is failure/anomaly in any of the server, a report is logged. Some of the features of the log report are

Time of Failure

Service that led to the failure

Product for which the server was running

Cluster Number

No. of Servers in the cluster

The problem is to find patterns from available data as to when such anomalies can occur. Since, logs are generated only when there is a failure, there is no way to get the various features/Data beforehand.

The Solution:

DSF is being leveraged to proactively predict outages in their hosted environments and data-centres by capturing various information right from their server installation data to server configuration data, updates/upgrades data, and facility information such as power and temperature fluctuations to the personnel who maintain the servers’ data


DSF platform uses machine learning to detect anomalies across massive data sets.

Our algorithms automate the analysis of an organization’s log data to find anomalies, link them together, and give you real insight into what’s happening with your data.

DSF helps IT security and operations professionals identify advanced security threats and IT performance problems faster and more accurately, eliminating manual effort and human error while reducing false positives.


The Analytical Revolution Of Sensors

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The Search Disruption

There are two specific events which mark this decade, first one being the emergence of disruptive business models such as e-commerce models for retail, travel, transport and services and the second one is the emergence of disruptive technologies such as search and analytics technologies. Both of these events present a series of opportunities in terms of doing cheaper business with faster implementation times, while at the same time they possess a serious threat to the old business models and software licensing models.