Data Center ArchitectureData Center Operations

Data analytics and supply chain management

Data trends, statistics and quantifiable indicators had been a part of supply chain management for long. But all is changing now in a very rapid form.

The type of real time analysis, which is available, out of large gathered unstructured data is making the difference.  The big data analysis can uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful information that can help organizations make more informed business decisions at all levels.

Data analysis options are readily available and can be used in areas of inventory management, transportation, logistics and forecasting. In warehouses, digital cameras are used to monitor stock levels. This is taken further for forecasting. The same data from camera can be used through machine learning algorithms to be fed in an intelligent stock management system to predict ordering levels. In other words, the whole setup of warehouses and distribution centers can run on its own with very little human interaction. Similarly, real-time shop-floor data adds capability to conduct sophisticated statistical assessments from isolated data sets, aggregating them and analyzing them to reveal important insights in manufacturing. Traditional data monitoring, which would involve sales and order tracking and point of sales data, is now being supplemented and taken to next level of analysis with weather, events and news, with the aim to generate insights as how customer will behave leading to impact operations this week, rather than on a broad annual timeframe.

Today at the organization level, the data is available from various sources and it can be analyzed with the use of software in advanced analytics.

  • The tools for data mining sift through data sets in search of patterns and relationships.
  • Predictive analytics builds models for forecasting customer behavior.
  • Machine learning taps algorithms to analyze large data sets.
  • and Deep learning is more advanced offshoot of machine learning.

 Challenges & Obstacles:

The data analysis brings in lot of challenges and governance requirement.

  • The amount of data to be involved, quality of data, consistency and architecture are key.
  • The involvement of IT team, right mix of technologies and efficient structure along with data security and elimination of all risks, need to be given due importance.
  • Getting right resources and having right talent with right skill team is core activity.
  • The analysis platform should be flexible and scalable.
  • A clear vision is required, it should lead to a situation of over analysis and paralysis.
  • Finally, all these investments and initiative should make financial sense and be sustainable.

In short, the ability of an organization to draw inference from available unstructured data from various sources on real time basis will make the difference in the future.


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