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What is the founding concept of Theme Work Analytics?

Companies across the globe have derived considerable productivity gains by implementing ERP, SCM and CRM systems during the past decade. Integrating the transaction processing systems has resulted in enhanced operational efficiency.

The next step has been to improve the effectiveness of decisions. Many firms have installed data mining and data warehousing systems with the expectation that they would provide the critical insights needed for decision effectiveness. There are terabytes of data sitting in such systems but without providing the knowledge and wisdom for competitive success. Investment in these systems has not yielded the desired solution.

We have identified the gap between the solutions and systems as the need for building appropriate models and their use in intelligent decision support systems . Theme Work Analytics is positioned to bridge this vital gap.


Can you explain this with an illustration?

Let us say a firm has implemented an excellent ERP system which contains the Inventory Management module for finished goods. It helps to smooth out operational wrinkles and ensures steady supply of goods to the market place. It operates with the concepts of replenishment level and replenishment quantity. There is nothing within this system however to either forewarn about changes in customer preferences or to seek changes in replenishment level or quantity based on cost changes. Hence the firm is caught in a situation of clocking sales without required margins.


Won’t a well designed SCM system make this happen?

Yes and the key here is to have a well designed system. The interplay between demand and supply parameters has to be captured in a dynamic mode and utilized to alter decisions on replenishment. Hence we are talking about models that are appropriate and self learning . That is the hallmark of any Intelligent System.


Please elaborate this concept.

Decision Support Systems (DSS, for short) are usually designed with a mathematical model and with parameters fixed based on available data. Both need to be updated dynamically. The market experience based on current quarter should be used to validate or update the parameter values. The annual data should be used to validate or change the model itself. If a system is capable of doing this automatically then we call it an Intelligent System.


Which industry has implemented such Intelligent DSS?

In our assessment the travel and hospitality industry comes close. The interplay between demand and supply changes in real time mode is exploited well in the revenue/yield management systems of the airlines.


What are the prerequisites for Intelligent DSS?

Data with sufficient details or granularity is a must . When an integrated ERP or CRM system is implemented it provides the base for data collection. In the absence of integrated systems in a company, data warehousing can facilitate this.

Next is comprehensive business domain knowledge which covers the internal functioning of the organization and of external interface to the market. Competitive posture of the organization in current context and ability to identify the trends are integral here.

Model building skills assume criticality at this stage. In terms of techniques, be it statistical, simulation or optimization or performance of available tools in various platforms a high level of expertise is required.

Finally the ability of the Modeler to reconcile conflicting interests of stake holders and find a win win solution for the organization is essential.

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