FORECAST

Your friendly neighbourhood advertising agency!

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About FORECAST
FORECAST is a two and a half decade old, Mumbai based, INS accredited Ad Agency. We have earned the reputation of a full service and trustworthy creative communication house. Team Forecast consists of professionals coming from highly multi-faceted backgrounds, brimming with ...
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$150 - $199/hr
50 - 249
1990
India
FORECAST
Your friendly neighbourhood advertising agency!
0.00/5 (0 Reviews)
4 Questions
  Demand forecasting softwares analyze and fetch historical demand data for an individual product or services. These softwares also delivers accurate forecast results for broader macro and micro-economic trends – using algorithms - to improve and optimize the sales or supply for future demand.    In demand forecasting for ecommerce, as with most analysis endeavors, data gathering efforts are crucial. Because you don’t want to be one among those who randomly stock products without actually knowing the trend and face a fall with a heap of unsold products.    But almost every ecommerce sellers have "that" already prepared in whatever ecommerce platform they are selling. Hence just by leveraging those data in appropriate tools or software, demand forecasting can be done.    Now lets see how one can choose demand forecasting software for their ecommerce business:        It must provide detailed reports regarding purchases, returns, shipments across all the ecommerce channel.   Must support multiple channel integrations. So that you don’t need to move the data from one platform to another.   The forecasting tool needs to propagate optimum utilization of available resources. Which would help ecommerce business to be very economical when it comes to purchasing of the product.   Does it supports the forecasting model that is most suitable for your business? There are different types of demand forecasting method, which is divided into two subcategories, quantitative techniques, and qualitative techniques. Read more about these techniques.   A good forecasting software should provide sufficient time to prepare for future demand with a fair degree of accuracy and reliability.    Using up-to-date demand forecasting model, inventory management becomes a much simpler task. The forecast models provide insight into when shifts occur, but more importantly, it’ll also show how big the shift is going to be.    Utilizing demand forecast models, it is possible to properly plan and manage inventory and human resources well in advance and with fewer disappointments.
  Demand forecasting softwares analyze and fetch historical demand data for an individual product or services. These softwares also delivers accurate forecast results for broader macro and micro-economic trends – using algorithms - to improve and optimize the sales or supply for future demand.    In demand forecasting for ecommerce, as with most analysis endeavors, data gathering efforts are crucial. Because you don’t want to be one among those who randomly stock products without actually knowing the trend and face a fall with a heap of unsold products.    But almost every ecommerce sellers have "that" already prepared in whatever ecommerce platform they are selling. Hence just by leveraging those data in appropriate tools or software, demand forecasting can be done.    Now lets see how one can choose demand forecasting software for their ecommerce business:        It must provide detailed reports regarding purchases, returns, shipments across all the ecommerce channel.   Must support multiple channel integrations. So that you don’t need to move the data from one platform to another.   The forecasting tool needs to propagate optimum utilization of available resources. Which would help ecommerce business to be very economical when it comes to purchasing of the product.   Does it supports the forecasting model that is most suitable for your business? There are different types of demand forecasting method, which is divided into two subcategories, quantitative techniques, and qualitative techniques. Read more about these techniques.   A good forecasting software should provide sufficient time to prepare for future demand with a fair degree of accuracy and reliability.    Using up-to-date demand forecasting model, inventory management becomes a much simpler task. The forecast models provide insight into when shifts occur, but more importantly, it’ll also show how big the shift is going to be.    Utilizing demand forecast models, it is possible to properly plan and manage inventory and human resources well in advance and with fewer disappointments.

 

Demand forecasting softwares analyze and fetch historical demand data for an individual product or services. These softwares also delivers accurate forecast results for broader macro and micro-economic trends – using algorithms - to improve and optimize the sales or supply for future demand.
  

In demand forecasting for ecommerce, as with most analysis endeavors, data gathering efforts are crucial. Because you don’t want to be one among those who randomly stock products without actually knowing the trend and face a fall with a heap of unsold products.
  

But almost every ecommerce sellers have "that" already prepared in whatever ecommerce platform they are selling. Hence just by leveraging those data in appropriate tools or software, demand forecasting can be done.
  

Now lets see how one can choose demand forecasting software for their ecommerce business:       

  • It must provide detailed reports regarding purchases, returns, shipments across all the ecommerce channel.
      
  • Must support multiple channel integrations. So that you don’t need to move the data from one platform to another.
      
  • The forecasting tool needs to propagate optimum utilization of available resources. Which would help ecommerce business to be very economical when it comes to purchasing of the product.
      
  • Does it supports the forecasting model that is most suitable for your business? There are different types of demand forecasting method, which is divided into two subcategories, quantitative techniques, and qualitative techniques. Read more about these techniques.
      
  • A good forecasting software should provide sufficient time to prepare for future demand with a fair degree of accuracy and reliability.
       

Using up-to-date demand forecasting model, inventory management becomes a much simpler task. The forecast models provide insight into when shifts occur, but more importantly, it’ll also show how big the shift is going to be.   


Utilizing demand forecast models, it is possible to properly plan and manage inventory and human resources well in advance and with fewer disappointments.

I would have phrased the question better. Will try to provide short answers to all questions part by part from my perspective:Where is BI going? Short answer: BI is going places, and gong there fast. In my 2010 BI model, we valued the BI software market at about $7.2 billion, growing at a global rate of 6.5% on an average, which is significant compared to more established enterprise technologies. I expect BI to increasingly make an entry into iel smith emerging markets, especially China, India, and Latin America. @Unnati Chauhan BI is going to every single enterprise application and delivering more value than the sum of parts. With Ent. Search, it is helping answer casual user queries. With BPM, it is providing perspective to CEP. What are the biggest problems with the existing established players, and how are startups trying to disrupt them?As with all establishments, the problems are of sustainable growth, keeping BI relevant to the needs of a changing demographic of end-users, being on the forefront of business issues, and noticing trends. Some of the common issues are:    Dealing with semi-structured data, and how to include the same in analysis  How to best use user-generated social content  How to deal with the sheer growth in the volume of enterprise and social data  How to better integrate into other information management technologies and enterprise applications  How to come as close to real-time (right-time, if you will) as required  How to deal with Big Data   This is obviously not an exhaustive list.Startups: One very successful startup that came into prominence in the last few years is Qliktech, which defined a radical approach to data analysis doing away with OLAP cubes. Some other companies are trying to come up with newer ways of data visualization. Some such as Jasper and Pentaho are open-source representations of BI. Newer players have BIRT as a starting point, so building a solution becomes less cumbersome. Still others are innovating with in-memory, in-database, MPP driven architectures and analytical databases.
I would have phrased the question better. Will try to provide short answers to all questions part by part from my perspective:Where is BI going? Short answer: BI is going places, and gong there fast. In my 2010 BI model, we valued the BI software market at about $7.2 billion, growing at a global rate of 6.5% on an average, which is significant compared to more established enterprise technologies. I expect BI to increasingly make an entry into iel smith emerging markets, especially China, India, and Latin America. @Unnati Chauhan BI is going to every single enterprise application and delivering more value than the sum of parts. With Ent. Search, it is helping answer casual user queries. With BPM, it is providing perspective to CEP. What are the biggest problems with the existing established players, and how are startups trying to disrupt them?As with all establishments, the problems are of sustainable growth, keeping BI relevant to the needs of a changing demographic of end-users, being on the forefront of business issues, and noticing trends. Some of the common issues are:    Dealing with semi-structured data, and how to include the same in analysis  How to best use user-generated social content  How to deal with the sheer growth in the volume of enterprise and social data  How to better integrate into other information management technologies and enterprise applications  How to come as close to real-time (right-time, if you will) as required  How to deal with Big Data   This is obviously not an exhaustive list.Startups: One very successful startup that came into prominence in the last few years is Qliktech, which defined a radical approach to data analysis doing away with OLAP cubes. Some other companies are trying to come up with newer ways of data visualization. Some such as Jasper and Pentaho are open-source representations of BI. Newer players have BIRT as a starting point, so building a solution becomes less cumbersome. Still others are innovating with in-memory, in-database, MPP driven architectures and analytical databases.

I would have phrased the question better. Will try to provide short answers to all questions part by part from my perspective:

Where is BI going? Short answer: BI is going places, and gong there fast. In my 2010 BI model, we valued the BI software market at about $7.2 billion, growing at a global rate of 6.5% on an average, which is significant compared to more established enterprise technologies. I expect BI to increasingly make an entry into iel smith emerging markets, especially China, India, and Latin America. @Unnati Chauhan
BI is going to every single enterprise application and delivering more value than the sum of parts. With Ent. Search, it is helping answer casual user queries. With BPM, it is providing perspective to CEP.

What are the biggest problems with the existing established players, and how are startups trying to disrupt them?
As with all establishments, the problems are of sustainable growth, keeping BI relevant to the needs of a changing demographic of end-users, being on the forefront of business issues, and noticing trends. Some of the common issues are:  

  1.  Dealing with semi-structured data, and how to include the same in analysis 
  2. How to best use user-generated social content 
  3. How to deal with the sheer growth in the volume of enterprise and social data 
  4. How to better integrate into other information management technologies and enterprise applications 
  5. How to come as close to real-time (right-time, if you will) as required 
  6. How to deal with Big Data  

This is obviously not an exhaustive list.

Startups: One very successful startup that came into prominence in the last few years is Qliktech, which defined a radical approach to data analysis doing away with OLAP cubes. Some other companies are trying to come up with newer ways of data visualization. Some such as Jasper and Pentaho are open-source representations of BI. Newer players have BIRT as a starting point, so building a solution becomes less cumbersome. Still others are innovating with in-memory, in-database, MPP driven architectures and analytical databases.

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FORECAST
1502-B-1, Universal Majestic, 15th Floor, P.L. Lokhande Marg, Behind R.B.K. International School, Chembur, Mumbai, Maharashtra 400043
India
91-9867789991
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