Analytic Solver vs. SAS Enterprise Guide

1. It is good tool for a mathematical model which is a single period and deterministic model 2. It is good for the users who are comfortable in handling the Excel Solver and needs to upgrade the Excel Solver for more than 200 variables 3. It works well for the multiple objective problems. 4. Difficult to manage the big model as 100 constraints and 2000 variables can limit the use of the tool's efficiency. 5. Its limitation is that a model designer can not make a big and complex model.

Incentivized

For writing out longer code creation for shaping data on complicated reports, the clean UI is helpful. If exploring data though, SAS Studio would be better suited given its easier interface for GUI graph building.

Incentivized

• Easy to use and learn.
• Strong optimization platform for business and supply chain problems which can be modeled in Excel
• It can handle multiple objectives and more than 8000 decision variables in a problem.
• Lots of tutorial and examples are available to solve the problem

Incentivized

• It can load a huge amount of data as compared to R Studio and Excel.
• Data processing speed is very fast, millions of records are loaded into this software very easily and data manipulation is also very easy.
• Inbuilt Statistical functions and procedures make it very comfortable to use for non analytics professionals as well.

• On the few occasions when I have used it to deal with problems of optimization of relatively large parameters (with a large number of restrictions and decision variables), the program has been slower, not substantially but slower, than programs such as the WinQsb, even when the latter runs on 32-bit machines and not 64. That has caught my attention, even though it is not a real problem for the uses I give to the program.
• Given my partial function as a university professor, it has been much more effective and practical to use other software, due to the limited options that the educational license associated with the software has.

Incentivized

• I would like to see advance interactions with external databases to be able to kill ongoing queries from SAS. As of now, you can stop pretty much any ongoing process besides the one running on a remote database (killing SAS/EG doesn't stop the remote process)
• When creating prompts for programs, it would be nice to be able to have conditional prompts (based on the selection of other prompts). The prompts are clearly a recent feature and constantly under development but I wish it would be more powerful.
• More of a SAS metadata issue but when loading SAS/EG (first connection to the server), it takes a few seconds which feels like a long time. I really don't understand why the initialization of the session can take so long. Don't get me wrong, this has no real impact on productivity but that 10s delay just feels really like eternity when you want to run some code in a new session.

On account of current user experience and the organization-wide acceptance.

It's not all bad, but I don't believe that an enterprise purchase of SAS is worth the expense considering the widely available set of tools in the data analytics space at the moment. In my company, it's a good tool because others use it. Otherwise, I wouldn't purchase a new set of it because it doesn't have some of the better analytical functions in it.

Incentivized

Although I use SAS support for information on functions, these are SAS related and haven't really come across anything that is specifically for SAS EG.

Incentivized

I've not worked hands-on with the implementation team, but there were no escalations barring a few hiccups in the deployment due to change in requirement & adoption to our company's remote servers.

The use of this software does not necessarily follow that it is "globally better" than others. In the department we have used this and others with similar characteristics, given that, as previously indicated, all the software has advantages and weaknesses with respect to other software with similar characteristics. Obtaining better results lies in the user's ability to detect those "benefits and weaknesses" and maximize their usefulness within the specific field of work in which they operate. In our case, one of the reasons that led us to try and use it, was related to trying to "tie" more processes to the same environment, which in this case is the one associated with the Excel database, in such a way as to reduce the initial manipulation and accommodation that should be made to the data if they come from different sources such as MATLAB, or WinQsb. This facilitates the use of software for the type of user who does not necessarily have deep knowledge of linear algebra or operations research, for example.

On the contrary, the most analytical and knowledgeable user manifested in a high percentage, preferring to use MATLAB as a tool, claiming that they have a greater and easier access to the calculation functions, which even in specific cases, could be modified.

Incentivized

Python-based platforms like Pandas or Spark are very good too at displaying data and do exploratory analysis. I definitely prefer them to SAS EG. It's just too slow, and doesn't let you peek into the data very easily. Lots of clicking, and I'd rather just write some code, rather do clicking.

Incentivized

• - It has allowed finding ways to optimize (minimizing costs or times) the field processes involved in various projects.
• It has even allowed, in specific cases where it was used for that purpose, to optimize the allocation of resources (people) to work in different jobs that present weekly variations of the activity that these people must perform.
• It has allowed the sensitivity analysis of projects to changes in the decision variables related to them, which, and in very dynamic and changing environments, resulted in substantial decreases in money losses.

Incentivized

• Faster decision making, through powerful big data handling functionalities.
• Faster operations on daily basis, once the project tree is built, unskilled personnel can use it in their daily operation.
• Don’t need to choose SAS EG if you are not going to be handling big data. (such as over 1 million rows and 50 columns)
• You need skilled personnel to build the initial project tree.

Incentivized