QAD Adaptive ERP vs. TensorFlow

If you're setting up operations where you have to manage manufacturing builds from raw components to finished goods, I would recommend QAD. It's nice to have subassemblies part numbers for your builds and enter in the number of accepted quantities and rejects. QAD is very helpful if you have a lot of parts floating around. I would not buy QAD if you only have to manage less than ~20 parts... just use Excel.

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TensorFlow is great for most deep learning purposes. This is especially true in two domains: 1. Computer vision: image classification, object detection and image generation via generative adversarial networks 2. Natural language processing: text classification and generation. The good community support often means that a lot of off-the-shelf models can be used to prove a concept or test an idea quickly. That, and Google's promotion of Colab means that ideas can be shared quite freely. Training, visualizing and debugging models is very easy in TensorFlow, compared to other platforms (especially the good old Caffe days). In terms of productionizing, it's a bit of a mixed bag. In our case, most of our feature building is performed via Apache Spark. This means having to convert Parquet (columnar optimized) files to a TensorFlow friendly format i.e., protobufs. The lack of good JVM bindings mean that our projects end up being a mix of Python and Scala. This makes it hard to reuse some of the tooling and support we wrote in Scala. This is where MXNet shines better (though its Scala API could do with more work).

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• The QAD Enterprise application is great, we only started using this application a few years ago. The Master Scheduling Workbench has been a great improvement to our daily operations.
• The Web-based QAD Supplier Portal has also been implemented recently in our company and has been a huge help to our purchasing and materials department.
• The QAD support that we receive has helped our company grow and is a major asset in upcoming projects.

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• A vast library of functions for all kinds of tasks - Text, Images, Tabular, Video etc.
• Amazing community helps developers obtain knowledge faster and get unblocked in this active development space.
• Integration of high-level libraries like Keras and Estimators make it really simple for a beginner to get started with neural network based models.

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• The layout is old fashion. Can be more modern.
• QAD Adaptive ERP has alot of functions that can be overwhelming to new users

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• RNNs are still a bit lacking, compared to Theano.
• Cannot handle sequence inputs
• Theano is perhaps a bit faster and eats up less memory than TensorFlow on a given GPU, perhaps due to element-wise ops. Tensorflow wins for multi-GPU and “compilation” time.

The cost / benefit of changing to a different ERP will create a high cost and low benefit that's why I believe that we'll continue renewing QAD for a long time.

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Easy to use, zero or minimum cost to maintain the database (DBA etc) , no issue with OS ( Linux), stable database

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Support of multiple components and ease of development.

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I haven't contacted support for QAD yet.

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Community support for TensorFlow is great. There's a huge community that truly loves the platform and there are many examples of development in TensorFlow. Often, when a new good technique is published, there will be a TensorFlow implementation not long after. This makes it quick to ally the latest techniques from academia straight to production-grade systems. Tooling around TensorFlow is also good. TensorBoard has been such a useful tool, I can't imagine how hard it would be to debug a deep neural network gone wrong without TensorBoard.

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Use of cloud for better execution power is recommended.

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QAD is very easy to use once it's set up. It's basically an Excel sheet that can handle a lot more data points and faster. It's nice that you can dump the data stored in QAD to a CSV file and analyze in Excel. Careful narrow down the data searches to a limited number of points or Excel will crash. QAD is much easier to set up than Arena and SAP. And the numbering systems you can create in QAD is more customizable.

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Keras is built on top of TensorFlow, but it is much simpler to use and more Python style friendly, so if you don't want to focus on too many details or control and not focus on some advanced features, Keras is one of the best options, but as far as if you want to dig into more, for sure TensorFlow is the right choice

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• It help us to reduce the inventories (FG and RAW)
• It reduce the time to adjust to the constantly changing international customs laws

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• Learning is s bit difficult takes lot of time.
• Developing or implementing the whole neural network is time consuming with this, as you have to write everything.
• Once you have learned this, it make your job very easy of getting the good result.

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