Ansys Discovery vs. aPriori Technologies vs. PyBullet

From my experience aPriori is a classical algorithm in data mining. It is used for mining frequent itemsets and relevant association rules. Also, it is devised to operate on a database containing a lot of transactions, for instance, items brought by customers in a store. Whereas aPriori requires high computation if the itemsets are very large and the minimum support is kept very low and the entire database needs to be scanned.

Incentivized

Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

• Sheet Metal-Process Group, Transfer Die is at the Best, which gives detailed operation setup for the Parts as well as shows the number of parts in the Sheet i.e nested Loop thereby reducing the Scrap as well (Scrap Management)
• Forging-Steel: Very good for for medium and complex medium Hot Forging type parts. Machine overhead data is very detailed where facts are used for the negotiation i.e Electricity Rate, Power, Labour Rate, Maintenance rate, etc.
• Surface Treatment / Other Secondary Process such as powder coating, wet coat, electro plating: These all work well. We can also use masking options the treatment, In the event of of any leak tests or X-Ray inspections, we can add the process which will then account the pre/post operation time for the treatments
• 2-Model Machining: Machines and routing combinations are huge and can be used to determine the most economic way to produce these parts. We can also override to find the supplier methodology and cost. It gives very detailed information on the feed rate, speed rate, and power capacity.
• Geometric Dimensioning and Tolerating (GD&T) can be added on the GCD's which will help detect the super finishing process, which is another advantage. In the current 2016 R2 SP, aPriori can detect it from the CAD Models
• Design to Cost feature, provides the design engineers as well as cost engineers to understand the which feature take most cycle time and whether changing that feature reduces the complexity in machining thereby cycle time can be reduced

• Customization of the cost logic is tricky.
• Full products costing & process involved in the proprietary parts are less or not available for costing
• Sheet metal logics for costing may be improved.
• Focus on Electronics Cost models
• Electronics - Component Costings

• Not to Lose the Position of Product Cost Management Importance
• Make Costing Simple and Ease
• Traceablitlity of Previous Costed
• Helps Design Engineers to actually look for the Which Feature contributes for the Most Cycle Time and help Design Engineers to re-design the CAD Model and look for the changes in the Cycle Time and the Machining parameters
• Interactive Cost Insight

I'm fairly happy with the product. My company appreciates the ability to customize the software, as well as manage our own data points for costs, machines, and materials. The coding aspect is not too difficult as well, though there are some issues with the availability of some information that could be improved.

Overall, I'm generally satisfied with the response and results that I get from aPriori support. They are intuitive, and try to understand what it is I'm trying to do, or how the problem is affecting me or my organization. They also are very available across the globe, as my team is located in multiple regions.

aPriori team helped a lot for the implementation, as they were on remote and helping for the file run and implementing the same on remote. aPriori team extrended full support for the implementation resolving the bottleneck and bugs if any during installing the software's also, they provide professional services in order to resolve some technical issue

aPriori is covering a lot of different processes instead of being an expert in only one. It can read geometric [information] directly from the CAD file so there's no need for manual entry or the risk of a mistake that comes with that. It provides data in a lot of different regions around the world this keeping the data up to date.

Incentivized

Invited by the Vendor

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

• It improved our business model and strategies.
• Provided get analytics for future view.
• The main negative impact is that it is costly, a waste of time to hold a vast number of candidate sets with many frequent itemsets, low minimum support, or large itemsets.

Incentivized

Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info