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Engineering/Business Process Management

A product's cost consists of direct and indirect (aka ‘structural’ or ‘overhead’) costs. In an engineered product company, the latter cost includes sales, customer service, administration, engineering, design & analysis, quality control, testing, rework, warranty etc. For its product, the greater part of the cost comes from its indirect component. For example, in the US manufacturing sector, this cost may be 1 to 5 times its direct counterpart, depending upon the industry.

Over the last couple of decades, the manufacturing field has made significant strides in direct cost reductions due to steady improvements in productivity and quality.  The initiatives such as “Lean”, “Six-Sigma” and the like, have been very successful. In the recent years, such successes have been prompting many US businesses to bring back their production from low-cost overseas countries.  However, the organizations such as the Manufacturing Institute and the like, caution that US structural costs are 20 percent higher than that of its major competitors. Without this cost disadvantage, the United States would be a lower-cost platform for manufacturing than all of its major trading partners except China, Mexico and Taiwan.

Hence, it is quite obvious that a product cost can now be significantly reduced only by controlling its indirect component which is in turn possible mainly thru’ associated process improvements.

Furthermore, a recent 2014 book e.g. Resource Revolution1 by McKinsey researchers says, “Companies that try to stick to the “old” 2% annual improvement solution are going to be obsolete quickly. Businesses that can deliver dramatic resource-productivity improvements at scale (about 10 to 15% annually) will become the great companies of the 21st century”. They talk about the next revolution employing five distinct approaches out of which three are comprehensively addressed by AdvEnSoft. These are Waste Elimination (thru’ greater process efficiency with its EPM solutions), Optimization and Virtualization (thru’ its virtual prototyping environment based on multiphysics, statistical simulations and analytics).

In the above back drop, as touched upon above, AdvEnSoft helps companies achieve substantial indirect product cost reductions thru’ its comprehensive & holistic Engineering Process Management (EPM) approach. EPM is similar to the Business Process Management (BPM) principles but applied to workflows in engineering processes. However, typical engineering tasks are relatively more complex, do require specialized expertise and vary considerably from one company to another even in the similar space. 

Integrated Product Engineering

Simulation Driven Product Development

Engineering Software

 

CADRotor

Developed by AdvEnSoft, in conjunction with Dr. Brian Murphy of Rotating Machinery Analysis, Inc. (www.xlrotor.com), CADRotor permits rapid, error-free capture of solid model geometry for subsequent analysis in XLRotor.

CADRotor is a convenient add-on that interfaces with popular 3D CAD packages such as SolidWorks, Inventor and the like. With minimul user input, the rotating assembly is identified, defined and processed. The corresponding brochure may be downloaded.

Analytics

equations

Business Analytics, as the name implies, is the application of analytics in business. It is utilized in a myriad of business processes like Marketing,Risk Assessment,Fraud Detection,CRM, Customer Loyalty,Operations, HR, etc.

Many industries such as Financial Services (Banks, Credit Cards, Loans, Insurance etc.), Retail, Telecom, Healthcare, Consumer goods, Manufacturing, Sports, Hotels, Airlines and indeed any industry where large amounts of data is generated utilize Business Analytics.

Business Analytics has evolved recently from other, perhaps more familiar terms like Big Data Analytics, Data Mining, Knowledge Discovery, Business Intelligence (BI), Data Warehousing, etc.

At the bottom of all these is the fact that the amount of data being produced in the world is increasing so fast that, according to some, 90% of the data that exists today was created in the last few years. Sophisticated statistical tools are required to deal with such vast quantities of data. The development of most statistical techniques was, until recently, based on elegant theory and analytical methods that worked quite well on the modest amounts of data being analyzed. The increased power and lower cost of computers have allowed the development of new techniques.

The current Business Analytics methodologies can be classified broadly as below:

Descriptive Analytics This traditional business intelligence (BI) analytics methodology reports what has happened or what is happening now! It is reactive in nature.
Predictive Analytics This analytics methodology utilizes a variety of techniques from statistics, modeling, machine learning and data mining to analyze current and historical facts to make predictions about future events. It assumes that the best predictor of future performance is past performance. The accuracy and usability of results depends greatly on level of data analysis and quality of assumptions. It is proactive in nature.
Prescriptive Analytics This analytics methodology uses array of optimization, simulation and project scheduling techniques to identify actions that will produce best results while operating within resource limitations and tight restriction, generate real prescriptive direction from static and streaming data(including big data), and suggest decision options to take advantage of predictions by anticipating what will happen, when it will happen and why it will happen. It is proactive in nature.
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