The System Level Cost (SLC) feature was first incorporated into SEER-H with release 6.0.  Prior to that release, SEER-H estimated standalone costs of individual work elements plus the costs of creating suitable interfaces between work elements, referring to such mechanical actions as attaching, aligning, calibrating, adjusting, etc., and electronic actions such as energizing, tuning, continuity checking, calibrating, etc.  It did not estimate the sometimes substantial costs of making a group of work elements perform properly as a complete system.  The SLC feature permits more complete estimates to be made ad is a powerful augmentation of SEER-H.   To read this whole paper, click here.

Embedded design integration is the integration of all efforts (hardware, software, etc.) within an system that is typically dedicated to perform one or a few functions. Typically there are real-time computing requirements involved in these types of designs. Integration costs can at times become a considerable portion of the overall cost on a project. The continuous introduction of newer technologies continue to drive up integration efforts.

Use of old factors that estimated Integration Assembly and Test ( IAT) as a function of overall top effort were weak before but now can produce dangerously low estimates. Part of the reason why IAT costs have classically been painful can be attributed to the fact that it is at this stage where the complicate blend of hardware to hardware or hardware to software comes to a point and must all meet specifications at once! What worked in recent simulations or in isolated tests now becomes painfully clear won’t work at a combined level. This invariably leads to the continuous waves of adjustments, partial redesigns, re-architecting, retests in a desperate effort to close in on the minimum requirements.

For estimation purposes, careful consideration has to be given to the work break down structure of the embedded systems being modeled to ensure adequate estimation of integration effort and risk are covered at all levels. For instance, just applying a 10-35% IAT factor at an enclosure level in order to capture integration of all board and SW underneath it might be okay for some simple systems but not newer technologies which bring along extra complexities. An example is where the design involves custom components (ASICs, FPGAs, etc) which in turn have embedded processors within their fabrics. In these System on a Chip (SOC) designs, it make sense to break out the cost of integrating the SW onto the chip itself. At the next level one could add another rollup to cover the integration of SW to the main board running a general purpose processor, etc.

Another example could be designs that involve multiple separate custom chips running on the board since there might be a significant effort to get these chips to work with each other.

In our SEER H hardware model, we break out these sections with a separate roll up to calculate the IAT. At this point, we take care to set the parameters that describe the complexity of the effort and the experience of the folks doing the work. If this type of design is done often, we would create a knowledgebase (default template) to capture and standardize the estimation approach. As can be seen, many designs can involve layers of IAT which in turn can drive up the final overall IAT numbers considerably beyond 35%.

For many years, analysts have been collecting information on the cost of producing hardware.  Over time, it has been shown that unit costs of a product typically decrease as the total production quantity increases.  The study of this effect has led to numerous models and theories to predict the cost of product over time for a production program.  Often these algorithms fall into a general category of Learning Curve analysis.  Once thought to model the increased learning in the hands-on personnel, Learning Curve analysis is now used to envelope cost improvements due to many facets of a continuing production program.   To read the whole paper, click here.