Sunil Kothari

Simulation as a cloud service for short-run high throughput industrial print production using a service broker architecture

Abstract Evaluating end-to-end systems is uniquely challenging in industrial/commercial printing due to a large number of equipment combinations and customization needed for each customer and application. Moreover any mismatch in capacities may render multi-million dollar investments to zero returns on investment. Simulation can help foresee changes on the shop floor when demand changes. Providing a library of components that can be assembled together is the usual approach used by many simulation vendors which still leaves a simulation engineer in the loop to make it usable. We detail our experiences on implementing a prototype (private) cloud service using service broker architecture and a dynamic model generator. The service broker handles the heterogeneity associated with demand and equipment configurations whereas the dynamic model generator customizes a generic model based on inputs from the user. This helps avoiding rewiring of simulation models on each engagement. The schema and the necessary front and back-end codes all reside in the cloud and, therefore, users pay on a per use basis without worrying about the upgrade/update of software at their end. The service supports multi-tenancy which results in low costs per user and provides sharing of resource information yet restricting access to proprietary workflows and policies. A typical run costs a very small amount, which is affordable for even small-sized PSPs. We show the utility of our work in the context of educational book publishing to evaluate equipment changes needed when the current lumpy order demand stream changes to a highly fragmented demand stream. We also discuss how our work can be extended to several other domains such healthcare, transportation, 3D printing.

On the Connections between Types and Service Level Agreements and Implications for Digital Commercial Printing and Other Domains

Digital commercial print providers are increasingly seeing more and more low-value, very short-run orders as a result of personalization and customization of content. In addition, consolidation due to margin pressure and low turnaround times necessitates a quicker reaction to scenarios such as acquiring additional capacity in the peak season or off-loading capacity during the off-peak season. Traditional tools for negotiating and acquiring customer orders are increasingly becoming prohibitive in this environment due to their high costs and lack of ability to make rapid changes. Tools that use machine readable service level agreements (SLAs) promise easier management of customer orders, but are currently not widely used in manufacturing domains such as digital commercial printing. If SLAs are to appear in manufacturing and other related domains, they will need to deal with both SLA Monitoring and SLA Negotiations in the same framework. Programming languages have long used the concept of Types to guarantee behavior of programs. In this paper we show that there is a deep connection between Types and SLAs. The connection stems from the fact that both the Types as well as the SLAs are inherent guarantees about the run-time behavior. The mapping between Types and SLAs is shown by formulating problems in both the domains using notations which have similar semantics. In particular, we show: 1) SLA Monitoring has a parallel in Type Checking, 2) SLA Negotiation has a parallel in Type Inference, and 3) SLA Inhabitation has a parallel in Type Inhabitation. We also briefly mention how the rich meta-theorems about types such as preservation, progress and replacement theorems can be used to reason about SLAs, especially for services which deliver manufactured products.

Assessing color reproduction tolerances in commercial print workflow

Except for linear devices like CRTs, color transformations from colorimetric specifications to device coordinates are mostly obtained by measuring a set of samples, inverting the table, and looking up values in the table (including interpolation), and mapping the gamut from input to output device. The accuracy of a transformation is determined by reproducing a second set of samples and measuring the reproduction errors. Accuracy as the average predicted perceptual error is then used as a metric for quality. Accuracy and precision are important metrics in commercial print because a print service provider can charge a higher price for more accurate color, or can widen his tolerances when customers prefer cheap prints. The disadvantage of determining tolerances through averaging perceptual errors is that the colors in the sample sets are independent and this is not necessarily a good correlate of print quality as determined through psychophysics studies. Indeed, images consist of color palettes and the main quality factor is not color fidelity but color integrity. For example, if the divergence of the field of error vectors is zero, color constancy is likely to take over and humans will perceive the color reproduction as being of good quality, even if the average error is relatively large. However, if the errors are small but in random directions, the perceived image quality is poor because the relation among colors is altered. We propose a standard practice to determine tolerance based on the Farnsworth-Munsell 100-hue test (FM-100) for the second set and to evaluate the color transpositions-a metric for color integrity-instead of the color differences. The quality metric is then the FM-100 score. There are industry standards for the tolerances of color judges, and the same tolerances and classification can be use for print workflows or its components (e.g., presses, proofers, displays). We generalize this practice to arbitrary perceptually uniform scales tailored to specific applications and present an implementation. In essence, we propose to extend the color discrimination test procedures used to evaluate human observers, to mechanical and electronic color reproduction devices.