Gary J. Dispoto

On-Demand Digital Print Services: A New Commercial Print Paradigm as an IT Service Vertical

On-demand digital print service is a form of personalized manufacturing service. Key to commercial print value-creation chain is the service engagement and fulfillment between the content suppliers and the print service providers (PSP). Content suppliers are the service clients, they request print services and supply content for print. PSPs provide the fulfillment services to the content suppliers in exchange for payment: converting the supplied content into printed products and shipping them to the end-customer. In this paper, we will describe on-demand digital print service, and the application of service-oriented architecture (SOA) as platform to integrate information and communication technologies (ICT) into the end-to-end print service fulfillment process to enable digital print automation, and the SOA implementation assisted by process modeling. We also extend the SOA framework to include the order negotiation process and envisage a coupled demand-fulfillment paradigm. SOA-based digital print automation leverages from the integration of ICT into the print manufacturing operations management, it is an IT service vertical.

Real-Time Production Scheduler for Digital-Print-Service Providers Based on a Dynamic Incremental Evolutionary Algorithm

We present a high-performance and real-time production scheduling algorithm for digital print production based on a dynamic incremental evolutionary algorithm. The optimization objective is to prioritize the dispatching sequence of orders and balance resource utilization. The scheduler is scalable for realistic problem instances and it provides solutions quickly for diverse print products that require complex fulfillment procedures. Furthermore, it dynamically ingests the transient state of the factory, such as process information and resource failure probability in print production; therefore, it minimizes the management-production mismatch. Discrete-event simulation results show that the production scheduler leads to a higher and more stable order on-time delivery ratio compared to a rule-based heuristic. Its beneficial attributes collectively contribute to the reduction or elimination of the shortcomings that are inherent in todays digital printing environment and help to enhance a print factorys productivity and profitability.

Digital print workflow optimization under due-dates, opportunity cost and resource constraints

On-demand digital printing is an example of emerging personalized manufacturing services. It provides unique opportunities to automate the printing process, enhance productivity, and better utilize resources such as equipment, servers and IT infrastructure. In this work, we present a unified solution approach to solve an important optimization problem in digital printing, viz., simultaneous mapping of component tasks of a print job to time steps (scheduling), selection of resources for these tasks, and mapping of tasks to resources (binding). We model print jobs, the relationships between them, and dependencies between tasks within a job, in terms of sequencing graphs. This formal representation is then used for scheduling and resource binding. The optimization objective is to enable justin-time manufacturing, that is, to minimize both the slack time (the duration between the delivery deadline and the completion time of the order) and the opportunity cost for job orders. The proposed approach uses genetic algorithms (GA) to systematically search the space of feasible solutions. The fitness function of the GA is carefully crafted to match the optimization objective. An integer linear programming (ILP) model is described to evaluate the GA heuristic by deriving optimal solutions for small problem instances. The optimization technique is further evaluated using print orders from a commercial print service provider and compared to baseline methods commonly implemented in the industrial settings.

Building a fine-art reproduction system from standard hardware

Most fine art reproduction workflows to date have been based on hyperspectral devices. These devices capture, process and print more than three channels of spectral data to produce spectrally accurate reproductions. While these workflows have unique advantages over standard three-channel workflows, such as the ability to produce reproductions that are colorimetrically accurate across many illuminants, they usually require custom hardware. Such hardware can be expensive, time-consuming to setup, and may require a full-time trained operator. We describe the challenges and issues in constructing a colorimetrically accurate fine art reproduction work- flow based on standard three-channel hardware. The workflow was designed to be as automated as possible, simple to use, and device-independent. The heart of the workflow is a software application that takes as input camera characterization data, reflectance statistics of the artwork, an image of the artwork, and an image of a reference card, and it outputs a properly exposed, uniformly illuminated and colorimetrically accurate reproduction. We describe the methods used to compute the exposure level, to compensate for illumination non-uniformities, and to generate a per-image color correction matrix. Finally, we present reproduction results and error statistics obtained using a workflow comprising a 4x5” Sinar camera, a Betterlight digital back, and an HP DesignJet 5500 printer.

The role of EDA in digital print automation and infrastructure optimization

The use of digital print provides unique opportunities to automate the printing process, revamp production steps, better utilize resources, and enhance productivity. This paper highlights the key role that electronic design automation (EDA) can play in the maturation of the digital print automation field. It first describes basic concepts in digital printing and digital commercial print services. Next it describes the application of discrete-event simulation to policy management and performance evaluation, and dynamic resource management using EDA flows based on scheduling and resource binding.

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.

Accurate Analysis and Prediction of Enterprise Service-Level Performance

An enterprise service-level performance time series is a sequence of data points that quantify demand, throughput, average order-delivery time, quality of service, or end-to-end cost. Analytical and predictive models of such time series can be embedded into an enterprise information system (EIS) in order to provide meaningful insights into potential business problems and generate guidance for appropriate solutions. Time-series analysis includes periodicity detection, decomposition, and correlation analysis. Time-series prediction can be modeled as a regression problem to forecast a sequence of future time-series datapoints based on the given time series. The state-of-the-art (baseline) methods employed in time-series prediction generally apply advanced machine-learning algorithms. In this article, we propose a new univariate method for dealing with midterm time-series prediction. The proposed method first analyzes the hierarchical periodic structure in one time series and decomposes it into trend, season, and noise components. By discarding the noise component, the proposed method only focuses on predicting repetitive season and smoothed trend components. As a result, this method significantly improves upon the performance of baseline methods in midterm time-series prediction. Moreover, we propose a new multivariate method for dealing with short-term time-series prediction. The proposed method utilizes cross-correlation information derived from multiple time series. The amount of data taken from each time series for training the regression model is determined by results from hierarchical cross-correlation analysis. Such a data-filtering strategy leads to improved algorithm efficiency and prediction accuracy. By combining statistical methods with advanced machine-learning algorithms, we have achieved a significantly superior performance in both short-term and midterm time-series predictions compared to state-of-the-art (baseline) methods.

ICC profiles: are we better off without them?

Before ICC profiles, a device-independent document would encode all color in a device independent CIE space like CIELAB. When the document was to be printed, the press person would measure a target and create a color transformation from the CIE coordinates to device coordinates. For office and consumer color printers, the color transformation for a standard paper would be hardwired in the printer driver or the printer firmware. This procedure had two disadvantages: the color transformations required deep expertise to produce and were hard to manage (the latter making them hard to share), and the image data was transformed twice (from input device to colorimetric and then to output device coordinates) introducing discretization errors twice. The first problem was solved with the ICC profile standard, and the last problem was solved by storing the original device dependent coordinates in the document- together with an input ICC profile-so the color management system could first collapse the two profiles and then perform a single color transformation. Unfortunately, there is a wide variety in the quality of ICC profiles. Even worse, the real nightmare is that quite frequently the incorrect ICC profiles are embedded in documents or the color management systems apply the wrong profiles. For consumer and office printers, the solution is to forgo ICC profiles and reduce everything to the single sRGB color space, so only the printer profile is required. However, the sRGB quality is insufficient for print solution providers. How can a modern print workflow solve the ICC profile nightmare?

Accurate Predictions of Process-Execution Time and Process Status Based on Support-Vector Regression for Enterprise Information Systems

Accurate predictions of both process-execution time and process status are crucial for the development of an intelligent enterprise information system (EIS). We have developed new automated learning-based process-execution time-prediction and process status-prediction methods that can be embedded into an EIS. Process-execution time prediction is a regression problem and state-of-the-art (baseline) time-prediction methods use a machine-learning regression model. Process status prediction is a binary classification problem in which a class labeled “completed” or “in-progress” is assigned to a process with respect to an arbitrary predictive horizon (i.e., the future time given by the method user). The methods proposed in this paper integrate statistical methods with support-vector regression. Comparison results obtained from the real data of a digital-print enterprise show that the proposed time-prediction method reduces both the relative mean error and the root-mean-squared error of the regression model. Furthermore, the proposed status-prediction method not only achieves higher classification accuracy than state-of-the-art methods, it also estimates the probability of the predicted status. In addition, algorithm development and training phases of the proposed methods do not rely on any arbitrary predictive horizon. Therefore, a single time-prediction model as proposed is sufficient for status prediction as opposed to a baseline status-prediction method that requires classification models for all potential predictive horizons.

Data-Driven Optimization of Order Admission Policies in a Digital Print Factory

On-demand digital print service is an example of a real-time embedded enterprise system. It offers mass customization and exemplifies personalized manufacturing services. Once a print order is submitted to the print factory by a client, the print service provider (PSP) needs to make a real-time decision on whether to accept or refuse this order. Based on the print factorys current capacity and the orders properties and requirements, an order is refused if its acceptance is not profitable for the PSP. The order is accepted with the most appropriate due date in order to maximize the profit that can result from this order. We have developed an automated learning-based order admission framework that can be embedded into an enterprise environment to provide real-time admission decisions for new orders. The framework consists of three classifiers: Support Vector Machine (SVM), Decision Tree (DT), and Bayesian Probabilistic Model (BPM). The classifiers are trained by history orders and used to predict completion status for new orders. A decision integration technique is implemented to combine the results of the classifiers and predict due dates. Experimental results derived using real factory data from a leading print service provider and Weka open-source software show that the order completion status prediction accuracy is significantly improved by the decision integration strategy. The proposed multiclassifier model also outperforms a standalone regression model.