Anylogic 2021 Conference Presentation: Automatic generation of simulation mod...Sudhendu Rai
The full talk is available at:
https://youtu.be/xz295p_wS1k
Abstract:
Discrete-event simulation modeling is a powerful technology to analyze and improve complex business processes. However, the development of such models requires significant expertise and skills. In any business, there exist general classes of processes with specific instantiations within each class. In this talk, we propose the use of data templates for capturing process data and automated generation of simulation models using those templates. These auto-generated simulation models can be further enhanced using the Anylogic visual programming interface. Other analytical features of Anylogic such as optimization, visualization, automated output of simulation results can be utilized to analyze and improve the processes. By developing a library of templates, organizational knowledge associated with the processes can be captured and stored. These models will capture process dynamics and interactions and be far richer than what is captured through the use of process maps only. This approach will enable a broader use of simulation technology to develop business processes and make improved organizational decisions through de-skilling. It will also enable the integration of simulation software into a broader process improvement technology ecosystem.
#datascience #analytics #simulationmodeling
#processexcellence #processimprovement #processoptimization #processautomation #processmining #processintelligence #processtransformation
Winter Simulation Conference 2021 - Process Wind Tunnel TalkSudhendu Rai
The talk associated with this presentation can be accessed at:
https://youtu.be/VXEVuXW9knU
Abstract
In this talk, we will introduce a simulation-based process improvement framework and methodology called the Process Wind Tunnel. We will describe this framework and introduce the underlying technologies namely process mapping and data collection, data wrangling, exploratory data analysis and visualization, process mining, discrete-event simulation optimization and solution implementation. We will discuss how Process Wind Tunnel framework was utilized to improve a critical business process namely, the post-execution trade settlement process. The work builds upon and generalizes the Lean Document Production solution (2008 Edelman finalist) for optimizing printshops to more general and complex business processes found within the insurance and financial services industry.
Capability-as-a-Service: Investigating the Innovation Potential from a Busine...CaaS EU FP7 Project
Capability-as-a-Service: Investigating the Innovation Potential from a Business Model Perspective.
By Kurt Sandkuhl (Rostock University), Janis Stirna (Stockholm University)
DIFENSE workshop @ CAISE'2015 in Stockholm
The talk describes how service operations in the insurance industry can be optimized using Process Wind Tunnel. Process Wind Tunnel utilizes novel process analytics, process mining, simulation and targeted automation to improve business processes. It also describes a real-world application of process mining to diagnose complex business process and gain insights. This talk was delivered at the Process Mining Camp 2020 and is available on Youtube (https://www.youtube.com/watch?v=ujEoPiuo9As)
Graphically viewing the flow of value as its fit, form and/or function is improved from suppliers to customers. It shows value-added and non-value-added activities.
Edelman competition presentation slidesSudhendu Rai
Xerox has invented, tested, and implemented a novel class of operations research-based productivity-improvement offerings, trademarked LDP Lean Document Production® solutions, for the $100 billion printing industry in the United States. These solutions, which Xerox has implemented in approximately 100 sites to date, have provided dramatic productivity and cost improvements for both print shops and document-manufacturing facilities, as measured by revenue-per-unit labor-cost reductions of 20-40 percent. They have generated approximately $200M of incremental profit across the Xerox customer value chain since their initial introduction in 2000. The offerings have extended the use of operations research to small- and medium-sized print shops in the printing industry while increasing the scope of its application to large document-production facilities.
Anylogic 2021 Conference Presentation: Automatic generation of simulation mod...Sudhendu Rai
The full talk is available at:
https://youtu.be/xz295p_wS1k
Abstract:
Discrete-event simulation modeling is a powerful technology to analyze and improve complex business processes. However, the development of such models requires significant expertise and skills. In any business, there exist general classes of processes with specific instantiations within each class. In this talk, we propose the use of data templates for capturing process data and automated generation of simulation models using those templates. These auto-generated simulation models can be further enhanced using the Anylogic visual programming interface. Other analytical features of Anylogic such as optimization, visualization, automated output of simulation results can be utilized to analyze and improve the processes. By developing a library of templates, organizational knowledge associated with the processes can be captured and stored. These models will capture process dynamics and interactions and be far richer than what is captured through the use of process maps only. This approach will enable a broader use of simulation technology to develop business processes and make improved organizational decisions through de-skilling. It will also enable the integration of simulation software into a broader process improvement technology ecosystem.
#datascience #analytics #simulationmodeling
#processexcellence #processimprovement #processoptimization #processautomation #processmining #processintelligence #processtransformation
Winter Simulation Conference 2021 - Process Wind Tunnel TalkSudhendu Rai
The talk associated with this presentation can be accessed at:
https://youtu.be/VXEVuXW9knU
Abstract
In this talk, we will introduce a simulation-based process improvement framework and methodology called the Process Wind Tunnel. We will describe this framework and introduce the underlying technologies namely process mapping and data collection, data wrangling, exploratory data analysis and visualization, process mining, discrete-event simulation optimization and solution implementation. We will discuss how Process Wind Tunnel framework was utilized to improve a critical business process namely, the post-execution trade settlement process. The work builds upon and generalizes the Lean Document Production solution (2008 Edelman finalist) for optimizing printshops to more general and complex business processes found within the insurance and financial services industry.
Capability-as-a-Service: Investigating the Innovation Potential from a Busine...CaaS EU FP7 Project
Capability-as-a-Service: Investigating the Innovation Potential from a Business Model Perspective.
By Kurt Sandkuhl (Rostock University), Janis Stirna (Stockholm University)
DIFENSE workshop @ CAISE'2015 in Stockholm
The talk describes how service operations in the insurance industry can be optimized using Process Wind Tunnel. Process Wind Tunnel utilizes novel process analytics, process mining, simulation and targeted automation to improve business processes. It also describes a real-world application of process mining to diagnose complex business process and gain insights. This talk was delivered at the Process Mining Camp 2020 and is available on Youtube (https://www.youtube.com/watch?v=ujEoPiuo9As)
Graphically viewing the flow of value as its fit, form and/or function is improved from suppliers to customers. It shows value-added and non-value-added activities.
Edelman competition presentation slidesSudhendu Rai
Xerox has invented, tested, and implemented a novel class of operations research-based productivity-improvement offerings, trademarked LDP Lean Document Production® solutions, for the $100 billion printing industry in the United States. These solutions, which Xerox has implemented in approximately 100 sites to date, have provided dramatic productivity and cost improvements for both print shops and document-manufacturing facilities, as measured by revenue-per-unit labor-cost reductions of 20-40 percent. They have generated approximately $200M of incremental profit across the Xerox customer value chain since their initial introduction in 2000. The offerings have extended the use of operations research to small- and medium-sized print shops in the printing industry while increasing the scope of its application to large document-production facilities.
Capacity Planning is a process whereby an organization comes together to agree on what they are going to focus on in the medium term. The content is based on the work of Chris Matts.
Measurement and Comparison of Productivity Performance Under Fuzzy Imprecise ...Waqas Tariq
The creation of goods and services requires changing the expended resources into the output goods and services. How efficiently we transform these input resources into goods and services depends on the productivity of the transformation process. However, it has been observed there is always a vagueness or imprecision associated with the values of inputs and outputs. Therefore, it becomes hard for a productivity measurement expert to specify the amount of resources and the outputs as exact scalar numbers. The present paper, applies fuzzy set theory to measure and compare productivity performance of transformation processes when numerical data cannot be specified in exact terms. The approach makes it possible to measure and compare productivity of organizational units (including non-government and non-profit entities) when the expert inputs can not be specified as exact scalar quantities. The model has been applied to compare productivity of different branches of a company.
The Fine Art of Combining Capacity Management with Machine LearningPrecisely
Today, capacity management within the enterprise continues to evolve. In the past, we were focused on the hardware – but now we are focused on the services. With that in mind, the amount of data available has increased significantly and has become difficult for individuals to sort through.
It is apparent that to be successful in this discipline, we need the machines to do more of the heavy lifting. This includes automatically creating reports, calling out anomalies and producing forecasts. The intuition of the human computer is imperative to the success.
View this webinar on-demand where we discuss:
• The strengths and weaknesses of capacity management with and without machine learning
• What machine learning can provide throughout the process
• The benefits of using capacity management and machine learning within your organization
Facility PlanningFacility Planning and DesignUsed .docxssuser454af01
Facility Planning
Facility Planning and Design
Used with permission: Dr. David Porter
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
2
Presentation Outline
—Introduction to
§ Facilities Planning
§ Facilities Layout
—Generating layout alternatives with
§ Systematic Layout Planning (SLP)
§ Computerized Relative Allocation of Facilities Technique
(CRAFT)
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
3
Facilities Planning
— Facilities planning determines how an activity’s tangible fixed assets
best support achieving the activity's objectives
— Facilities Planning Viewpoints
§ Civil Engineering
§ Electrical/Mechanical Engineering
§ Architectural
§ Construction Management/Contractor
§ Real Estate
§ Urban Planning
§ Industrial Engineering (IE)
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
4
IE Viewpoint of Facilities Planning
— Industrial Engineers focus on
§ Requirements
§ Resource allocation, and
§ Efficient use of resources
— Facilities are the integration of many lower level systems
§ Space requirements with respect to flow and operations control
§ Personnel & Equipment Requirements
§ System design/layout with respect to flow and operations control
§ The use of information systems and technology to increase
effectiveness
§ Movement within a facility and between facilities (i.e., location)
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
5
Example of a Manufacturing Facility
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
6
From an IE Viewpoint
— Why is the equipment in this facility located as shown?
— Why are they arranged as shown?
— Why are there so many duplicated items?
— Why is the facility so large or small?
— How many people will be working in the facility?
— Does this design meet requirements?
— etc.
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
7
IE Approaches
— Industrial Engineers develop models to understand, design and
validate systems
§ Procedures
• e.g., Systematic Layout Planning (SLP)
§ Analytical models
• e.g., machine fraction equations, queuing models
§ Analytical layout models/software
§ Computer simulations
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
8
Elements of Facilities Planning
Facilities
Planning
Facilities
Location
Facilities
Design
Facilities
Systems
Production
System
Design
Layout
Design
Handling/Storage
Systems
Design
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
9
Facilities Layout
— Facilities layout is a design activity and as such there is often a lot of
art (i.e., experience) and application-specific knowledge that must be
utilized when developing a layout
§ Grocery store layout vs. department store lay ...
ADV Slides: What the Aspiring or New Data Scientist Needs to Know About the E...DATAVERSITY
Many data scientists are well grounded in creating accomplishment in the enterprise, but many come from outside – from academia, from PhD programs and research. They have the necessary technical skills, but it doesn’t count until their product gets to production and in use. The speaker recently helped a struggling data scientist understand his organization and how to create success in it. That turned into this presentation, because many new data scientists struggle with the complexities of an enterprise.
Capacity Planning is a process whereby an organization comes together to agree on what they are going to focus on in the medium term. The content is based on the work of Chris Matts.
Measurement and Comparison of Productivity Performance Under Fuzzy Imprecise ...Waqas Tariq
The creation of goods and services requires changing the expended resources into the output goods and services. How efficiently we transform these input resources into goods and services depends on the productivity of the transformation process. However, it has been observed there is always a vagueness or imprecision associated with the values of inputs and outputs. Therefore, it becomes hard for a productivity measurement expert to specify the amount of resources and the outputs as exact scalar numbers. The present paper, applies fuzzy set theory to measure and compare productivity performance of transformation processes when numerical data cannot be specified in exact terms. The approach makes it possible to measure and compare productivity of organizational units (including non-government and non-profit entities) when the expert inputs can not be specified as exact scalar quantities. The model has been applied to compare productivity of different branches of a company.
The Fine Art of Combining Capacity Management with Machine LearningPrecisely
Today, capacity management within the enterprise continues to evolve. In the past, we were focused on the hardware – but now we are focused on the services. With that in mind, the amount of data available has increased significantly and has become difficult for individuals to sort through.
It is apparent that to be successful in this discipline, we need the machines to do more of the heavy lifting. This includes automatically creating reports, calling out anomalies and producing forecasts. The intuition of the human computer is imperative to the success.
View this webinar on-demand where we discuss:
• The strengths and weaknesses of capacity management with and without machine learning
• What machine learning can provide throughout the process
• The benefits of using capacity management and machine learning within your organization
Facility PlanningFacility Planning and DesignUsed .docxssuser454af01
Facility Planning
Facility Planning and Design
Used with permission: Dr. David Porter
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
2
Presentation Outline
—Introduction to
§ Facilities Planning
§ Facilities Layout
—Generating layout alternatives with
§ Systematic Layout Planning (SLP)
§ Computerized Relative Allocation of Facilities Technique
(CRAFT)
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
3
Facilities Planning
— Facilities planning determines how an activity’s tangible fixed assets
best support achieving the activity's objectives
— Facilities Planning Viewpoints
§ Civil Engineering
§ Electrical/Mechanical Engineering
§ Architectural
§ Construction Management/Contractor
§ Real Estate
§ Urban Planning
§ Industrial Engineering (IE)
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
4
IE Viewpoint of Facilities Planning
— Industrial Engineers focus on
§ Requirements
§ Resource allocation, and
§ Efficient use of resources
— Facilities are the integration of many lower level systems
§ Space requirements with respect to flow and operations control
§ Personnel & Equipment Requirements
§ System design/layout with respect to flow and operations control
§ The use of information systems and technology to increase
effectiveness
§ Movement within a facility and between facilities (i.e., location)
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
5
Example of a Manufacturing Facility
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
6
From an IE Viewpoint
— Why is the equipment in this facility located as shown?
— Why are they arranged as shown?
— Why are there so many duplicated items?
— Why is the facility so large or small?
— How many people will be working in the facility?
— Does this design meet requirements?
— etc.
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
7
IE Approaches
— Industrial Engineers develop models to understand, design and
validate systems
§ Procedures
• e.g., Systematic Layout Planning (SLP)
§ Analytical models
• e.g., machine fraction equations, queuing models
§ Analytical layout models/software
§ Computer simulations
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
8
Elements of Facilities Planning
Facilities
Planning
Facilities
Location
Facilities
Design
Facilities
Systems
Production
System
Design
Layout
Design
Handling/Storage
Systems
Design
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
9
Facilities Layout
— Facilities layout is a design activity and as such there is often a lot of
art (i.e., experience) and application-specific knowledge that must be
utilized when developing a layout
§ Grocery store layout vs. department store lay ...
ADV Slides: What the Aspiring or New Data Scientist Needs to Know About the E...DATAVERSITY
Many data scientists are well grounded in creating accomplishment in the enterprise, but many come from outside – from academia, from PhD programs and research. They have the necessary technical skills, but it doesn’t count until their product gets to production and in use. The speaker recently helped a struggling data scientist understand his organization and how to create success in it. That turned into this presentation, because many new data scientists struggle with the complexities of an enterprise.
Exploring Neo4j Graph Database as a Fast Data Access LayerSambit Banerjee
This article describes the findings of an extensive investigative work conducted to explore the feasibility of using a Neo4j Graph Database to build a Fast Data Access Layer with near-real time data ingestion from the underlying source systems.
DutchMLSchool 2022 - Process Optimization in Manufacturing PlantsBigML, Inc
Process Optimization in Manufacturing Plants, by Keyanoush Razavidinani, Digital Business Consultant at A1 Digital.
*Machine Learning School in The Netherlands 2022.
Sudhendu rai publications list (including issued patents)Sudhendu Rai
This document contains a list of my papers (56) and patents (79). It has been organized by year of publication. Some preliminary categorization has also been done.
Sudhendu Rai Patent abstracts (Word document with embedded patents)Sudhendu Rai
A compilation of titles and abstracts of 79 issued patents generated over the course of 15 years. They span areas of optimization, controls, machine learning, production design, lean, event-log analysis, sustainability, cellular production, inventory optimization, enterprise design and production control.
Sudhendu Rai - Patent title and abstractsSudhendu Rai
A compilation of titles and abstracts of 79 issued patents generated over the course of 15 years. They span areas of optimization, controls, machine learning, production design, lean, event-log analysis, sustainability, cellular production, inventory optimization, enterprise design and production control.
Process wind tunnel - A novel capability for data-driven business process imp...Sudhendu Rai
A talk I gave recently on data-driven process improvement methodology and techniques with applications and results from insurance and finance processes
Methods and applications for demand (time-series) forecastingSudhendu Rai
The talk discusses Historical (time series modeling),Judgmental, analyst driven, Hybrid historical and judgmental techniques for forecasting demand in large operations..
Presented at the Frontiers in Service Conference
MULTIOBJECTIVE OPTIMIZATION AND QUANTITATIVE TRADE-OFF ANALYSIS IN XEROGRAPHI...Sudhendu Rai
A complex engineering system such as a xerographic marking engine is an aggregate of interacting subsystems that are coupled through a large number of constraints and design variables. The traditional way of designing these systems is to decouple the overall design into smaller subsystems and assign teams to work on these subsystems. This approach is critical to making the project manageable and enabling concurrent development. However, if the goal is to design systems that can deliver best possible performance, i.e. if the performance limits are being pushed to the extreme, characterizing the interactions becomes critical.
Multiobjective optimization is a design methodology that addresses the issue of designing large systems where the goal is to simultaneously optimize a finite number of performance criteria that come from one or more disciplines and are coupled through a set of design variables and constraints. This approach to design makes explicit and quantitative the inherent trade-offs that need to be made in doing coupled system design. It also enables the determination of the attainable limits of performance from a given system.
This paper will discuss the multiobjective optimization methodology and optimal methods of performing quantitative trade-off analysis. These design methods will be applied to problems from the xerographic design domain and results will be presented.
Wsc 2015 modeling customer demand in print service environments usingSudhendu Rai
For simulation modeling, what-if analysis and optimization studies of many service and production operations, demand models that are reliable statistical representations of current and future operating conditions are required. Current simulation tools allow demand modeling using known closed-form statistical distributions or raw demand data collected from operations. In many instances, demand data cannot be described by known closed-form statistical distributions and the raw data collected from operations is not representative of future demand. This paper describes an approach to demand modeling where historical demand data collected over a finite time period is combined with user-input using two-tier bootstrapping to produce synthetic demand data that preserves the statistical distribution of the original data but has overall metrics such as volume, workflow mix and individual task and job sizes that represent projected future state scenarios. When the customer demand data follows highly non-normal distributions, a modified procedure is presented.
AN EVENT-LOG ANALYSIS AND SIMULATION-BASED APPROACH FOR QUANTIFYING SUSTAINAB...Sudhendu Rai
This paper describes a discrete-event simulation and event-log analysis based approach for computing sustainability metrics in production environments to perform various types of comparative analysis and assessments. Event logs collected from the production environment are analyzed to compute current state sustainability metrics such as energy usage, carbon footprint and heating/cooling requirements. Bootstrapping based forecasting leveraging expert input is utilized to estimate future demand. The forecasted demand is then simulated to predict sustainability metrics. The discrete-event simulation results from the forecasted data and computation of heat produced is combined with thermodynamic
models of heat transfer through the thermal envelope of the facility to provide more accurate estimates of true carbon footprint associated with the production operations while also enabling cross-comparative studies of setting operations in different geographical locations. The framework and software tool enables the integration of productivity metrics and sustainability metrics in decision-making process for designing
and operating production environments.
SIMULATION-BASED OPTIMIZATION USING SIMULATED ANNEALING FOR OPTIMAL EQUIPMENT...Sudhendu Rai
The paper describes a software toolkit that enables the data-driven simulation-based optimization of print shops It enables quick modeling of complex print production environments under the cellular production framework. The software toolkit automates several steps of the modeling process by taking declarative inputs from the end-user and then automatically generating complex simulation models that are used to determine improved design and operating points. This paper describes the addition of another layer of automation consisting of simulation-based optimization using simulated-annealing that enables automated search of a large number of design alternatives in the presence of operational constraints to determine a cost-optimal solution. The results of the application of this approach to a real-world problem are also described.
Data-Driven Simulation-Enhanced optimization of Service operationsSudhendu Rai
This paper describes a systematic six-step data-driven simulation-based methodology for optimizing people-based service systems on a large distributed scale that exhibit high variety and variability. The methodology is exemplified
through its application within the printing services industry where it has been successfully deployed by Xerox Corporation across small, mid-sized and large print shops generating over $250 million in profits across the customer value chain. Each
step of the methodology consisting of innovative concepts co-development and testing in partnership with customers, development of software and hardware tools to implement the innovative concepts, establishment of work-process and practices for customer-engagement and service implementation, creation of training and infrastructure for large scale deployment, integration of the innovative offering within the framework of existing corporate offerings and lastly the monitoring and deployment of the financial and operational metrics for estimating the return-on investment and the continual renewal of the offering are described in detail.
Fat-tail inputs in manufacturing systems (Industrial Engineering Research Con...Sudhendu Rai
The impact of extremely high levels of input variability on design and scheduling of processes is discussed. An interesting application of fat-tail modeling and analysis to operational processes. Process design based on insights from this analysis resulted in significant positive impact on end-to-end productivity of real-world processes.
Maximizing operations productivity with lean document productionSudhendu Rai
This presentation provides a link to a webinar describing a case study involving a large global bank. Discussion is focused around the use of process analytics, cellular workflows and simulation optimization to improve the productivity of a large transaction operations resulting in realization of 32-46% savings in floor space, labor and equipment related metrics post-implementation of changes.
A software toolkit and data-driven process improvement solution leveraging ev...Sudhendu Rai
One of the earliest applications of process mining, simulation, process analytics, tail scheduling and a comprehensive software toolkit that was used to train consultants and deploy the solution across 100+ operations globally within Xerox. Incremental profit impact of ~$200M was realized.
Process wind tunnel for improving insurance business processesSudhendu Rai
Process Wind Tunnel is data-driven process improvement methodology using process analytics, process mining, discrete-event simulation, optimization, scheduling and targeted automation. This presentation describes the approach and a case study from commercial insurance underwriting.
The full talk is available at:https://www.youtube.com/watch?v=b7MtF0PlDxo
An innovative software framework and toolkit for process optimization deploye...Sudhendu Rai
Many business enterprises outsource the management of processes and technology that they consider non-core to third-party service providers. The challenge for the service providers is to leverage their expertise to deliver managed services that more efficient, productive and profitable. Examples include IT infrastructure management (IBM, HP), print services management (Xerox, Ricoh, Pitney Bowes), food services (Aramark, Compass Group, Sodexo) and others. Many traditional product companies have increasingly diversified as service providers of this type and rely on a combination of people and technology for services delivery.
The capability and expertise to deliver high quality optimized processes on a large scale without incurring high costs is a key imperative for these companies. This is traditionally achieved through standardization of processes, capture and dissemination of best practices and domain knowledge and structured training programs. Process optimization technologies such as discrete-event simulation, stochastic process modeling and advanced analytics have traditionally been the forte of expert (and often high-paid) consultants which has limited their broad use in these services industries primarily due to cost constraints.
This talk will describe how process optimization solutions were developed and delivered on a large scale to the Xerox document production outsourcing-services business. Early on, extensive consulting engagements with end-customers were used to abstract and generalize the process optimization problem. Then the various steps of the process optimization problem such as data collection, statistical analysis, simulation and modeling, optimization, scheduling and monitoring were abstracted and modeled. Technology and algorithms for each step were developed, refined, automated, integrated and then encapsulated in an easy-to-use software toolkit that supported a structured customer engagement process by less-skilled delivery personnel. The delivery of process optimization services using these automated tools that encapsulate advanced analytics, process modeling, optimization and scheduling techniques has enabled significant savings and improvement in customer satisfaction for the document production outsourcing-services business. The talk will conclude with a discussion of lessons learnt and next steps to further automate and simplify the services delivery process.
Productivity improvement solutions for manufacturing systems with highly vari...Sudhendu Rai
Application of data-driven process optimization to improve a large credit card production service bureau that exhibited extremely high job size variability.
Of particular interest is the fat-failed (very high variability) job size distribution and how factory processes were partitioned based on job sizes and setup requirements to significantly improve throughput, cycle time and labor utilization.
Data-driven model-based restructuring of enterprise transaction operationsSudhendu Rai
We present a case study in the use of process data analytics and discrete-event simulation to improve productivity in a large complex transaction print production environment.
Implementation of lean document production in the printing industrSudhendu Rai
Abstract: A previous Interfaces (Edelman Finalist) paper [12] described the LDP Lean Document Production toolkit and work process based on operations research techniques that demonstrated significant improvements in the productivity of small to mid-sized print shops. In this paper, the extensions of the methodology to improve productivity of very large print shops are described. The printing industry is segmented into four quadrants based on resource utilization and job variability. Job variability in one of the industry segments is extremely high and is characterized by fat-tail distributions. This paper describes the extensions required to handle the applications of LDP Lean Document Production to this market segment.
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
1. Smart Business Process - Driving breakthrough
productivity improvements in service operations
Sudhendu Rai
2. • Introduction
– Xerox Services Capabilities Overview
– Introducing the notion of Smart Business Process
– The need for Smart Business Process
• Vision
– Strategic vision
– Examples of science of process optimization
• LDP Lean Document Production- An instantiation of SBP within the print
production services domain
– Overview of print production process
– LDP Solution
– LDP Case Studies & Business Impact
• Current focus – Expanding SBP to transaction processing services
• Concluding Remarks
PARC | 2
Agenda
4. PARC | 4
What is Smart Business Process?
A business process that is continually optimized and operated using data-driven simulation
optimization and real-time productivity analytics encapsulated in standalone and web-
based software solutions
Data collection
using IT systems
& customized
technologies
(RFID, handheld
devices)
Data analysis
& simulation
optimization
framework
Workflow
models
using
simulations
Real-time
operational
techniques)],([)(
),(min
xYExgwhere
xxg
=
Smart
Business
Process
5. PARC | 5
The need for Smart Business Process
Service operations are
constantly challenged with
designing efficient
processes and to operate
them profitably
Transaction print
and mail
operations was
paying $3M in
financial
penalties due to
changes in
regulatory
compliance laws
Margin
improvement
Margin and client
sat improvement
TP capability
standardization,
margin
improvement,
service
integration
Service processes are
getting data-rich through
evolution of underlying
process management
platforms
IBM Filenet,
SPC controller
Regulatory
compliance
requires fine-
grained data
collection
OOM
Prisma
MCP2
MCP3
Cheap data collection
sensing technology offers
opportunity for process
data collection on multiple
dimensions
Wireless scanner
for manual
process event
data logging
RFID technology
Video cameras
Mobile phone
apps
Process modeling &
optimization can enable
breakthrough process
productivity gains
Theoretical
models
using simplified
abstractions
useful to gain
insights but do
not scale to real
world problems
Data driven
simulation
modeling and
optimization of
processes can be
the answer.
Additional innovation needed
• Process characterization and
abstraction
• Data collection, cleansing and
fusing
• Service structure optimization
• Real-time management techniques
Global
Bank
Global
Bank
Service
Bureau
Service
Bureau
6. Advance the science of data-driven domain specific service process
simulation optimization, instantiate the solutions through software tools
and platforms and demonstrate significant business results through
their deployment
PARC | 6
Strategic Vision
Handheld(s)
DCClient
Scan
Barcodes
AccessPoint
Data
Collection PC
DCServer
Job
Tracking
DB
Customer
Data
Job
Ticket
Operator
Ticket
Machine
Ticket Task
ID
Job
TicketJob
Ticket
Shop
Definition
Job Ticket
Printing
3'-0"
6'-0"
Desk
Customer Counter
DocuTech
6135
Specialty-paper
Shelving
F
r
o
n
t
StateofUtahCopyCenter1foot=
10feet=
F
r
o
n
t
Closet
Filing
Cabinet
Job File
Trays
Supplies
Booklet
Maker
F
r
o
n
t
F
r
o
n
t
Boxing & Finishing Table
DocuColor 6060
DocuColor 6060
F
r
o
n
t
Desk
Fridge &
Microwave
Desk
DC470
Table Table
Supplies Supplies Supplies
Filing
Cabinet
Work
Station
Scan
Station
Work
Station
Work
Station
Scan
Station
M20i
M20i
Trac
Super-
Sealer
The
Educa
tor
Lamin-
ator
Tabl
etop
Cutte
r
Sic
kin
ger
CL
12
Coil
Roller
Power
Pole
Power
Poles
Paper Pallet
DocuColor
5252
Supplies
Paper Pallet
Paper Pallet
Paper Pallet
Mobile Finishing Table
Mobile Finishing Table
Mobile Finishing Table
VeloBind
323
Ibico
EP-28
Power
Pole
DocuTech
6135
DocuTech
6135
DocuTech
6135
Power
Pole
Power
Pole
GBC
16DB2 &
111PM-3
GBC
Magna-
punch
FF1
DigiB
Mail Merge
FF?
Digi?
FF?
Digi?
Power
Pole
Shelves
Mail
Horizon PF-P330
Folder
Baumfolder
714
PDI
HD4170
Triumph 4850 A
Cutter
Ibico
HB-24
Coil Roller
Comb Binder
Comb
Binder
&
Punch
Coil
Punch
Comb
Punch
GBC
USP13
Profold Elite
Folder
Interlake S3A
Stitcher
F
r
o
n
t
Paddy Wagon
Drill
Inputs
Red: fax
Orange: customer walk-up
Yellow: email
Pink: Links
Green: courier
Print
Blue: B&W
Purple: color
Finishing
Teal: stitching
Dark red: cutting
Dark orange: folding
Dark green: comb & coil
Dark blue: drill
Desk
3'-0"
6'-0"
Specialty-paper
Shelving
StateofUtahCopyCenter1foot=
10feet=
Closet
Boxing & Finishing Table
Work Table
Table
Table
Work
Station
Scan
Station
M20i
Horizon PF-P330
Folder
GBC
USP13
Paddy Wagon
Challenge
EH3A Drill
DC470
Mobile Finishing Table
Chicago Screws
Supplies Supplies Supplies Supplies
Paper PalletPaper Pallet
Paper Pallet
Paper Pallet
Work
Station
Scan
Station
CustomerCounter
Desk
Filing
Cabinet
Filing
Cabinet
M20i
Profold Elite
Folder
Triumph4850A
Cutter
Clamco
Shrinkwrapper
GBC
Magna-
punch
Sickinger
CL12
PDI
HD4170
Ibico
EP-28
Ibico
HB-
24
GBC
16DB2 &
111PM-3Trac
Super-
Sealer
VeloBind
323
Fridge &
Microwave
The
Educa
tor
Lamin-
ator
MobileFinishingTable
Mobile Finishing Table
MobileFinishingTable
Job File
Trays
Inputs
Red: fax
Orange: customer walk-up
Yellow: email
Pink: Links
Green: courier
Print
Blue: B&W
Purple: color
Finishing
Teal: stitching
Dark red: cutting
Dark orange: folding
Dark green: comb & coil
Dark blue: drill
Interlake S3A
Stitcher
Baumfolder
714
DocuColor
5252
DocuTech
6135B
DocuTech
6135C
DocuTech
6135D
Booklet
Maker
DocuColor
6060B
Table-
top
Cutter
$$$
Cell Routing Algorithm
xij: Portion of job Ji to be manufactured by cell Cj.
tij: Estimated time for cell Cj to finish 100% of job Ji.
(tij=0 if Ji cannot be finished in Cj)
minimize F(x11,x12,…, xnm)
subject to
xij >=0, for all i,j
x11+x12+…+x1m=1, …,xn1+xn2+…+xnm=1
e.g.F=Gj(x11, x12,…, xnm) =x1j t1j+x2j t2j+…+xnj
tnj.
(F= Time that a given cell j isbusy)
minimize max {L1G1(x11, …, xnm),…, LmGm(x11,
…, xnm)}
subject to
xij >=0, for all i,j
x11+x12+…+x1m=1, …, xn1+xn2+…+xnm=1
Ljsare nonnegative constantsselected to expressour
preferences among the costs
e.g.
Take L1 >>L2,…, L1 >>Lm, to emphasize the busy
time of the first cell over the others.
Take L1 =L2 = … =Lm, to minimize the time to
finish all jobs.
Optimized
Process
Continuous Improvement
7. Recognizing the new trend in Operations Research: From
Problem-driven OR to Data-Driven OR
Problem driven OR
– The starting point is a problem identified by an academic or an
industry professional, and the challenge is to find answers by
developing new theory or new insights about the problem at hand
Data driven OR
– The starting point is not a specific problem, but rather a large data
set that allowed us to identify new opportunities
References
Simchi-Levi, D. “OM Research: From Problem-Driven to Data-Driven Research”, Manufacturing & Service Operations
Management Vol. 16, No. 1, Winter 2014, pp 2-10
PARC | 7
8. Examples of Science of Process Optimization
• Data-driven simulation optimization
– Optimization of non-linear stochastic processes where the objective function is
evaluated using simulation models driven by actual process data
– A very rich area of research with a regular full-track at WSC – a premier conference
in the field of stochastic discrete-event simulation
• Process flexibility
– Cellular vs departmental workflows
– Flexibility allocation in a service enterprise
• Inter-process buffer optimization
• Workflow design and scheduling in the presence of heavy-tail job size
distribution
PARC | 8
9. Simulation Optimization – Discrete/Continuous
Optimization via simulation
where g(x) is the single objective
represented as the expected value of a random variable where represents the
randomness.
denotes the d-dimensional vectors with integer components
is the continuous parameter space
The distribution of is unknown function of the decision variable x but can be
determined via simulation models
Why is this hard?
• Simulation is computationally demanding
• The randomness makes it hard to compare and evaluate outcomes during the
optimization iterations and provide guarantees on optimality.
)],([)(
),(min
xYExgwhere
xxg
=
),( xY
),( xY
d
Z
d
R
PARC | 9
10. Areas of simulation optimization research
• Discrete optimization via simulation
• Ranking and selection
• Efficient simulation budget allocation
• Continuous optimization via simulation
• Random search methods
• Response surface methodology
• Stochastic gradient estimation
• Stochastic approximation
• Sample average approximation
• Stochastic constraints
• Variance reduction techniques
• Model-based stochastic search methods
• Markov decision processes
US Government funding agencies
• National Science Foundation
• Air Force Office of Scientific Research
• Department of Energy
• NIH
• Office of Naval Research
Many of these have been awarded within the last 5 years
PARC | 10
11. Exploring Process Flexibility for Service Process Design
• Flexibility is expensive
• How much flexibility is enough and how to allocate it
optimally is still an active area of research
Seminal work on process flexibility
Jordan, W.C, Graves, S.C. “Principles on the Benefits of Manufacturing
Process Flexibility”. Management Science Vol. 41, No. 4, April 1995.
Multi-stage supply chain
Graves SC, Tomlin BT (2003) Process flexibility in supply chains.
Management Sci. 49(7):907–919.
Queuing networks
Iravani SM, Van Oyen MP, Sims KT (2005) Structural flexibility: A new
perspective on the design of manufacturing and service operations.
Management Sci. 51(2):151–166.
Call centers
Wallace RB, Whitt W (2005) A staffing algorithm for call centers with skill-
based routing. Manufacturing Service Oper. Management 7(4):276–294.
Proof of optimality of closed chains
Chou MC, Chua GA, Teo C-P, Zheng H (2010b) Design for process
flexibility: Efficiency of the long chain and sparse structure. Oper. Res.
58(1):43–58.
Simchi-Levi D., Wei, Y. “Understanding the performance of the long and
sparse designs in process flexibility”. Operations research. Vol. 60
No. 5, Sep-Oct 2012 pp 1125-1141
Open Issues In Flexibility Research
•For example: With asymmetric demand, closed chain may not be optimal
•Legros B., Jouini O., Dallery Y., “A flexible architecture for call centers with
skill-based routing” International Journal of Production Economics 159
(2015), 192-207
• “The most well-known architectures with limited flexibility such as
chaining fail against such symmetry. We propose a new architecture
referred to as single pooling with only two skills per agent and
demonstrate its efficiency”
AAdam
BBob
CCarol
DDianne
EEdith
FFred
AAdam
BBob
CCarol
DDianne
EEdith
FFred
Limited
Flexibility
Closed
Chain
Network
Inflexible
network
PARC | 11
12. Buffer Optimization: Inter-machine buffers and production
uncertainty
System
Efficiency
Buffer Size
B
Gershwin, S.B., “Manufacturing Systems Engineering”. Prentice Hall.
PARC | 12
13. Developing solutions to deal with high levels of task size
variability in service processes
• Let X be a random variable with cdf F(x)
= P[X≤ x] and complementary cdf (ccdf)
Fc(x) = P[X>x]. We say here that a
distribution F(x) is fat-tailed if
Fc (x) ~ cx-a 0<a<2
In the limit of x->∞
α
dlogx
(x)dLogF
lim
x
c
-=
→
• Size-based binning policies are
more effective when task size
distribution is heavy-tailed in
distributed server processing
• Methods have to be adapted to
take into account production
characteristics such as setups,
job arrival patterns, multiple job
types etc.
h
xxdF
h
M
xxdFxxdFxxdF
p
k
px
x
x
x
x
kx
h
h
=====
=
= -
)(
)(...)()(
1
2
1
1
0
x0=k xh=px1 x2 xi
1.0
F(x) = Pr{ X ≤ x }
1
1
)( 1
1
11
=
+
-
=
-
--
a
a
a
aa
if
if
k
p
k
p
h
i
k
h
ih
x
h
ii
k: smallest job size
p: largets job size
a: Exponent in the Bounded Pareto distribution
M: Mean
h: Number of cutoff points
Bounded Pareto Distribution f(x) = (akax-a-1)/(1-(k/p)a)
pxk
pdf
job size
• On Choosing a Task Assignment policy for a distributed server system: M. Harchol-Balter, M.E. Crovella, C.D Murta,
Lecture Notes in Computer Science, Springer Berlin/Heidelberg.
• Size-independent vs. size-dependent policies in scheduling heavy-tailed distributions. Nham, J. (MS Thesis, MIT)
PARC | 13
14. • Simulation optimization enables us to optimize
complex service processes in the presence of
variability and uncertainty
• Optimal allocation of flexibility in service
operations can deliver almost the same benefit as
full flexibility
• Buffer optimization can enable systems to delivery
high throughput in the presence of failures and
downtime
• Novel scheduling strategies are utilized for dealing
with extremely high levels of variability in task size
distributions
PARC | 14
Benefits of a scientific approach to business process
optimization
)],([)(
),(min
xYExgwhere
xxg
=
System
Efficiency
Buffer Size
x0=k xh=px1 x2 xi
pdf
job size
AAdam
BBob
CCarol
DDianne
EEdith
FFred
AAdam
BBob
CCarol
DDianne
EEdith
FFred
15. PARC | 15
Lean Document Production: An instance of SBP for Print
Production Services Domain
Data collection
using IT systems
& customized
technologies
(RFID, handheld
devices)
Data analysis
& simulation
optimization
framework
Workflow
models
using
simulations
Real-time
operational
techniques
Smart
Business
Process
SO techniques
• Ranking & selection
• Greedy algorithms
• Simulated
annealing
Data collection tools
• Wireless handheld
event data collection
tool
• Job/shop templates
• Standardized
questionnaires
16. Diverse Types of Print Shops
BellandHowell
Inserter
Inserter
Inserter
PB 8 Series
PB 8 Series
PB 8 Series
Inserter
Cage
Inserter Room
Desk
Desk
Desk
Desk
Desk
Desk
LOADING
Server Room
Mailing
Area
Input
Desk
P
r
i
n
t
e
r
1
Cutter
P
r
i
n
t
e
r
2
P
r
i
n
t
e
r
4
P
r
i
n
t
e
r
3
Desk
SQA
DESK
Moore
Sealer
Desk
Roll System
Printer
Desk
H
I
L
I
T
E
P
R
I
T
E
R
Desk
ShrinkWrapper
Pillar
DeskDesk
Desk
Desk
P
r
i
n
t
e
r
3
Desk
ShrinkWrapper
Roll System
Printer
55' - 4 1/4"
2' - 4 7/8"
18'-12"
62' - 1 1/8"
12'-0"
PAPER
PAPER
SKRINK
WRAP CUTTER DRILL
DOCUTECH # 2DOCUTECH # 1
D
O
C
U
T
EC
H
#
3
53905
1
0
0
D
O
C
4
0
B
DOC 40 A
DC
265 A
DC
265B
FAX
55' - 4 1/4"
Transaction Print Shop Copy Shop
Offset Print ShopCombination of Transaction & Copy Shop
PARC | 16
17. Print production services workflow overview
Collater
Cutter
Binder
Postage
Meter
Shipping
Electronic
Submission
Color Printer
Black & White
Printer
Large continuous
feed printer
Paper
cart
WIP
Jobs
Customer
Walk-in
Paper
cart
WIP
Finishing Mailing
Graphics
design
Pre-press
Customer
service
Printing
PARC | 17
18. 360003000024000180001200060000
Median
Mean
4003002001000
A nderson-Darling Normality Test
V ariance 2034062.0
Skew ness 16.142
Kurtosis 383.015
N 1692
Minimum 1.0
A -Squared
1st Q uartile 6.0
Median 28.0
3rd Q uartile 152.0
Maximum 39802.0
95% C onfidence Interv al for Mean
267.4
425.32
403.4
95% C onfidence Interv al for Median
25.0 33.0
95% C onfidence Interv al for StDev
1379.7 1476.0
P-V alue < 0.005
Mean 335.4
StDev 1426.2
95% Confidence Intervals
Job Size (Page Count) distribution
Challenges in optimizing production processes in a services
business
Production is done in customer
premises-Not a controlled factory
environment
Multiple sources of variability-
analytical modeling impractical
• Job
– arrival and due dates
– sizes
– types (routings)
– Volume fluctuation
• Equipment
– Random machine failure
and repair
– Processing rate variability
• Personnel
– Labor skill differences
– Flexible work schedules
Day
Volume
39035131227323419515611778391
6000000
5000000
4000000
3000000
2000000
1000000
0
_
X=2220922
UCL=5074045
LB=0
3_5 4_5 5_5 6_5 7_5 8_5 9_5 10_511_512_51_6 2_6 3_6
111
Daily Production Volume
Failure Repair
PARC | 18
19. Traditional Print Shop Operation Frameworks
BellandHowell
Inserter
Inserter
Inserter
PB 8 Series
PB 8 Series
PB 8 Series
Inserter
Cage
Inserter Room
Desk
Desk
Desk
Desk
Desk
Desk
LOADING
Server Room
Mailing
Area
Input
Desk
P
r
i
n
t
e
r
1
Cutter
P
r
i
n
t
e
r
2
P
r
i
n
t
e
r
4
P
r
i
n
t
e
r
3
Desk
SQA
DESK
Moore
Sealer
Desk
Roll System
Printer
Desk
H
I
L
I
T
E
P
R
I
T
E
R
Desk
ShrinkWrapper
Pillar
DeskDesk
Desk
Desk
P
r
i
n
t
e
r
3
Desk
ShrinkWrapper
Roll System
Printer
•High equipment flexibility
•Low labor flexibility
•Classical job-shop scheduling
Job Shops Inline or Flow Shops
Print Shrinkwrap
•Automated inline systems
•Dedicated line (inflexible)
Mail
Print Insert Ship
PressureSeal
Shrinkwrap
Fulfillment
PARC | 19
20. LDP Lean Document Production Solution – The Notion of
Autonomous Cells
BellandHowell Inserter
Inserter
Inserter
PB 8 Series
PB 8 Series
PB 8 Series
Inserter
Cage
Inserter Room
Desk
Desk
Desk
Desk
Desk
Desk
LOADING
Server Room
Mailing
Area
Input
Desk
P
r
i
n
t
e
r
1
Cutter
P
r
i
n
t
e
r
2
P
r
i
n
t
e
r
4
P
r
i
n
t
e
r
3
Desk
SQA
DESK
Moore
Sealer
Desk
Roll System
Printer
Desk
H
I
L
I
T
E
P
R
I
T
E
R
Desk
ShrinkWrapper
Pillar
DeskDesk
Desk
Desk
P
r
i
n
t
e
r
3
Desk
ShrinkWrapper
Roll System
Printer
LOADING
Server Room
Mailing
Area
Input
Desk
P
r
i
n
t
e
r
1
Cutter
Inserter
PB8
Series
Inserter
PB8
Series
P
r
i
n
t
e
r
2
P
r
i
n
t
e
r
4
P
r
i
n
t
e
r
3
Desk
Desk
Desk
SQA
DESK
Inserter
PB8
Series
Moore
Sealer
Desk
Cell 4
Roll System
Printer
Desk
Desk
Cell 2
H
I
L
I
T
E
P
R
I
T
E
R
Desk
Shrink Wrapper
Pillar
Cell 3
Cell 1
An autonomous cell has all the resources (equipment and labor) to create a few
different types of finished products
PARC | 20
21. Routing
Sequencing and
Release Control
Batch-Splitting
• Job routing to cells occurs at jobs queued at the shop level
• Sequencing and release control occurs at the jobs queued at the cell interface
• Optimal batch-splitting occurs within the cell
LDP Lean Document Production Solution- Hierarchical
Scheduling
PARC | 21
22. Modeling, analysis and optimization algorithms
Cell Routing Algorithm
xij: Portion of job Ji to be manufactured by cell Cj.
tij: Estimated time for cell Cj to finish 100% of job Ji.
(tij=0 if Ji cannot be finished in Cj)
minimize F(x11,x12,…, xnm)
subject to
xij >= 0, for all i,j
x11+x12+…+x1m=1, …, xn1+xn2+…+xnm=1
e.g. F = Gj(x11, x12,…, xnm) = x1j t1j+x2j
t2j+…+xnj tnj.
(F = Time that a given cell j is busy)
minimize max {L1G1(x11, …, xnm), …, LmGm(x11,
…, xnm)}
subject to
xij >= 0, for all i,j
x11+x12+…+x1m=1, …, xn1+xn2+…+xnm=1
Ljs are nonnegative constants selected to express
our
preferences among the costs
e.g.
Take L1 >> L2, …, L1 >> Lm, to emphasize the
busy
time of the first cell over the others.
Take L1 = L2 = … = Lm, to minimize the time to
finish all jobs.
Batch Splitting Algorithm
T(b) = s1 + (r1+r2+…+rn) b + (N/b –1)
max{s1+r1b, s2+r2b, …, sn+rnb}.
• Compute the set of integers bs that
divide N exactly.
• Evaluate T(b) for all the bs in this set,
and store these
values in a vector.
• Select the minimum component of
this vector. The b
corresponding to this component is
the optimal batch size.
Print Black
& White
Pages
Print Color
Pages
Collate
& Trim
Fold Stitch
Mail
Print Black
& White
Pages
Print Color
Pages
Collate
& Trim
Fold Stitch
Mail
Print shop
independent
job description
language
Production
workflow
Signature booklet
with black and
white pages
Automated workflow mapping
Creation of discrete event simulation
models from declarative specification
of shop, job and algorithms
PARC | 22
23. Automated modeling and simulation
Inputs
•Shop, cell, equipment
and operator
configuration and
schedule
•Scheduling policy
parameters
Automated process
models incorporating
scheduling, batching,
dispatching rules and
operator assignment
Output Analysis
Iterative Design, Analysis and Optimization
PARC | 23
25. Examples of Simulation Optimization within LDP
Equipment
optimization
Operator
optimization
Rai, Gross, Ettam,
“Simulation-based
optimization using greedy
techniques and simulated
annealing for optimal
equipment selection
within print production
environments”
PARC | 25
26. LDP Lean Document Production Assessment Process
Current State Analysis
Job Types Capacity Analysis
Implementation
Bar-coded
Job Ticket
Control Logic
Tracking Database Internet
Server
Site Survey & Data Collection
Finishing Room
Print Room
Cell Design & Floor Plan Studies
Autonomous
Cells
Simulation results
Iterate over
multiple
scenarios
PARC | 26
27. Change Management Tools
Project
Definition
Roles
Shared need
Shape vision
and
Communication
Mobilize
commitment
Systems &
structures
Executed SOW
Force Field Analysis
More of / Less of
In/out frame
Assess Recommend Move & Install Ramp UP Steady State
Virtual
Pilot
Calendar Test
Management Roles
Stakeholder analysis
Threat vs. Opportunity Matrix “North Star”
Communication Plan / Lean Education Part I and II
Elevator Speech
Stakeholder analysis
Lean Metrics/ Pre and post transformation
PARC | 27
28. Benefits to Operations from deployment of LDP
Produce
More Jobs
Reduce
Your Costs
Grow
Your Business
Delight
Your Customers
• Improve job
turnaround time
by more than 20%
• Improve quality
(fewer defects
and late jobs)
• Manage customer
demands more
effectively
• Improve ability to
respond to rush
orders
• Improve
productivity by
more than 20%
• Improve capacity
by more than 10%
• Simplify job
management
(fewer touch
points)
• Do more with fewer
resources
• Reduce labor cost
by more than 12%
• Save more than
15%
in floor space
• More effectively
utilize/deploy
equipment
• Reduce storage and
obsolescence costs
• Increase revenue
by expanding
capacity of existing
capital and labor
• Increase profits and
cash flow, giving
you the opportunity
to INVEST and
GROW
PARC | 28
30. Lean Document Production: Success Stories
Global Financial Services Firm
Challenge
• Facing regulatory compliance pressures
to deliver more information in less time
Solution
• Applied Lean Document Production to consolidate
two statement-processing centers
• Streamlined operations to reduce labor costs, floor
space, and maintenance expenses
• Reconfigured and updated printing and insertion
equipment
Results
• First-year savings of $2.5M
• Reduced footprint by 46%, leading to $1M
in cost savings
• Optimized staffing allocation, increased color
output quality
“Using Xerox Lean Document
Production, we’re able to
produce customer statements
more efficiently, while
reducing costs significantly.”
Senior Executive,
Global Financial Services
Firm
PARC | 30
31. Printer Room
Inserter Room
Insert Warehouse
Print Forms
WarehouseCreditcard
Productionarea
Large transaction print and mail facility– before LDP
PARC | 31
32. Large transaction print and mail facility– after LDP
Key Results:
❑ 46% savings in floor space
(Emptied the printer
room)
❑ RPC consolidated in the
freed up space, additional
cost avoidance of $1M
Printers & Inserters
Combined in to cells
Freed up
Space
Freed
up
space
PARC | 32
33. Concluding Remarks
• The notion of Smart Business Process (SBP) was introduced
– A business process that is (continually) optimized and operated using data-
driven simulation optimization and real-time productivity analytics encapsulated
in standalone and web-based software solutions
• Some examples of underlying science behind SBP were presented
• An instantiation of SBP in the domain of print production services namely
Lean Document Production was discussed in detail to demonstrate
breakthrough operational productivity improvements
• SBP is being expanded to other areas:
• Transaction processing
• Healthcare/Hospitals
• Customer care/ call centers
• Transportation services
• …
PARC | 33