BusinessBusiness & Service

Simulation enables businesses to make informed, data-driven forecasts that guide decision making, produce positive outcomes and build competitive advantage.

ExtendSim Suite ExtendSim AT

Lean AnalysisUsing lean analysis methodology in ExtendSim to model a supply chain network in Europe, a manufacturer realized a 98% increase in service level to the end customer plus demand could now be achieved with about 28% less inventory. Call CenterFinancial institution call center uses ExtendSim to analyze the number of incoming calls versus availability of personnel and can now provide an optimal level of service to their clients.
University of Indianapolis students optimized the Humane Society's adoption process by determining that the addition of Kennel Technicians would improve overall operationsUniversity of Indianapolis by decreasing wait time, thus impacting adoption rates by 32% increase. BankEmployees at a claims department of a bank were specialized to manage particular types of claims so were sometimes experiencing spurts of multiple calls to manage, yet still had a low average work load. The bank used ExtendSim to analyze several options for work cells to optimize personnel utilization - increasing level of service and employee satisfaction.

ExtendSim In Business & Service Industries

What-if analysis.

Improving quality, revenue, and yields.

Evaluation and implementation of Lean and Six Sigma strategies.

Process development, assessment, and improvement.

Risk forecasting.

Reengineering of business processes.

Regulating allocation of research and development resources to competing projects.

Helping users to become a strategic thinkers and, ultimately, agents of change.

Managing human resource problems such as the redesign of employee appraisal systems, allocation of salary increases, and hiring of employes.

Who is Using ExtendSim

Stanford Graduate School of Business BusinessExtendSim is being used worldwide in courses at the undergraduate, MBA, and executive MBA levels by prestigious universities such as Stanford and Harvard.

An information provider used ExtendSim to determine how to be responsive to its call center customers while managing costs.

A plant manufacturing laminated plastic products uses an ExtendSim model to analyze metrics on performance vs. the takt times required, end-to-end cycle times and to look at overall equipment effectiveness.

Students at Villanova University use ExtendSim to analyze the impact of Automatic Vehicle Information (AVI) systems for use on collecting tolls.

A semiconductor company accurately simulated complex recipes and flows in ExtendSim, allowing management to explore operating strategies. Optimized productivity resulted in a 32% reduction in cycle time.

The support requests received by the IT department of a leading oil company arrived at different rates and level of support needed - some at a higher level of urgency than others. The company's original plan had triaged requests based on specialized service areas. As this led to unsatisfactory service levels in response time, an ExtendSim model was successfully used to explore the reallocations of resources among service areas based on volume and mix triggers.

PacBell used ExtendSim to model DSL subscriptions at the turn of this century.

A multi-functional company was formed by aggregating several smaller regional companies, resulting in operations that consisted of numerous redundancies and multiple competing legacy systems. The company needed to consolidate operations, remove redundancies, and transition between the old structure and new. Using ExtendSim, they created a common template, like a management flight simulator, in which any process could be represented and communicated effectively. Issues common to all processes, such as resource costs or the availability, training, and movement schedules for workers were now managed centrally.

Pitney BowesPitney Bowes used ExtendSim to create a Site Analysis Management Model (SAMM) that allowed precise planning and optimization of labor, equipment, and service levels in on-site business support services. SAMM links a customer's business plan to a desired level of service and protects those levels by design creating better mailroom management.

Case Studies Case Studies

Parisian high-rise office building Office Workers and Lifts
1Point2 - Sassenage, France

A Parisian high-rise office building used ExtendSim to simulate the flow of workers utilizing the building's elevators to determine if the elevator distribution was efficient.Download pdf
Deloitte and Touche Management Flight Simulator
Deloitte & Touche Consulting Group

In the mid 90s, the Deloitte & Touche Consulting Group built a Management Flight Simulator in ExtendSim to experiment with different management strategies for internal training sessions. The model focuses on the relationship between the economic factors of staffing levels, sales effort, competition, employee training, and market research. Control panels were set up within the model for trainees to vary input parameters in business factors such as staffing levels. Using this interface, class participants were able to use a sophisticated model with minimal knowledge of simulation.Download pdf
Food Engineering magazine Virtual Engineering's New Frontier
Kevin T. Higgins
Food Engineering Magazine - March 2003


Food Engineering Magazine - processing linePackages that accurately emulate the hybrid processes that characterize food and beverage manufacturing are making simulation more than just a snazzy presentation tool for upper management. Virtual Engineering's New Frontier describes users roughing out their production and packaging lines on screen to gain insight into the dynamics of their lines and where and why bottlenecks occur.Download pdf
Quilmes Flow architecture simulation: a powerful approach to buffer dimensioning in high-speed packaging lines
S.A.Ricardo Rodríguez & Rosana Marino - S&T - Servicio y Tecnología

Pilot experience in the adoption of performance simulation approach to design, redesign, and acquisition of bottling lines for the Zarate plant of Cervecería y Maltería Quilmes S.A. This paper discusses the management of the huge bottling resources of the company: more than 36 lines in 12 factories, in 6 countries, with an aggregated investment value of $ 200 million, occupying more than a thousand people and producing 250 million cases a year of 300 different SKUs.Download pdf
Food Logistics Resource Optimization by Simulation Technique in Food Logistics
É. Hajnal and G. Kollár, Corvinus University of Budapest; G. Almásy, University of Veszprém; K. Kollár-Hunek, Budapest University of Technology and Economics
Applied Ecology and Environmental Research, vol 5, number 1 - May 15, 2007


Any activity that receives inputs and convert them to outputs can be considered a process. So, essentially, similar equations are used in the theory of chemical, biochemical, nuclear, mechanical, and other types of process engineering modeling. This particular research group set out identify the processes, network of processes, process variables and process equations in food logistics. This paper introduces how process building, simulation run, and optimization can be carried out in a Food Distribution Centre with a message-based discrete event simulation software - ExtendSim. Simulation usage as a decision supporting tool in the hand of company management is introduced as well as the affect of wrong decisions on the extent of air pollution coming out from cooling vehicle.Download pdf
OpStat Simulation Modeling in Lean Programs
James J. Curry

OpStatSimulation modeling is a valuable complement to, rather than a replacement for, the traditional lean manufacturing analysis tools. For example, value stream mapping is a key tool; it is a good beginning. But it is static and typically only done for high-volume products or classes of products. Simulation allows the value stream map to become dynamic, and to model the range of probable val- ues—not just averages. It also allows linkages to other tools such as projected capacity utilization in a consistent manner.Download pdf
USC Managing Service Systems with an Offine Waiting Option and Customer Abandonment
Vasiliki Kostami, Sriram Dasu, and Amy R. Ward
Manufacturing & Service Operations Management - December 19, 2008


Many service providers offer customers the choice of either waiting in a line, or going offine and returning at a dynamically determined future time. The best known example is the FASTPASS® system at Disneyland. To operate such a system, the service provider must first make an upfront decision on how to allocate service capacity between the two lines. Then, during system operation, he must dynamically provide estimates of the waiting times at both lines to each arriving customer. The estimation of offine waiting times is complicated by the fact that some offine customers do not return for service at their appointed time.

The paper Managing Service Systems with an Offine Waiting Option and Customer Abandonment shows that when demand is large and service is fast, for any fixed capacity allocation decision, the two-dimensional process tracking the number of customers waiting inline and offine collapses to one dimension, and characterize the one-dimensional limit process as a reflected diffusion with linear drift.

The analytic tractability of this one-dimensional limit process allows to solve for the capacity allocation that minimizes average cost, when there are costs associated with customer abandonments and queueing. It is further shown that in this limit regime, a simple scheme based on Little's law to dynamically estimate inline and offline wait times is effective.


Note: This paper was made possible with the help of an ExtendSim Research Grant for the final project of Vasiliki Kostami in obtaining his PhD in Operations Management from the University of Southern California.Download pdf

VideosVideos

Play VideoExtendSim Discrete Event Tutorial - Car Wash

The key to discrete event modeling is the construction of a flow diagram using blocks to represent the problem's operations and resources. The most common discrete event model involves the handling of one or more waiting lines or queues, such as those found in supermarkets, factories, banks, etc.

https://www.youtube.com/watch?v=OGOybogbCTo