Operations Management Simulation

[Mariela Coxon]

Thisbusiness simulation focuses on operations management and advanced manufacturing within the context of a fully integrated organization. Your students learn all of the fundamentals of a modern manufacturing system, including demand forecasting, production scheduling, changeover, and quality control. They are responsible for the setup of a lean and reliable pull manufacturing operation.

This business simulation game provides your students with the experience of managing the operations of a fully integrated firm. Marketing, sales channel, accounting, and finance decisions are simplified while operations management is deeply explored.

In the Operations Management simulation, your students start a new company that will enter the microcomputer business during a turbulent period in our economic history. An outside group of venture capitalists provides the seed capital to cover their start up costs. Student teams acquire a factory in Asia and start to produce microcomputers. They will market them throughout the world. Your students will have limited financial resources and complete accounting responsibility. They have four decision rounds to get their company off the ground in spite of the economic and political risks that they will face. Within this time frame, they should become a self-sufficient firm, earning substantial profits from their operations.

Balanced scorecard that measures profitability, customer satisfaction, market share in the targeted market segments, manufacturing productivity, financial risk, asset management, preparedness for the future and wealth.

Connect provides a digital learning environent that can save instructors time and provide adaptive, personalised resources to help boost student grades. Your one stop shop for all course content, available with all Education titles.

SmartBook is an adaptive learning tool integrated within Connect that created a personalised learning path based on a student's single strengths and weaknesses. It identifies gaps in knowledge and focuses on areas needing reinforcement, helping students to study smarter, instead of harder. SmartBook's insightful report features highlight student progress or topic areas that need more work, assisting instructors in shaping their teaching strategies

Assignable and gradable end-of-chapter content helps students learn to apply Operations Management concepts and analyse their work in order to form key business decisions. Algorithmic questions allow students to practice problems as many times as they need, to ensure that they fully understand each problem.

Examples are narrated and animated, with step-by-step walkthroughs of algorithmic versions of assigned exercises. These examples allow students to identify, review or reinforce the concepts and activities covered in classes. Availble on most Operations Management titles and providing immediate feedback provided, students can focus on the areas where they need the most guidance.

Featuring on-location video scenarios with real companies and their actual manufacturing processes. Operations Management On-Location Video Series brings topics of crucial operations management to life. All videos are matched to chapters topics within the text and are available within Connect.

Concept overview videos are assignable videos that correspond questions with crucial chapter topics. By reinforcing core learning goals, each video includes auto-graded concept-check questions that bring online courses to life. The videos are created by instructors, and are therefore unique, helping the student feel as though they are in the classroom, even if they are not.

Connect resources include a comprehensive test bank of various question types, which allow the instructor to create auto-graded assessment material with multiple problem types, algorithmic variation, and randomised question order.

The 3D, interactive, game-based simulation allows students to manage the operations of a clothing manufacturing and distribution company. Practice Operations brings operations management to life. Enforcing key concepts while promoting critical thinking and strategic decision making, student will easily engage with this activity.

The game is set up in a number of focused modules that deal with the issues of production process, capacity, supply chain, JIT, labor management, order fulfillment, customer satisfaction, and quality control. Give the game a try with the demo video here.

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This book is recommended for anyone who is interested in supply chain and operations management. It has applied models and a simple, easy to follow format for people without an engineering background. The book combines modeling and management views on decision-making, and introduces the basic principles of using simulation for decision making. To reduce the technical complexity, the main focus is management decision analysis, and using KPI for operational, customer, and financial performance in decision making.

Littlefield is an online competitive simulation of a queueing network with an inventory point. Faculty can choose between two settings: a high-tech factory named Littlefield Technologies or a blood testing service named Littlefield Labs.

In a typical setting, students are divided into teams, and compete to maximize their cash position through decisions: buying and selling capacity, adjusting lead time quotes, changing lot sizes and inventory ordering parameters, and selecting scheduling rules. Different Littlefield assignments have been designed to teach a variety of traditional operations management topics including:

In many modern hospitals, resources are shared between patients who require immediate care, and must be dealt with as they arrive (emergency patients), and those whose care requirements are partly known to the hospital some time in advance (elective patients). Catering for these two types of patients is a challenging short-term operational decision-making problem, since some portion of each resource must be set aside for emergency patients when planning for the number and type of elective patients to admit. This paper shows how symbiotic simulation can help hospitals with important short-term operational decision making. We demonstrate how a symbiotic simulation model can be developed from an existing simulation model by adding the ability to load the state of the physical system at run-time and by making use of conditional length-of-stay distributions. The model is parameterised using 18 months of patient administrative data from an Anonymised General Hospital. Further, we propose a new Δ-Method that is suitable for validating a stochastic symbiotic simulation model. We demonstrate the benefit of our symbiotic simulation by showing how it can be used as an early warning system, and how additional patient-level information which might only become available after admission, can affect the predicted bed census.

The potential benefits of using discrete event simulation (DES) models in health care are well established, and they are often preferred to other modelling approaches because of their ability to emulate the randomness seen in physical systems at a level of detail which is necessary for models to be convincing. Numerous literature surveys have tracked the progress of this modelling approach, including [1,2,3,4,5]. However, the use of DES is often limited to strategic or tactical decision making, and few have attempted to produce models which can help hospitals with short-term (operational) decision making. This is where symbiotic simulation can help.

Symbiotic simulation is a methodology in which there is a close relationship between a physical system and the simulation system that represents it. Based on the types of relationship between the physical system and simulation system, [6] classify symbiotic simulation into several categories. In this paper we describe research relevant to what they refer to as a symbiotic simulation decision support system. In this type of symbiotic simulation, the simulation reads data from the physical system regularly (i.e. to re-initialise the system state and if necessary, update the decision variables and/or simulation parameters). The simulation outputs are then used for what-if analysis, and an external decision maker can choose to change the behaviour of the physical system. In other words, the simulation system indirectly controls the physical system via the external decision maker, instead of an automatic actuator. As operational and real-time data becomes more readily available in health care, the use of symbiotic simulation in health care is becoming more feasible and some early work, for example [7, 8], is starting to appear. However, research into the application of symbiotic simulation in health care is still a long way behind industries such as manufacturing.

Our research aim for this paper is to investigate important issues associated with the development and use of symbiotic simulation decision support systems in the context of operational management of inpatients beds.

In order to undertake this research we developed a whole-hospital, proof-of-concept symbiotic simulation model. We did this with the involvement of a real Anonymised General Hospital (AGH) for a period of about 18 months, after which we lost touch with them due to management changes. This relationship gave us exactly what was needed for this research. It provided us with a rich context, a full inpatient activity dataset for an 18 month period, and clear indications of how they would hope to use a symbiotic simulation, including the main performance measures that would interest them. Hence our proof of concept model is based on a conceptual model agreed with AGH staff, its validity is investigated by comparing model outputs versus actual performance, and its application is demonstrated based on realistic scenarios and real data sets.

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