REDUCING CUSTOMER WAIT TIME AND IMPROVING PROCESSES AT ABC’s ATV RENTALS

This project serves to explore the system bottlenecks of a small, family owned ATV rental company. The main objective is to reduce the average time a customer spends in the system, focusing on customer wait time as well as other areas that can be improved. This was done by collecting time studies and inputting the values into simulation software, which was run to represent the current system as well as various other possible scenarios encountered by rental companies. While creating the simulation, adaptive techniques were incorporated into the simulation. These techniques aim to increase the durability and reusability of the simulation for future use. An example of incorporating adaptive simulation is through having the simulation software draw values from an Excel spreadsheet. This example of adaptive simulation targets the efficiency of use, as values and formulas are easier to calculate and visualize in Excel than the simulation software. Through the scenarios created in the simulation software, the main system bottleneck was discovered to be the company’s trailer fleet size. Several scenarios were then created to further explore the theory and resulted in confirming it. The results of this analysis conclude that to reduce customer wait time in the system, the company should increase its fleet size by one trailer. A secondary, no cost solution is to eliminate ATV load/unload times by moving ATVs to the dunes prior to customer arrival instead of loading them on a customer by customer basis

Essays on Online Browsing and Purchase

Essay One: Modeling Online Browsing and Purchase of Airline Tickets Online purchases are increasingly becoming a significant portion of total purchases in most product categories. While prior research in marketing has looked at information search and purchase decisions separately, we use a joint framework to study consumers\u27 online browsing and purchase of airline tickets in a unique dataset of household-level dynamic click stream panel data. We use a three-stage model to study: i) the choice of the first website visited,: ii) the duration of browsing on travel websites before making a purchase: iii) the choice of the website where consumers will make the purchase, and how a later stage choice is affected by decisions in previous stages. We simultaneously estimate these three models which constitute a non-linear discrete-continuous equation system using a simulation-based econometric technique. We find significant effects of expected level of expenditure, prior browsing experience, prior purchase experience in determining consumer browsing and purchase behavior. We are able to quantify the differences in attractiveness of a website in getting consumers to first visit them and compare it with the conversion effectiveness of a website in terms of getting consumers who visit to make purchases. A significant impact of choice of the first site visited and browsing duration on choice of the purchase site indicates the importance of modeling these decisions simultaneously. Our results can help managers identify the major determinants of consumer browsing and online purchase behavior, some of which cannot be observed in a brick-and-mortar environment. Essay Two: Modeling Online Multi-category Purchase in Travel In this paper we investigate online purchase behavior at the basket level and model the multi-category purchases in the travel product category. While prior research in marketing has looked at browsing or individual category purchase decisions, we study consumers\u27 online purchase of airline, car rental and hotel purchases together using a unique dataset of household-level dynamic click stream panel data. We use a two-stage model to study: i) the propensity of consumers to purchase a combination of products as a basket and: ii) the choice of the website where consumers will make those purchases. We then estimate the propensity of consumers to purchase a particular combination of products in their basket from different websites. This behavior constitutes a high dimensional system of multinomial equations which are then solved using a simulation-based econometric technique. We find significant effects of site preference, loyalty, prior browsing and demographic variables in determining consumer multi-category purchase behavior. Our results can help managers identify the major determinants of multi-category purchase as well as provide insights into cross promoting as well as upselling other products to consumers who visit their website

Wireless distributed intelligence in personal applications

Tietokoneet ovat historian kuluessa kehittyneet keskustietokoneista hajautettujen, langattomasti toimivien järjestelmien suuntaan. Elektroniikalla toteutetut automaattiset toiminnot ympärillämme lisääntyvät kiihtyvällä vauhdilla. Tällaiset sovellukset lisääntyvät tulevaisuudessa, mutta siihen soveltuva tekniikka on vielä kehityksen alla ja vaadittavia ominaisuuksia ei aina löydy. Nykyiset lyhyen kantaman langattoman tekniikan standardit ovat tarkoitettu lähinnä teollisuuden ja multimedian käyttöön, siksi ne ovat vain osittain soveltuvia uudenlaisiin ympäristöälykkäisiin käyttötarkoituksiin. Ympäristöälykkäät sovellukset palvelevat enimmäkseen jokapäiväistä elämäämme, kuten turvallisuutta, kulunvalvontaa ja elämyspalveluita. Ympäristöälykkäitä ratkaisuja tarvitaan myös hajautetussa automaatiossa ja kohteiden automaattisessa seurannassa. Tutkimuksen aikana Seinäjoen ammattikorkeakoulussa on tutkittu lyhyen kantaman langatonta tekniikkaa: suunniteltu ja kehitetty pienivirtaisia radionappeja, niitten ohjelmointiympäristöä sekä langattoman verkon synkronointia, tiedonkeruuta ja reititystä. Lisäksi on simuloitu eri reititystapoja, sisäpaikannusta ja kaivinkoneen kalibrointia soveltaen mm. neurolaskentaa. Tekniikkaa on testattu myös käytännön sovelluksissa. Ympäristöälykkäät sovellusalueet ovat ehkä nopeimmin kasvava lähitulevaisuuden ala tietotekniikassa. Tutkitulla tekniikalla on runsaasti uusia haasteita ihmisten hyvinvointia, terveyttä ja turvallisuutta lisäävissä sovelluksissa, kuten myös teollisuuden uusissa sovelluksissa, esimerkiksi älykkäässä energiansiirtoverkossa.The development of computing is moving from mainframe computers to distributed intelligence with wireless features. The automated functions around us, in the form of small electronic devices, are increasing and the pace is continuously accelerating. The number of these applications will increase in the future, but suitable features needed are lacking and suitable technology development is still ongoing. The existing wireless short-range standards are mostly suitable for use in industry and in multimedia applications, but they are only partly suitable for the new network feature demands of the ambient intelligence applications. The ambient intelligent applications will serve us in our daily lives: security, access control and exercise services. Ambient intelligence is also adopted by industry in distributed amorphous automation, in access monitoring and the control of machines and devices. During this research, at Seinäjoki University of Applied Sciences, we have researched, designed and developed short-range wireless technology: low-power radio buttons with a programming environment for them as well as synchronization, data collecting and routing features for the wireless network. We have simulated different routing methods, indoor positioning and excavator calibration using for example neurocomputing. In addition, we have tested the technology in practical applications. The ambient intelligent applications are perhaps the area growing the most in information technology in the future. There will be many new challenges to face to increase welfare, health, security, as well as industrial applications (for example, at factories and in smart grids) in the future.fi=vertaisarvioitu|en=peerReviewed

Smart Sustainable Mobility: Analytics and Algorithms for Next-Generation Mobility Systems

To this date, mobility ecosystems around the world operate on an uncoordinated, inefficient and unsustainable basis. Yet, many technology-enabled solutions that have the potential to remedy these societal negatives are already at our disposal or just around the corner. Innovations in vehicle technology, IoT devices, mobile connectivity and AI-powered information systems are expected to bring about a mobility system that is connected, autonomous, shared and electric (CASE). In order to fully leverage the sustainability opportunities afforded by CASE, system-level coordination and management approaches are needed. This Thesis sets out an agenda for Information Systems research to shape the future of CASE mobility through data, analytics and algorithms (Chapter 1). Drawing on causal inference, (spatial) machine learning, mathematical programming and reinforcement learning, three concrete contributions toward this agenda are developed. Chapter 2 demonstrates the potential of pervasive and inexpensive sensor technology for policy analysis. Connected sensing devices have significantly reduced the cost and complexity of acquiring high-resolution, high-frequency data in the physical world. This affords researchers the opportunity to track temporal and spatial patterns of offline phenomena. Drawing on a case from the bikesharing sector, we demonstrate how geo-tagged IoT data streams can be used for tracing out highly localized causal effects of large-scale mobility policy interventions while offering actionable insights for policy makers and practitioners. Chapter 3 sets out a solution approach to a novel decision problem faced by operators of shared mobility fleets: allocating vehicle inventory optimally across a network when competition is present. The proposed three-stage model combines real-time data analytics, machine learning and mixed integer non-linear programming into an integrated framework. It provides operational decision support for fleet managers in contested shared mobility markets by generating optimal vehicle re-positioning schedules in real time. Chapter 4 proposes a method for leveraging data-driven digital twin (DT) frameworks for large multi-stage stochastic design problems. Such problem classes are notoriously difficult to solve with traditional stochastic optimization. Drawing on the case of Electric Vehicle Charging Hubs (EVCHs), we show how high-fidelity, data-driven DT simulation environments fused with reinforcement learning (DT-RL) can achieve (close-to) arbitrary scalability and high modeling flexibility. In benchmark experiments we demonstrate that DT-RL-derived designs result in superior cost and service-level performance under real-world operating conditions

The BG News December 3, 2004

The BGSU campus student newspaper December 3, 2004. Volume 95 - Issue 68https://scholarworks.bgsu.edu/bg-news/8364/thumbnail.jp

A Guide to Designing: The Ultimate WPI PAX East Booth

Our objective for this project was to plan, design, and execute an exhibitor’s booth representing the WPI IMGD program at the Penny Arcade Expo East 2017. We researched experiences of the past two PAX project groups, extensively collaborated with WPI’s Marketing department to design and create content for the booth, and selected student games to showcase. Our goals to brand WPI and WPI IMGD at the event was overall a success

The BG News November 16, 2011

The BGSU campus student newspaper November 16, 2011. Volume 102 - Issue 38https://scholarworks.bgsu.edu/bg-news/9464/thumbnail.jp