Search

Search Results

• 1. [Article] Digital Single-Lens Reflex Camera Technology Forecasting with Technology Forecasting using Data Envelopment Analysis

  With the advancement of digital image technology, the biggest change in the camera market is that digital single-lens reflex cameras (DSLRs) have replaced film type single-lens reflex cameras, which have ...

  Citation × Citation Citation Abstract Copy Title: Digital Single-Lens Reflex Camera Technology Forecasting with Technology Forecasting using Data Envelopment Analysis Author: Abdulai, Mimie, Charoensupyan, Apisit, Hu, Nan, Koosawangsri, Rachanida, Wang, Xiaowen, Yoon, Byung Sung Year: 2012 With the advancement of digital image technology, the biggest change in the camera market is that digital single-lens reflex cameras (DSLRs) have replaced film type single-lens reflex cameras, which have seen very little development lately. This advancement is reflected in some core technologies of DSLRs such as digital image sensors and electronic shutter mechanisms, which have allowed taking photographs even in the toughest conditions. Therefore, as a disruptive technology, the developments in DSLR technologies are worthy of notice. This paper utilizes technology forecast using data envelopment analysis (TFDEA), which is a quantitative forecasting method. In the study, we use input and output data collected from about a hundred DSLRs of the top five currently dominant brands such as Canon, Nikon, Sony, Pentax and Olympus. The anticipation is that we observe changes in DSLR technologies. Final results show future DSLRs’ capabilities and provide insight that TFDEA, as a forecasting method, can be applicable to determine market trends of consumer electronics as well as technologies. Title: Digital Single-Lens Reflex Camera Technology Forecasting with Technology Forecasting using Data Envelopment Analysis Author: Abdulai, Mimie, Charoensupyan, Apisit, Hu, Nan, Koosawangsri, Rachanida, Wang, Xiaowen, Yoon, Byung Sung Year: 2012 With the advancement of digital image technology, the biggest change in the camera market is that digital single-lens reflex cameras (DSLRs) have replaced film type single-lens reflex cameras, which have seen very little development lately. This advancement is reflected in some core technologies of DSLRs such as digital image sensors and electronic shutter mechanisms, which have allowed taking photographs even in the toughest conditions. Therefore, as a disruptive technology, the developments in DSLR technologies are worthy of notice. This paper utilizes technology forecast using data envelopment analysis (TFDEA), which is a quantitative forecasting method. In the study, we use input and output data collected from about a hundred DSLRs of the top five currently dominant brands such as Canon, Nikon, Sony, Pentax and Olympus. The anticipation is that we observe changes in DSLR technologies. Final results show future DSLRs’ capabilities and provide insight that TFDEA, as a forecasting method, can be applicable to determine market trends of consumer electronics as well as technologies. Close

• 2. [Article] Employee Training Schedule Optimization

  As the amount of technical information explodes in volume and technical professionals struggle with staying current with the latest information, one important channel is the large scale trade or technical ...

  Citation × Citation Citation Abstract Copy Title: Employee Training Schedule Optimization Author: Alzahrani, Ahmed, Gupta, Apeksha, Hsu, Allen Wei Chen, Tiwari, Rashi, Vinchhi, Komal Year: 2016 As the amount of technical information explodes in volume and technical professionals struggle with staying current with the latest information, one important channel is the large scale trade or technical conference. Bringing value back to business by keeping employees up to date with trends in the industry is something that a company expects when it invests time and money in sending its employees to such conferences. To choose sessions from a large conference is an exacting job for employees as they need to scroll over such huge data set and select the best session that matches their interest and preferences. This results in large number of employee hours consumed, which ultimately can prove expensive to a company. Due to these reasons, organizations might find formal planning methods beneficial to get the best return on their investment. To test this, we are examining the case of a major automotive industry company named Daimler Trucks North America. This company is sending six of its employees from the IT department to attend a conference conducted by IBM called “World of Watson”. Human Resource and IT capability being key resources to create competition and differentiating factors between two companies. Training presents a prime opportunity to expand the knowledge base of all employees, but companies and employees alike find this activity a little taxing, what with training conferences having a rigid schedule, with back to back sessions, some even running parallel to each other and add to this the work schedule of employees and their varying skills level and interests, companies and employees face variety of issues to be sorted out before employees can actually figure out which session to attend. The main objective of this project is to take the interest of the employee into account and develop an optimized schedule for 6 Daimler employees who have to attend a three-day training conference being organized by IBM. The primary metrics used for this schedule planning are the availability of employee, their skill level, and the area of preference. To increase overall coverage of sessions, we came up with a model that does not assign the same session to two employees. The model also assures that no time slot is left vacant. This helped with increasing the scope of learning. Currently, Daimler has a traditional and delegated approach, where another employee was given the task of going through the list of sessions and to choose the sessions each attendee should attend. The main problem with this approach is that the entire decision of selecting sessions is manual and hence not very personalized. This paper attempts to propose a scheduling tool based on excel to cope with the complex task of picking the best session to be attended by an employee based on their pillar preferences. Title: Employee Training Schedule Optimization Author: Alzahrani, Ahmed, Gupta, Apeksha, Hsu, Allen Wei Chen, Tiwari, Rashi, Vinchhi, Komal Year: 2016 As the amount of technical information explodes in volume and technical professionals struggle with staying current with the latest information, one important channel is the large scale trade or technical conference. Bringing value back to business by keeping employees up to date with trends in the industry is something that a company expects when it invests time and money in sending its employees to such conferences. To choose sessions from a large conference is an exacting job for employees as they need to scroll over such huge data set and select the best session that matches their interest and preferences. This results in large number of employee hours consumed, which ultimately can prove expensive to a company. Due to these reasons, organizations might find formal planning methods beneficial to get the best return on their investment. To test this, we are examining the case of a major automotive industry company named Daimler Trucks North America. This company is sending six of its employees from the IT department to attend a conference conducted by IBM called “World of Watson”. Human Resource and IT capability being key resources to create competition and differentiating factors between two companies. Training presents a prime opportunity to expand the knowledge base of all employees, but companies and employees alike find this activity a little taxing, what with training conferences having a rigid schedule, with back to back sessions, some even running parallel to each other and add to this the work schedule of employees and their varying skills level and interests, companies and employees face variety of issues to be sorted out before employees can actually figure out which session to attend. The main objective of this project is to take the interest of the employee into account and develop an optimized schedule for 6 Daimler employees who have to attend a three-day training conference being organized by IBM. The primary metrics used for this schedule planning are the availability of employee, their skill level, and the area of preference. To increase overall coverage of sessions, we came up with a model that does not assign the same session to two employees. The model also assures that no time slot is left vacant. This helped with increasing the scope of learning. Currently, Daimler has a traditional and delegated approach, where another employee was given the task of going through the list of sessions and to choose the sessions each attendee should attend. The main problem with this approach is that the entire decision of selecting sessions is manual and hence not very personalized. This paper attempts to propose a scheduling tool based on excel to cope with the complex task of picking the best session to be attended by an employee based on their pillar preferences. Close