ASM2-HuynhThaiHieu-GCD18314-GCD0821

ASSIGNMENT FRONT SHEET Qualification BTEC Level HND Diploma in Computing Unit number and title Unit 06: Managing a Successful Computing Project Submission date 26 June 2019 Date Received 1st submission Re-submission Date Date Received 2nd submission Student Name Huynh Thai Hieu Student ID Class GCD0821 Assessor name GCD18314 Student declaration I certify that the assignment submission is entirely my own work and I fully understand the consequences of plagiarism I understand that making a false declaration is a form of malpractice Student’s signature Grading grid P5 P6 P7 M3 M4 D2  Summative Feedback: Grade:  Resubmission Feedback: Assessor Signature: Internal Verifier’s Comments: IV Signature: Date: Seatek Recruitment Solutions ® PROJECT RESEARCH REPORT: VIRTUAL RECRUITMENT ASSISTANT: SRBot Huynh Thai Hieu Research and Development department, Seatek Recruitment Solutions ® 19 June 2019 TABLE OF CONTENTS INTRODUCTION Project aims .1 Project Objectives METHODOLOGIES .2 Data gathering process Data analyzing process RESULTS QUANTITATIVE RESEARCH (ONLINE SURVEY) RESULTS Average age of participants Current participant’s employment status .4 Participant’s satisfactions about current system QUALITATIVE RESEARCH (MEETING) RESULTS 10 Perspective of recruiter partner companies and participant’s employees 10 Perspective of members from our SRS’s Recruitment solution department 10 Perspective of the experts from Jobster.io platform developer team 11 DISCUSSIONS 12 CONCLUTIONS 14 Important informations gathered after conducting quantitative & qualitative researches 14 Values gained after conducting quantitative & qualitative researches 14 RECOMMENDATIONS .15 REFERENCES 16 TABLE OF FIGURES Figure Participant's average age chart .3 Figure Current participant's employment status chart .4 Figure Participant's satisfaction about current system chart Figure Percentages of participant’s satisfaction on current system (Age 18-23) .5 Figure Percentages of participant’s satisfaction on current system (Age 24-29) .6 Figure Percentages of participant’s satisfaction on current system (Age 29-35) .6 Figure Percentages of participant’s satisfaction on current system (Age 35+) Figure Participant’s satisfaction on system’s job suggestion service Figure Percentage figure of participant’s satisfaction on current system’s job suggestion service (Age 18-23) Figure 10 Percentage figure of participant’s satisfaction on current system’s job suggestion service (Age 24-29) Figure 11 Percentage figure of participant’s satisfaction on current system’s job suggestion service (Age 29-35) Figure 12 Percentage figure of participant’s satisfaction on current system’s job suggestion service (Age 35+) .10 INTRODUCTION Our company, Seatek Recruitment Solutions (SRS) has been successful in building a solid reputation to become one of the nation’s most trusted Search Recruitment Specialists, by providing the best personal service and exceptional quality to both clients and candidates involved With over 25 years of proven experience sourcing candidates in various fields and unique professions nationally, now with the expansion of AI technologies, we believe that new technological developments, such as Project: Virtual Recruitment Assistant: SRBot will enable our team to develop a recruitment solution system far superior to what is currently available PROJECT AIMS Project: Virtual Recruitment Assistant: SRBot aims to: • • • • Automate all time-consuming tasks such as: resume screening and interview scheduling Provide candidates sourcing based on company’s needs Improve quality of hire by providing suitability predictions based on candidate’s data Reduce bias PROJECT OBJECTIVES The development of this project is in order to satisfy following objectives: • Reduce the time-frame of resume scanning process by times • Reduce the cost of hiring employees to scan and analyze resumes • Increase applicants (employees looking for a job) amount • Increase quality of recruiting solutions by providing suggestions through AI’s CV-analysis After the completion of researching process, including not only from our company’s perspective but also including the results gathered through qualitative and quantitative researches conducted, this report has been made in order to reflect entire project, evaluating the usefulness of the project itself, as well as providing justification of recommendation based on the evidence findings and analysis, therefore provide the most dynamic SRBot Project METHODOLOGIES DATA GATHERING PROCESS • At the beginning, the project’s scope and aims, which can be defined by listening to opinions (both technical & personal) from members who has been running the current Recruitment system (Members of SRS’s Recruitment Solution Department) was the first target for our team to investigate • Next up, in order to have a broader view of this project, as well as identify all the needs and feedbacks from the applicants, the Quantitative research - Online survey has been sent to the latest 200 applicants who did found their job by applying to our company’s Job suggestion system • In order to identify all the needs and feedbacks from the customer companies, as well as companies who are currently looking for a recruitment solution and interested in our company, the meeting conference has been conducted to full-fill the Qualitative research In this meeting conference, all the ideas and opinions gathered from participants will be recorded & documented to serve the data analysis process Participants who had the joined the meeting conference was: o o o o Representative persons from the customer companies (including the companies that SRS has co-operated before and new companies that are looking forward to co-operate with SRS in the future) un-employed employees that wanted to find a job through the SRS’s job search system All member of SRS’s Recruitment Solutions Department Experts from Jobster.io platform developer team DATA ANALYZING PROCESS • In order to analyze the data gathered through the Quantitative research – Online survey, we applied the Correlation Analysis method, one of the most common method in Quantitative Data Analysis methods, where the relationships between variables got described and analyzed carefully • In order to analyze the data gathered during the meeting conference (Qualitative research), we applied the Content & Narrative Analysis method, one of the most common method in Qualitative Data Analysis methods, which focuses on using the stories and experiences shared by everyone in the meeting, analyze them to find the answer for the research questions RESULTS QUANTITATIVE RESEARCH (ONLINE SURVEY) RESULTS AVERAGE AGE OF PARTICIPANTS Average age of participants 90 80 80 70 60 50 50 40 40 30 30 20 10 18-23 24-29 29-35 More than 35 FIGURE PARTICIPANT'S AVERAGE AGE CHART A total of 200 employees who already did used our SRS recruitment system to find their job participated in this Online survey The most average age of participants of our system is from 24 to 29 (80 participants), next up is from 18 to 23 (50 participants), 29 to 35 (40 participants) and More than 35 (30 participants) CURRENT PARTICIPANT’S EMPLOYMENT STATUS Current participant's employment status 100 90 80 70 60 50 40 30 20 10 I'm having a job, I'm having a great but I'm job and I love it! considering to find another… Age More than 35 25 Age 29-35 20 10 I'm just got graduated! Internship is what I'm looking for! 10 I'm just quit a boring job! Age 24-29 30 40 10 Age 18-23 15 30 FIGURE CURRENT PARTICIPANT'S EMPLOYMENT STATUS CHART After joined our SRS recruitment system and found their job, Figure represents the opinions of participated employees about their employment status Most of them (90 participants) thinks that they are having a great job Other 60 participants are looking for a job-hop, whereas 20 others have just quitted their job recently 30 participants has been doing internship PARTICIPANT’S SATISFACTIONS ABOUT CURRENT SYSTEM Participant's satisfaction about current system 120 110 100 80 60 50 40 30 20 10 0 Really bad Bad Neutral Good Really good FIGURE PARTICIPANT'S SATISFACTION ABOUT CURRENT SYSTEM CHART Figure 3.1 illustrates the satisfaction of participated employees about our current SRS recruitment system “Neutral” is the most selected one with 110 votes, next up is 50 votes for “Good”, 30 votes for “Bad” and 10 votes for “Really good” Participant's satisfaction on current system (Age 18-23) 4% 0% 10% 20% Really bad Bad Neutral Good 66% Really good FIGURE PERCENTAGES OF PARTICIPANT’S SATISFACTION ON CURRENT SYSTEM (AGE 18-23) Figure represents the percentage of satisfaction about our current SRS recruitment system from the perspective of participants with the average age from 18 to 23 66% of them selected “Neutral” option, 20% of them selected “Good”, 10% for “Bad”, 4% for “Really good” and 0% for “Really bad” Applicant's satisfaction on current system (Age 24-29) 0% 6% 20% Really bad Bad 31% Neutral Good 43% Really good FIGURE PERCENTAGES OF PARTICIPANT’S SATISFACTION ON CURRENT SYSTEM (AGE 24-29) Figure represents the percentage of satisfaction about our current SRS recruitment system from the perspective of participants with the average age from 24 to 29 43% of them selected “Neutral” option, 31% of them selected “Good”, 20% for “Bad”, 6% for “Really good” and 0% for “Really bad” Participant's satisfaction on current system (Age 29-35) 0% 0% 25% 17% Really bad Bad Neutral Good 58% Really good FIGURE PERCENTAGES OF PARTICIPANT’S SATISFACTION ON CURRENT SYSTEM (AGE 29-35) Figure represents the percentage of satisfaction about our current SRS recruitment system from the perspective of participants with the average age from 29 to 35 58% of them selected “Neutral” option, 25% of them selected “Good”, 17% for “Bad”, 0% for “Really good” and “Really bad” Applicant's satisfaction on current system (Age 35+) 0% 10% 6% Really bad Bad 17% Neutral Good 67% Really good FIGURE PERCENTAGES OF PARTICIPANT’S SATISFACTION ON CURRENT SYSTEM (AGE 35+) Figure represents the percentage of satisfaction about our current SRS recruitment system from the perspective of participants with the average age from 35 onward 67% of them selected “Neutral” option, 17% of them selected “Good”, 6% for “Bad”, 10% for “Really good” and 0% for “Really bad” Participant's satisfaction on system's job suggestion service 120 100 80 60 40 20 Values Totally unsuitable Unsuitable Neutral Suitable Totally suitable 30 40 100 25 FIGURE PARTICIPANT’S SATISFACTION ON SYSTEM’S JOB SUGGESTION SERVICE Figure illustrates the opinions about our current SRS’s job suggestion service in perspective of participated employees After using our job suggestion service, most of them (100 participants) found their suitable job Next up are “Neutral” job (40 participants), Totally suitable job (25 participants), Unsuitable job (30 participants) and Totally unsuitable job (5 participants) Age Satisfaction value 18-23 24-29 29-35 More than 35 Totally unsuitable 1 Unsuitable 20 Neutral 10 20 5 Suitable 20 35 25 20 Totally suitable 15 Table Participant’s satisfaction evaluation on system’s job suggestion service (categorized based on their average age) Table represents the satisfaction value in details by categorizing data gathered based on participant’s average age In order to have a better illustration for those number, Percentage figures bellow has been made: Participant's satisfaction on current system's job suggestion service (Age 18-23) 2% 8% Totally unsuitable 30% 20% Unsuitable Neutral Suitable 40% Totally suitable FIGURE PERCENTAGE FIGURE OF PARTICIPANT’S SATISFACTION ON CURRENT SYSTEM’S JOB SUGGESTION SERVICE (AGE 18-23) Figure represents the percentage of participant’s satisfaction on current system’s job suggestion service (from the average age of 18 to 23) 40% of them found their suitable job, 30% selected “Totally suitable”, 20% for “Neutral”, 8% for “Unsuitable” and 2% for “Totally unsuitable” Participant's satisfaction on current system's job suggesting service (Age 24-29) 4% 2% Totally unsuitable 25% Unsuitable Neutral 44% Suitable 25% Totally suitable FIGURE 10 PERCENTAGE FIGURE OF PARTICIPANT’S SATISFACTION ON CURRENT SYSTEM’S JOB SUGGESTION SERVICE (AGE 24-29) Figure 10 represents the percentage of participant’s satisfaction on current system’s job suggestion service (from the average age of 24 to 29) 44% of them found their suitable job, 4% selected “Totally suitable”, 25% for “Neutral”, 25% for “Unsuitable” and 2% for “Totally unsuitable” Participant's satisfaction on current system's job suggesting service (Age 29-35) 2% 13% 10% Totally unsuitable 12% Unsuitable Neutral Suitable 63% Totally suitable FIGURE 11 PERCENTAGE FIGURE OF PARTICIPANT’S SATISFACTION ON CURRENT SYSTEM’S JOB SUGGESTION SERVICE (AGE 29-35) Figure 11 represents the percentage of participant’s satisfaction on current system’s job suggestion service (from the average age of 29 to 35) 63% of them found their suitable job, 13% selected “Totally suitable”, 12% for “Neutral”, 10% for “Unsuitable” and 2% for “Totally unsuitable” Participant's satisfaction on current system's job suggesting service (Age 35+) 3% 7% 6% Totally unsuitable 17% Unsuitable Neutral Suitable 67% Totally suitable FIGURE 12 PERCENTAGE FIGURE OF PARTICIPANT’S SATISFACTION ON CURRENT SYSTEM’S JOB SUGGESTION SERVICE (AGE 35+) Figure 12 represents the percentage of participant’s satisfaction on current system’s job suggestion service (from the average age of 35 and beyond) 67% of them found their suitable job, 7% selected “Totally suitable”, 17% for “Neutral”, 6% for “Unsuitable” and 3% for “Totally unsuitable” QUALITATIVE RESEARCH (MEETING) RESULTS After completing the JAD meeting conference, bullet points that describe the perspective of relevant participants about current system can be documented as follow: PERSPECTIVE OF RECRUITER PARTNER COMPANIES AND PARTICIPANT’S EMPLOYEES • Current Recruitment Solution system of our company can be considered as the top best-company that works in this revenue of entire country, thanks to the effective and dynamic results we provided in Recruitment solutions • Current Recruitment Solution system of our company requires quite much time to process However, the end results, which is the suitable candidates we suggested, are sometimes not as effective as they wanted • Current Recruitment Solution system have no automated application or website that provide the online-CV-applying function since always require applicants to send their hard-form CV documents, as well as face-to-face interviewing PERSPECTIVE OF MEMBERS FROM OUR SRS’S RECRUITMENT SOLUTION DEPARTMENT • Current Recruitment Solution system has been working so well based on efforts of 30 dedicated team members, as well as 30 part-time interns 10 • Current Recruitment Solution system cost monthly $10,000 in total to run (humanresource financial purpose, other facilities incurred, etc.) • Current Recruitment Solution system could screen & analyze CV within hours, shorting and documenting the data within 30 minutes The data recorded will be compared with the requirements data from recruiter companies, this process will take hours Then, the job suggestions will be sent back to the employee, if they agree, it should take 15 minutes to setup an appointment for 30 minutes interview In conclusion, entire process that each employee will need to take will take at least hours and 45 minutes (not including interviewing time, the interview date should be agreed between recruiter company and employee themselves) • Current Recruitment Solution system can not work more than hours per day and days per week PERSPECTIVE OF THE EXPERTS FROM JOBSTER.IO PLATFORM DEVELOPER TEAM Current Recruitment Solution system has been working very well, however, based on their experience in applying AI technologies into Recruitment solutions, the efficiency of our system could be far more superior if we implement AI into it: • Reduce the system’s required human-resource by automizing high-volume tasks such as CV screening & analyzing (which can be reduced from hours into 30 seconds each CV) • Provide CV analysis and shorting function 24/7, therefore increase the performance by 20% and no disruption to the workflow • Provide automated chatbot to contact with candidate participants to set up the appointment for interview • Provide the ability to the interview online by giving questions, recording the answer audio from participants and analyze it However, the best amount of team member within the developer team should be at least 12 members (not to mention their expertise in fields of IT developer) in order to make the Project’s development process “free in danger” 11 DISCUSSIONS This section will discuss about the data gathered through results described earlier: • According to the result of Figure 1., with 80 over 200 participated employees who did join our quantitative research, the average age from 24 to 29 represents as the age that people wanted to find a career solution the most, next up is the age from 18 to 23 with 50 participants and 29 to 35 with 40 participants Finally, employees with the average age of 35 onward are the one who has the least interested in finding a career solution (30 participants over 200) • Based on Figure 2., after applied to our SRS recruitment system for a career solution, most of participated employees are feeling happy about their career (90 votes for “I’m having a great job” option), more than a quarter of them (60 participants) looking for a job-hop, whereas 20 others just quitted their “boring” job On the other hand, 30 participants are happy doing their internship program Diving deeper into this figure, half of participated employees who have the average age from 24 to 29 are considering to find a job-hop opportunity (40 votes over 80 participants), when 10 others just quit their job This can be the reason why this age having the largest number of participants applied into our SRS system for a career solution • The results displayed in Figure represents the opinion about participated employee’s satisfaction on our current system, where more than a half of participated employees who took this Online survey agrees that they had no good nor bad feelings and experiences about our system (110 votes for “neutral”) 60 others had positive experiences when applying to our system for career solution, whereas 30 others had negative ones Considering “Neutral” as non-negative experiences, according to Figure 4, 5, 6, Applicants with the average age from 24 to 29 are the one having the least positive (or neutral) experiences applying to our system (80%, combining neutral, good and really good percentages), compared to other average age from 18 to 23 (90%), 29 to 35 (83%) and 35+ (94%) The reason for this phenomenon can be identified that not only because of the large number of participants sharing this average age can cause various opinions, but can also be identified because of the system’s processing time-frame Described earlier in the results gathered after the JAD meeting, everyone from every party’s perspective agrees that our current system requires a lot of time, as well as humanresource to the task Since the participants with the age from 24 to 29 are dynamic and highly evaluate advanced, fast and efficient solution, current human based system of our company may not enough to satisfy all of those criteria On the other hand, applicants with the average age from 35 and above are the one having the most positive experiences applying to our system (94%) The reason for this phenomenon can be identified that employees who sharing this average age has been familiar with the original system, as well as processes within it 12 • According to Figure 7., the outputs of our current system, which is the job suggestions, in perspective participants, has been having high values (100 votes for “suitable” and 25 votes for “Totally suitable”) However, the amount of “unsuitable” or “totally unsuitable” job suggested still remains to take 17,5% overall (35 votes over 200 participants) Table has described in detail the satisfaction value from each age group of participants, combining with Figure 8., we could see the potential of our company in solving Internship Recruitment solutions, where participants who has the age from 18 to 23 feels that Internship opportunity suggested by our system are suitable for them (35 votes over 50 participants of this age) In the other hand, the job suggestions provided to participants from 24 to 29 has the highest “unsuitable” rate compared to other ages (27% compared to 10% of age 18-23, 12% of age 29-35 and 9% of age 35+) • According to the results gathered after the JAD meeting conference for the Qualitative research, our current system can be considered as working well based on humanresource efforts With the contribution of 60 dedicated team members of our company’s Recruitment Solution Department, current human-based system can provide career suggestions back to participants after at least working-day However, based on expert’s experience, if our company allows to develop entire new AI-based system, such as this SRBot Project, the career suggestions to participants phase can be reduced into 30 minutes only 13 CONCLUTIONS IMPORTANT INFORMATIONS GATHERED AFTER CONDUCTING QUANTITATIVE & QUALITATIVE RESEARCHES • Our company’s revenue in terms of a Career Solutions Provider is mostly based on the participation of employees with the average age from 18 to 29, which can be considered as the age where people are dynamic in terms of finding or changing their job and wellexperienced with advanced technologies • Our company’s current system has been providing the Job Suggestions Service at acceptable rate However, the rate of “Unsuitable” job suggested still being a problem at high ratio with 17,5% • Our Customer Companies (Recruitment Partners) wanted us to improve the system in terms of reduce the time-frame required to process and improve the quality of candidates we provide by doing deeper analytics about their company’s culture and needs, then compare to the suitability based on those candidate’s nature • Our participant employees wanted our company to automate and simplify the CV applying process, where they could apply their CV online • Our company’s current Recruitment & Career Solution System works mainly based on efforts of 60 members, costing $10,000 each month to operate Entire process to complete each participant’s case took as least hours and 45 minutes VALUES GAINED AFTER CONDUCTING QUANTITATIVE & QUALITATIVE RESEARCHES After conducting Quantitative & Qualitative Researches, the values gained can be categorized as follows: • Thoughts, opinions about current system has been acknowledged completely from every perspective: partner companies, candidate employees and members within current system itself Thanks to those data, this Project: SRBot’s objective and scope now can be modified to not only fit our company’s business venture needs, but also fit to the needs from partner companies & candidates • Professional experiences during the process of conducting those researches: meeting scheduling, invitations making, questionnaire making, results gathering & documenting • Reduce the distance between our company and our partners, participants by providing them the opportunity to describe their feelings about our system, therefore showing them that our company care about what they want 14 RECOMMENDATIONS After conducting the Quantitative & Qualitative researches, the needs for our company in developing a new integrated system, which is the Project: SRBot, is absolutely necessary Although the Project: SRBot’s Management Plan documents has been made, some changes will be needed to deploy: a) About the project objectives: • • Each Project objective criteria should be re-defined with fixing and detail values of improvement it will make compared to current system, since the Project’s objectives described in the last Project Management Plan version only describe the criteria but with no fixing value of improvement Adding one new objective criteria: Improve the dynamism in interviewing process by provide online interviewing function This objective haven’t issued in the last version of Project Management Plan, but after the meeting conference, this objective exist as one of the features that both candidate employees and partner companies wanted to have By adding this new objective criteria, the Project schedule, WBS and mostly the Project budget will be affected In order to allow this change, stakeholders of this Project: SRBot needs to have a meeting to make the decision If this recommendation got approved, the project budget, team members, schedule and WBS should all be redefined and documented in the Project Change Management section b) About the project schedule, WBS, Team members and budget: In order to successfully accomplish the Project: SRBot within the agreed time-frame that got described in the Project Management Plan with the highest safety rate, considering opinions from experts (Jobster.io experts) after the meeting conference, the Developer Team of Project: SRBot should recruit more members Our current Developer Team of this Project: SRBot consist of members in total, because of the high workload required, team members may need to use Crashing method in order to finish those defined task on-time (described in Project Schedule section in the Project Development Plan) This Crashing method may cause enormous negative impact on entire project if problems during it occurs, so recruiting more quality members (as recommended by experts) should be the best method to protect the project’s development processes In order to allow this change, stakeholders of this Project: SRBot needs to have a meeting to make the decision If this recommendation got approved, the project budget, as well as the project schedule should be redefined clearly and documented in the Project Change Management section 15 REFERENCES 2017 Job Seeker Nation Survey: Finding the Fault Lines in the American Workforce [ONLINE] Available at: https://web.jobvite.com/FY17_Website_2017JobSeekerNation_LP2.html [Accessed 26 June 2019] SocialCops 2019 A Complete Guide to Quantitative Research Methods - SocialCops [ONLINE] Available at: https://blog.socialcops.com/academy/resources/quantitative-research-methods/#section5a [Accessed 26 June 2019] Management, P., 2017 A Guide To The Project Management Body Of Knowledge (PMBOK(r) Guide-sixth Edition / Agile Practice Guide Bundle (PMBOK Guide) Project Management Institute 16