recruiting selecting training and placing operators
play

RECRUITING, SELECTING, TRAINING, AND PLACING OPERATORS A N INTEG RA - PowerPoint PPT Presentation

RECRUITING, SELECTING, TRAINING, AND PLACING OPERATORS A N INTEG RA TED SYSTEM NE RC Huma n Pe rfo rma nc e Co nfe re nc e 2013 BACKGROUND Ove r the la st five ye a rs, T VA ha s ma de impro ve me nts in o rg a niza tio n, pro c e


  1. RECRUITING, SELECTING, TRAINING, AND PLACING OPERATORS A N INTEG RA TED SYSTEM NE RC Huma n Pe rfo rma nc e Co nfe re nc e 2013

  2. BACKGROUND • Ove r the la st five ye a rs, T VA ha s ma de impro ve me nts in o rg a niza tio n, pro c e sse s, e q uipme nt, a nd so ftwa re . • Ho we ve r, b y the F a ll o f 2011, we re a lize d tha t we ha d hit a pla te a u. • We fe lt tha t a dditio na l impro ve me nts wo uld ha ve to c o me fro m the huma n c o mpo ne nt o f o rg a niza tio na l pe rfo rma nc e . NE RC Huma n Pe rfo rma nc e 3 Co nfe re nc e 2013

  3. BACKGROUND • Dr. K a ufma n a nd his te a m ha d b e e n wo rking with o ur nuc le a r o pe ra to rs sinc e 2004. • His o pe ra to r sc re e ning syste m ha d inc re a se d the pe rc e nta g e o f c a ndida te s tha t ha d suc c e ssfully pa sse d the NRC e xa m a nd re c e ive d the ir o pe ra to r’ s lic e nse . • My tho ug ht wa s to a pply the sa me so rt o f disc ipline to hiring tra nsmissio n syste m o pe ra to rs. NE RC Huma n Pe rfo rma nc e 4 Co nfe re nc e 2013

  4. OLD SYSTEM • Se pa ra te pro g ra ms fo r re c ruiting , se le c tio n, tra ining , a nd pla c e me nt (no inte g ra tio n) • L o ng le a d time s to fill va c a nc ie s • E ve ry de pa rtme nt he a d o pe ra te d inde pe nde ntly — so me time s ste a ling e mplo ye e s fro m me • E xpe rie nc e d c a ndida te s = hig h sa la ry c o st • T ra ining wa s ine ffic ie nt — o ne tra ine e a t a time • No o rg a nize d c a re e r pla nning a nd de ve lo pme nt pro c e ss NE RC Huma n Pe rfo rma nc e 5 Co nfe re nc e 2013

  5. NEW SYSTEM • Re c ruit fo r a who le c la ss — 4 to 6 tra ine e s a t o ne time . • E ntry le ve l po sitio n — AA de g re e in e le c tric a l te c hno lo g y o r e q uiva le nt e xpe rie nc e . So me ha nds o n e xpe rie nc e “will b e he lpful.” • F o r se le c tio n, ta rg e t pe o ple who , a fte r tra ining , ha ve the po te ntia l to mo ve into to a ny o f the ma in de sks a nd b e c o me a b o ve a ve ra g e pe rfo rme rs. NE RC Huma n Pe rfo rma nc e 6 Co nfe re nc e 2013

  6. BENEFITS OF NEW SYSTEM • Ha ving a po o l o f ne w e mplo ye e s re duc e s the le a d time to fill va c a nc ie s. • We use o ur tra ining re so urc e s mo re e ffic ie ntly. • Sa la ry c o st is lo we r. • E mplo ye e de ve lo pme nt a nd c a re e r pla nning use s dia g no stic da ta fro m the hiring pro c e ss. NE RC Huma n Pe rfo rma nc e 7 Co nfe re nc e 2013

  7. EMPLOYEE SELECTION NE RC Huma n Pe rfo rma nc e 8 Co nfe re nc e 2013

  8. THEORY 1. Pe o ple a re the o nly a c tive pa rts o f a n o rg a niza tio n. All the o the r pa rts — fa c ilitie s, c o mpute rs, tra nsmissio n line s — a re ine rt. T he y just sit the re until a pe rso n puts the m to use to c re a te va lue . 2. Be tte r pe o ple c re a te mo re va lue . NE RC Huma n Pe rfo rma nc e 2 Co nfe re nc e 2013

  9. BUSINESS CASE FOR HIRING BETTER PEOPLE • Pe o ple c o st a lo t: sa la rie s, b e ne fits, fa c ilitie s, tra ining . • But the inc re me nta l c o st is sma ll fo r hiring a b o ve a ve ra g e e mplo ye e s. • T he impa c t fro m ha ving a b o ve a ve ra g e e mplo ye e s is impro ve d huma n pe rfo rma nc e fo r ye a rs to c o me . • F e we r fa ilure s in tra ining • Quic ke r le a rning c urve • F e we r e rro rs in o pe ra tio ns • I mpro ve d o rg a niza tio na l pe rfo rma nc e • Mo re e mplo ye e s with the po te ntia l to mo ve up NE RC Huma n Pe rfo rma nc e 10 Co nfe re nc e 2013

  10. SELECTION PROCESS DEVELOPMENT • Ho w do yo u c re a te a se le c tio n pro c e ss tha t b ring s in pe o ple who will b e c o me g o o d e mplo ye e s a nd e xc lude s pe o ple who will b e c o me po o r e mplo ye e s? • T hre e ste ps • De fine jo b re q uire me nts • Ob ta in va lid to o ls to me a sure a pplic a nts a g a inst the jo b re q uire me nts • Ma ke e vide nc e -b a se d de c isio ns NE RC Huma n Pe rfo rma nc e 11 Co nfe re nc e 2013

  11. REQUIREMENTS • Re q uire me nts = K no wle dg e , skills, a ptitude s, a nd mo tiva tio n tha t pe o ple ne e d to pe rfo rm we ll. • Co mmo n c o re o f re q uire me nts fo r five ma jo r jo b s • E .g ., Re a ding c o mpre he nsio n — a b le to re a d, inte rpre t, a nd a pply NE RC sta nda rds a nd o pe ra ting pro c e dure s. • A fe w de sk-spe c ific re q uire me nts, e .g . writing a b ility NE RC Huma n Pe rfo rma nc e 12 Co nfe re nc e 2013

  12. REQUIREMENTS • Co nc e ptua l Ab ility • Ba sic skills — re a ding , ma th, a b stra c t re a so ning • E le c tric a l a ptitude — a b le to unde rsta nd b a sic e le c tric a l c o nc e pts, sc he ma tic s, flo ws, sig na ls, a nd se q ue nc e s • Mo tiva tio n • Co mmitme nt • Re silie nc e • I nte rpe rso na l Skills • E ffe c tive c o mmunic a tio n, writte n a nd o ra l • E a sy to wo rk with NE RC Huma n Pe rfo rma nc e 13 Co nfe re nc e 2013

  13. SELECTION MEASUREMENTS • Co g nitive a b ility te sts • Re a ding c o mpre he nsio n • Writing a b ility • Ma th • E le c tric a l kno wle dg e a nd a ptitude • Ab stra c t re a so ning • Pe rso na lity te st • Vo c a tio na l inte re st te st • Ope ra to r simula tio n • Struc ture d inte rvie w NE RC Huma n Pe rfo rma nc e 14 Co nfe re nc e 2013

  14. SELECTION PLAN NE RC Huma n Pe rfo rma nc e 15 Co nfe re nc e 2013

  15. PILOT STUDY • Be fo re using the me a sure s, we e va lua te d the m o n a n e xisting g ro up o f 27 tra nsmissio n syste m o pe ra to rs. • T his a llo we d us to se e wha t wo rke d a nd wha t didn’ t. • Ga ve us a b e nc hma rk to se t c ut o ff sc o re s. • Pro vide d va lidity e vide nc e fo r the se le c tio n pro c e ss. NE RC Huma n Pe rfo rma nc e 16 Co nfe re nc e 2013

  16. VALIDATION • Va lida tio n: E vide nc e tha t sho ws the a c c ura c y o f the me a sure me nts use d in se le c tio n. • Co mpa re sc o re s fro m the se le c tio n pro c e ss with jo b pe rfo rma nc e . • F o r jo b pe rfo rma nc e , Do ug a nd his ma na g e rs ra te d the o pe ra to rs o n pe rfo rma nc e a nd po te ntia l. NE RC Huma n Pe rfo rma nc e 17 Co nfe re nc e 2013

  17. CORRELATION WITH PERFORMANCE NE RC Huma n Pe rfo rma nc e 18 Co nfe re nc e 2013

  18. CUT SCORE NE RC Huma n Pe rfo rma nc e 19 Co nfe re nc e 2013

  19. CONCLUSIONS • Pre dic ting huma n pe rfo rma nc e is no t a n e xa c t sc ie nc e , b ut o ur a c c ura c y wa s pre tty g o o d. • Using a c ut sc o re o f 7, we wo uld ha ve se le c te d 91% o f the c a ndida te s who we re to p pe rfo rme rs (10 o f 11). • 75% o f the middle pe rfo rme rs (6 o f 8). • And we wo uld ha ve re je c te d 62.5% o f the c a ndida te s who we re b o tto m pe rfo rme rs (5 o f 8). NE RC Huma n Pe rfo rma nc e 20 Co nfe re nc e 2013

  20. CONCLUSIONS • Assuming g o o d q ua lity a pplic a nts, using the a sse ssme nt pro c e ss wo uld • I nc re a se the numb e r o f g o o d pe rfo rme rs • Re duc e the numb e r o f b a d pe rfo rme rs. • Ove r time , the ra tio o f g o o d pe rfo rme rs to b a d pe rfo rme rs wo uld inc re a se , a nd • Org a niza tio na l pe rfo rma nc e will impro ve : fe we r e rro rs, b e tte r re spo nse to e me rg e nc ie s, lo we r o pe ra ting c o sts NE RC Huma n Pe rfo rma nc e 21 Co nfe re nc e 2013

  21. THE ASSESSMENT PROCESS T wo ste ps • Pre -sc re e n. Qua lifie d a pplic a nts ta ke a pe rso na lity te st a nd a vo c a tio na l inte re st te st o nline • Co mpa re pro file to e xisting T Op’ s • Sc re e ns o ut pe o ple who se pe rso na lity a nd wo rk pre fe re nc e s a re no t a g o o d ma tc h. • Asse ssme nt c e nte r. Six ho ur a sse ssme nt using a ll the me a sure s: two o nline te sts, five writte n te st, a n inte rvie w, a nd a n o pe ra to r simula tio n • Applic a nts e va lua te d in re la tio n to pilo t g ro up • 1 = sig nific a ntly b e lo w a ve ra g e , 2 = b e lo w a ve ra g e , 3 = a b o ve a ve ra g e , 4 = sig nific a ntly a b o ve a ve ra g e . NE RC Huma n Pe rfo rma nc e 22 Co nfe re nc e 2013

  22. DATA HANDLING • Pro c e ss yie lds 64 sc o re s fo r e a c h c a ndida te • Da ta is e nte re d into a spre a dshe e t • Ra w sc o re s a re c o nve rte d to sta nda rd sc o re s • Sta nda rd sc o re s a re c o mb ine d into sc o re s fo r the re q uire me nts. • F ina l re po rt NE RC Huma n Pe rfo rma nc e 23 Co nfe re nc e 2013

  23. EVIDENCE BASED DECISIONS • Ca ndida te s e va lua te d a g a inst a ll the c o mpe te nc ie s. • Ma na g e rs g e t a writte n re po rt tha t ra nks the c a ndida te s, hig hlig hts stre ng ths a nd we a kne sse s, a nd g ive s re c o mme nda tio ns fo r se le c tio n. • Ma na g e rs disc uss the re sults, a nd, c o nside ring a ll the e vide nc e , ma ke o ffe rs to the c a ndida te s the y think mo st like ly to suc c e e d. NE RC Huma n Pe rfo rma nc e 24 Co nfe re nc e 2013

Recommend


More recommend