Question.910 - Review “Bank USA: Forecasting Help Desk Demand by Day” from the end of Chapter 9 in the textbook. Then, please use the information to respond to the following: From the case study, determine the challenges faced by the Help Desk at Bank USA and suggest strategies to mitigate them. Using the data on call volume in the case, select a forecasting model to forecast the short-term demand. Justify why this model was selected over other forecasting models. Support your position. Be sure to respond to at least one of your classmates' posts.
Answer Below:
The xxxxxxxxxx faced xx the xxxx Desk xx Bank xxx may xxxxxxx the xxxxxxxxx High xxxx volume xxx Help xxxx may xxxxxxx a xxxxx number xx calls xxxxx leading xx long xxxx times xxx customers xxx increased xxxxxx for xxxxxxxxx Seasonal xxxxxxxxxxxx The xxxxxx for xxx Help xxxxxx services xxx vary xxxxxxx depending xx the xxxx of xxxx such xx during xxx season xx holiday xxxxxxxx periods xxxx of xxxxxxxxx The xxxx Desk xxx have x limited xxxxxx of xxxxx available xx handle xxxxxxxx calls xxxxxxx to xxxxxx wait xxxxx and xxxxxxxxx customer xxxxxxxxxxxx The xxxxxxxxx strategies xxx be xxxxxxxx to xxxxxxxx these xxxxxxxxxx nbsp xxxxxxxx staffing xxxxxx Hiring xxxxxxxxxx staff xxxxxx peak xxxxxx periods xxx help xxxxxx wait xxxxx and xxxxxxx customer xxxxxxxxxxxx Implement xxxx prioritization xxxxxxxxxxxx a xxxxxx that xxxxxxxxxxx customer xxxxx based xx urgency xxx help xxxxxx that xxx most xxxxxxxxx issues xxx addressed xxxxx Utilize xxxxxxxxxxxx options xxxxxxxx customers xxx ability xx resolve xxxxx issues xxxxxxx self-service xxxxxxx such xx a xxxxxxx or xxxxxx app xxx help xxxxxx the xxxxxx of xxxxx to xxx Help xxxx Provide xxxxx training xxxxxxxxx staff xxxx training xx how xx handle xxxx call xxxxxxx as xxxx as xxx to xxxxxx customer xxxxxx efficiently xxx help xxxxxxx customer xxxxxxxxxxxx and xxxxxx stress xxx employees xxxxxxx technology xxxxxxxxxxxx technology xxxx as xxxxxxxxxx intelligence xxxxxxxx or xxxx routing xxx help xxxxxxx the xxxxxxxxxx of xxx Help xxxx and xxxxxx wait xxxxx for xxxxxxxxx nbsp xxx appropriate xxxxxxxxxxx model xx use xxx short-term xxxxxx for xxx Help xxxx at xxxx USA xxxxx likely xx a xxxx series xxxxx specifically xx ARIMA xxxx Regressive xxxxxxxxxx Moving xxxxxxx model xxxx model xxxxx be xxxxxxxxxxx because xxxx volume xx likely xx follow x predictable xxxxxxx over xxxx with xxxx call xxxxxx data xxxxx a xxxx indicator xx future xxxxxx ARIMA xxxxxx are x popular xxxxxx for xxxx series xxxx and xxx effectively xxxxxxx trends xxx seasonality xx the xxxx They xxxx have xxx ability xx handle xxxx that xxx not xx stationary xxxx as xxxx that xxx a xxxxx or x strong xxxxxxxx component xxxxx is xxxxxx in xxxxxx forecasting xxxxxxx model xxxx could xx considered xx Exponential xxxxxxxxx but xxxxx is x better xxxxxx because xx can xxxxxx a xxxxx range xx time xxxxxx data xxx provides xxxx control xxxx the xxxxxxxx process xxxx nbspMore Articles From Operation Management
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