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 Increase xxxxxxxx levels xxxxxx additional xxxxx during xxxx demand xxxxxxx can xxxx reduce xxxx times xxx improve xxxxxxxx satisfaction xxxxxxxxx call xxxxxxxxxxxxxx Implementing x system xxxx prioritizes xxxxxxxx calls xxxxx on xxxxxxx can xxxx ensure xxxx the xxxx important xxxxxx are xxxxxxxxx first xxxxxxx self-service xxxxxxx Offering xxxxxxxxx the xxxxxxx to xxxxxxx their xxxxxx through xxxxxxxxxxxx options xxxx as x website xx mobile xxx can xxxx reduce xxx number xx calls xx the xxxx Desk xxxxxxx staff xxxxxxxx Providing xxxxx with xxxxxxxx on xxx to xxxxxx high xxxx volumes xx well xx how xx handle xxxxxxxx issues xxxxxxxxxxx can xxxx improve xxxxxxxx satisfaction xxx reduce xxxxxx for xxxxxxxxx Utilize xxxxxxxxxx Implementing xxxxxxxxxx such xx artificial xxxxxxxxxxxx chatbots xx call xxxxxxx can xxxx improve xxx efficiency xx the xxxx Desk xxx reduce xxxx times xxx customers 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 processMore Articles From Operation Management
.ico)
.ico)
.ico)
.ico)
