TY - GEN
T1 - Workforce planning models for distribution center operations
AU - Krishna, Athul Gopala
AU - Prabhu, Vittaldas V.
N1 - Publisher Copyright:
© IFIP International Federation for Information Processing 2016. All rights reserved.
PY - 2016
Y1 - 2016
N2 - Customer order fulfillment at distribution centers (DC) is increasingly necessitated by innovative strategies to maximize operational performance that are primarily driven by cost and service level under supply chain variability. In order to better understand the tradeoffs, in this paper, a generic computational model is developed to estimate forklift travel times for DCs with any arbitrary floor space and loading docks. In particular, travel times are modelled as random variables and the moments of the probability distribution of travel times are estimated and used as inputs to analytical queueing model and discrete event simulation model. Results show that the analytical and simulation models are within 3% under different demand scenarios. These models are used to determine the impact of work-force capacity on key performance measures such as Truck Processing Time (TPT) and Labor Hours Per Truck (LHPT). The workforce capacity for different demand scenarios is determined using three different approaches - Target Utilization Level, Square Root Staffing (SRS) rule (adapted from call center staffing) and Optimization. The result from these models indicate that adapting workforce capacity to match varying demand can reduce cost by 18% while maintaining desired service level.
AB - Customer order fulfillment at distribution centers (DC) is increasingly necessitated by innovative strategies to maximize operational performance that are primarily driven by cost and service level under supply chain variability. In order to better understand the tradeoffs, in this paper, a generic computational model is developed to estimate forklift travel times for DCs with any arbitrary floor space and loading docks. In particular, travel times are modelled as random variables and the moments of the probability distribution of travel times are estimated and used as inputs to analytical queueing model and discrete event simulation model. Results show that the analytical and simulation models are within 3% under different demand scenarios. These models are used to determine the impact of work-force capacity on key performance measures such as Truck Processing Time (TPT) and Labor Hours Per Truck (LHPT). The workforce capacity for different demand scenarios is determined using three different approaches - Target Utilization Level, Square Root Staffing (SRS) rule (adapted from call center staffing) and Optimization. The result from these models indicate that adapting workforce capacity to match varying demand can reduce cost by 18% while maintaining desired service level.
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U2 - 10.1007/978-3-319-51133-7_25
DO - 10.1007/978-3-319-51133-7_25
M3 - Conference contribution
AN - SCOPUS:85016007966
SN - 9783319511320
T3 - IFIP Advances in Information and Communication Technology
SP - 206
EP - 213
BT - Advances in Production Management Systems
A2 - Naas, Irenilza
A2 - Vendrametto, Oduvaldo
A2 - Reis, Joao Mendes
A2 - Goncalves, Rodrigo Franco
A2 - Silva, Marcia Terra
A2 - Kiritsis, Gregor
A2 - von Cieminski, Gregor
PB - Springer New York LLC
T2 - IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2016
Y2 - 3 September 2016 through 7 September 2016
ER -