case study; operations planning; order picking management; forecasting; workload balancing
Abstract :
[en] In order to differentiate from competitors in terms of customer service, warehouses accept late orders while providing delivery in a quick and timely way. This trend leads to a reduced time to pick an order. This paper introduces workload forecasting in a warehouse context, in particular a zone picking warehouse. Improved workforce planning can contribute to an effective and efficient order picking process. Most order picking publications treat demand as known in advance. As warehouses accept late orders, the assumption of a constant given demand is questioned in this paper. The objective of this study is to present time series forecasting models that perform well in a zone picking warehouse. A real-life case study demonstrates the value of applying time series forecasting models to forecast the daily number of order lines. The forecast of order lines, along with order pickers’ productivity, can be used by warehouse supervisors to determine the daily required number of order pickers, as well as the allocation of order pickers across warehouse zones. Time series are applied on an aggregated level, as well as on a disaggregated zone level. Both bottom-up and top-down approaches are evaluated in order to find the best-performing forecasting method.
Research Center/Unit :
LEMA : Local Environment Management & Analysis Lepur : Centre de Recherche sur la Ville, le Territoire et le Milieu rural - ULiège
Disciplines :
Special economic topics (health, labor, transportation...) Civil engineering
Author, co-author :
van Gils, Teun
Ramaekers, Katrien
Caris
Cools, Mario ; Université de Liège > Département ArGEnCo > Transports et mobilité
Language :
English
Title :
The use of time series forecasting in zone order picking systems to predict order pickers’ workload
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Bibliography
Athanasopoulos, George, Rob J., Hyndman, Haiyan, Song, and Doris C., Wu. 2011. “The Tourism Forecasting Competition.” International Journal of Forecasting 27 (3): 822–844.
Chase Jr, Charles W. 2013. Demand-driven Forecasting: A Structured Approach to Forecasting. New York: John Wiley & Sons.
Cools, Mario, Elke, Moons, and Geert, Wets. 2009. “Investigating the Variability in Daily Traffic Counts Through use of ARIMAX and SARIMAX Models: Assessing the Effect of Holidays on Two Site Locations.” Transportation Research Record: Journal of the Transportation Research Board 2136 (-1):57–66.
Davarzani, Hoda, and Andreas, Norrman. 2015. “Toward a Relevant Agenda for Warehousing Research: Literature Review and Practitioners’ Input.” Logistics Research 8 (1): 1–18.
De Gooijer, Jan G., and Rob J., Hyndman. 2006. “25 Years of Time Series Forecasting.” International Journal of Forecasting 22 (3): 443–473.
De Koster, René B. M., Tho, Le-Duc, and Kees Jan, Roodbergen. 2007. “Design and Control of Warehouse Order Picking: A Literature Review.” European Journal of Operational Research 182 (2): 481–501.
De Koster, René B. M., Tho, Le-Duc, and Nima, Zaerpour. 2012. “Determining the Number of Zones in a Pick-and-sort Order Picking System.” International Journal of Production Research 50 (3): 757–771.
Defraeye, Mieke, and Inneke, Van Nieuwenhuyse. 2016. “Staffing and Scheduling under Nonstationary Demand for Service: A Literature Review.” Omega 58: 4–25.
Gardner Jr., Everette S. 2006. “Exponential Smoothing: The State of the Art -- Part II.” International Journal of Forecasting 22 (4): 637–666.
Goh, Carey, and Rob, Law. 2002. “Modeling and Forecasting Tourism Demand for Arrivals with Stochastic Nonstationary Seasonality and Intervention.” Tourism Management 23 (5): 499–510.
Gu, Jinxiang, Marc, Goetschalckx, and Leon F., McGinnis. 2007. “Research on Warehouse Operation: A Comprehensive Review.” European Journal of Operational Research 177 (1): 1–21.
Gu, Jinxiang, Marc, Goetschalckx, and Leon F., McGinnis. 2010. “Research on Warehouse Design and Performance Evaluation: A Comprehensive Review.” European Journal of Operational Research 203 (3): 539–549.
House-Peters, Lily A., and Heejun, Chang. 2011. “Urban Water Demand Modeling: Review of Concepts, Methods, and Organizing Principles.” Water Resources Research 47 (5): W05401.
Hwang, H., and D. G., Kim. 2005. “Order-batching Heuristics based on Cluster Analysis in a Low-level Picker-to-part Warehousing System.” International Journal of Production Research 43 (17): 3657–3670.
Hyndman, Rob J., and Anne B., Koehler. 2006. “Another Look at Measures of Forecast Accuracy.” International Journal of Forecasting 22 (4): 679–688.
Jacobs, F. Robert, Richard B., Chase, and Richard, Chase. 2010. Operations and Supply Chain Management. New York: McGraw-Hill/Irwin.
Jane, Chin-Chia. 2000. “Storage Location Assignment in a Distribution Center.” International Journal of Physical Distribution & Logistics Management 30 (1): 55–71.
Jane, Chin-Chia, and Yih-Wenn, Laih. 2005. “A Clustering Algorithm for Item Assignment in a Synchronized Zone Order Picking System.” European Journal of Operational Research 166 (2): 489–496.
Koo, Pyung-Hoi. 2008. “The use of Bucket Brigades in Zone Order Picking Systems.” OR Spectrum 31 (4): 759–774.
Le-Duc, Tho, and René B.M., De Koster. 2005. Determining Number of Zones in a Pick-and-pack Orderpicking System. ERIM Report Series Research in Management, Technical Report ERS-2005-029-LIS.
Rouwenhorst, B., B., Reuter, V., Stockrahm, G. J., van Houtum, R. J., Mantel, and W. H. M., Zijm. 2000. “Warehouse Design and Control: Framework and Literature Review.” European Journal of Operational Research 122 (3): 515–533.
Ruben, Robert A., and F. Robert, Jacobs. 1999. “Batch Construction Heuristics and Storage Assignment Strategies for Walk/Ride and Pick Systems.” Management Science 45 (4): 575–596.
Sanders, Nada R., and Larry P., Ritzman. 2004. “Using Warehouse Workforce Flexibility to Offset Forecast Errors.” Journal of Business Logistics 25 (2): 251–269.
Schwarzkopf, Albert B., Richard J., Tersine, and John S., Morris. 1988. “Top-down versus Bottom-up Forecasting Strategies.” International Journal of Production Research 26 (11): 1833.
Song, Haiyan, and Gang, Li. 2008. “Tourism Demand Modelling and Forecasting- A Review of Recent Research.” Tourism Management 29 (2): 203–220.
Suganthi, L., and Anand A., Samuel. 2012. “Energy Models for Demand Forecasting- A Review.” Renewable and Sustainable Energy Reviews 16 (2): 1223–1240.
Van Gils, Teun, Katrien, Ramaekers, Kris, Braekers, and An, Caris. 2015. “Improving Operational Workforce Scheduling in a Warehouse using Time Series Forecasting.” Abstract from European conference for Operational Research 2015; Glasgow, United Kingdom.
Zotteri, Giulio, Matteo, Kalchschmidt, and Federico, Caniato. 2005. “The Impact of Aggregation Level on Forecasting Performance.” International Journal of Production Economics 93–94: 479–491.
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