Access the full text.
Sign up today, get DeepDyve free for 14 days.
R. Koster, Tho Le-Duc, K. Roodbergen (2006)
Design and control of warehouse order picking: A literature reviewEur. J. Oper. Res., 182
P. Goodwin (2002)
Integrating management judgment and statistical methods to improve short-term forecastsOmega-international Journal of Management Science, 30
P. Goodwin (1996)
Statistical correction of judgmental point forecasts and decisionsOmega-international Journal of Management Science, 24
(2012)
Labor management systems: The (very near) future of LMS
Jinxiang Gu, M. Goetschalckx, L. McGinnis (2010)
Research on warehouse design and performance evaluation: A comprehensive reviewEur. J. Oper. Res., 203
M. Riley, A. Lockwood (1997)
Strategies and measurement for workforce flexibility: an application of functional flexibility in a service settingInternational Journal of Operations & Production Management, 17
J. Wacker, R. Lummus (2002)
Sales forecasting for strategic resource planningInternational Journal of Operations & Production Management, 22
(1995)
Dynamic Econometrics
C. Jarque, Anil Bera (1987)
A test for normality of observations and regression residualsInternational Statistical Review, 55
N. Sanders, G. Graman (2009)
Quantifying costs of forecast errors: A case study of the warehouse environment☆Omega-international Journal of Management Science, 37
L. Ritzman, B. King (1993)
The relative significance of forecast errors in multistage manufacturingJournal of Operations Management, 11
D. Bowersox, D. Closs, M. Cooper (2002)
Supply Chain Logistics Management
(1994)
Time Series Analysis: Forecasting and Control (3rd ed)
Teun Gils, K. Ramaekers, A. Caris, M. Cools (2017)
The use of time series forecasting in zone order picking systems to predict order pickers’ workloadInternational Journal of Production Research, 55
N. Sanders, L. Ritzman (1991)
On Knowing When to Switch from Quantitative to Judgemental ForecastsInternational Journal of Operations & Production Management, 11
D. Mare (2015)
The Oxford Handbook of Economic ForecastingJournal of the Operational Research Society, 66
L. Godfrey (1978)
TESTING AGAINST GENERAL AUTOREGRESSIVE AND MOVING AVERAGE ERROR MODELS WHEN THE REGRESSORS INCLUDE LAGGED DEPENDENT VARIABLESEconometrica, 46
Nadia Sanders, K. Manrodt (2003)
The efficacy of using judgmental versus quantitative forecasting methods in practiceOmega-international Journal of Management Science, 31
(2016)
Warehouse & Distribution Science: Release 0.97, The Supply Chain and Logistics Institute, Georgia Institute
The column
N. Sanders, L. Ritzman (2004)
Integrating judgmental and quantitative forecasts: methodologies for pooling marketing and operations informationInternational Journal of Operations & Production Management, 24
Gang Wang, A. Gunasekaran, E. Ngai, T. Papadopoulos (2016)
Big data analytics in logistics and supply chain management: Certain investigations for research and applicationsInternational Journal of Production Economics, 176
A. Timmermann (2005)
Forecast CombinationsCEPR Discussion Paper Series
T. Breusch (1978)
TESTING FOR AUTOCORRELATION IN DYNAMIC LINEAR MODELS, 17
Y. Gong, R. Koster (2011)
A review on stochastic models and analysis of warehouse operationsLogistics Research, 3
M. Brusco, T. Johns (1995)
The effect of demand characteristics on labour scheduling methodsInternational Journal of Operations & Production Management, 15
Sander Leeuw, V. Wiers (2014)
Warehouse manpower planning strategies in times of financial crisis: evidence from logistics service providers and retailers in the NetherlandsProduction Planning & Control, 26
(2007)
Integral Warehouse Management
R. Ruben, F. Jacobs (1999)
Batch Construction Heuristics and Storage Assignment Strategies for Walk/Rideand Pick SystemsManagement Science, 45
M. Waller, S. Fawcett (2013)
Click Here for a Data Scientist: Big Data, Predictive Analytics, and Theory Development in the Era of a Maker Movement Supply ChainJournal of Business Logistics, 34
The purpose of this paper is to show that intentional demand forecast bias can improve warehouse capacity planning and labour efficiency. It presents an empirical methodology to detect and implement forecast bias.Design/methodology/approachA forecast model integrates historical demand information and expert forecasts to support active bias management. A non-linear relationship between labour productivity and forecast bias is employed to optimise efficiency. The business analytic methods are illustrated by a case study in a consumer electronics warehouse, supplemented by a survey among 30 warehouses.FindingsResults indicate that warehouse management systematically over-forecasts order sizes. The case study shows that optimal bias for picking and loading is 30-70 per cent with efficiency gains of 5-10 per cent, whereas the labour-intensive packing stage does not benefit from bias. The survey results confirm productivity effects of forecast bias.Research limitations/implicationsWarehouse managers can apply the methodology in their own situation if they systematically register demand forecasts, actual order sizes and labour productivity per warehouse stage. Application is illustrated for a single warehouse, and studies for alternative product categories and labour processes are of interest.Practical implicationsIntentional forecast bias can lead to smoother workflows in warehouses and thus result in higher labour efficiency. Required data include historical data on demand forecasts, order sizes and labour productivity. Implementation depends on labour hiring strategies and cost structures.Originality/valueOperational data support evidence-based warehouse labour management. The case study validates earlier conceptual studies based on artificial data.
International Journal of Physical Distribution & Logistics Management – Emerald Publishing
Published: Feb 22, 2018
Keywords: Demand forecasting; Labour efficiency; Forecast bias; Labour management; Warehouse planning
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.