Investigating the Demand for Short-shelf Life Food Products for SME Wholesalers
Accurate forecasting of fresh produce demand is one
the challenges faced by Small Medium Enterprise (SME)
wholesalers. This paper is an attempt to understand the cause for the
high level of variability such as weather, holidays etc., in demand of
SME wholesalers. Therefore, understanding the significance of
unidentified factors may improve the forecasting accuracy. This
paper presents the current literature on the factors used to predict
demand and the existing forecasting techniques of short shelf life
products. It then investigates a variety of internal and external
possible factors, some of which is not used by other researchers in the
demand prediction process. The results presented in this paper are
further analysed using a number of techniques to minimize noise in
the data. For the analysis past sales data (January 2009 to May 2014)
from a UK based SME wholesaler is used and the results presented
are limited to product ‘Milk’ focused on café’s in derby. The
correlation analysis is done to check the dependencies of variability
factor on the actual demand. Further PCA analysis is done to
understand the significance of factors identified using correlation.
The PCA results suggest that the cloud cover, weather summary and
temperature are the most significant factors that can be used in
forecasting the demand. The correlation of the above three factors
increased relative to monthly and becomes more stable compared to
the weekly and daily demand.
[1] G. L. Robertson, “Shelf Life of Packaged Foods, Its Measurement and
Prediction,” Developing New Food Products for Changing Market
Place, 2000.
[2] P. Meulstee, and M. Pechenizkiy, “Food Sales Prediction: "If Only It
Knew What We Know",” Data Mining Workshops, 2008. ICDMW '08.
IEEE International Conference on 2008, pp. 134-143.
[3] M. Shukla, and S. Jharkharia, “Agri-fresh Produce Supply Chain
Management: A State-of-the-art Literature Review.” International
Journal of Operations & Production Management: IJOPM,” The
Official Journal of the European Operations Management Association,
EUROMA, vol.33, no.2, 2014, pp. 114-158.
[4] S. A. Smith, S. H. Mcintyre and D. D. Achabal, “A Two-Stage Sales
Forecasting Procedure Using Discounted Least Squares,” Journal of
Marketing Research, vol.31, no.1, 1994.
[5] J. N. Jia, and Z. P. Liu, “Study on Demand Forecasting of Enterprise
Management Human Resources Based on MRA-OLS,” Management
and Service Science (MASS), International Conference, 2010, pp. 1-4.
[6] Y. Meng, F. R. Zhang, P. L. An M. L. Dong and Z. Y. Wang, “Industrial
land-use efficiency and planning in Shunyi, Beijing.” Landsc Urban
Plan, vol.85, 2008, pp. 40-48.
[7] X. H. Xu, and H. Zhang, “Forecasting Demand of Short Life Cycle
Products by SVM,” Management Science and Engineering, ICMSE
2008. 15th Annual Conference Proceedings, International Conference on
2008, pp. 352-356.
[8] B. Guo, and N. Han, “Competitiveness of Supply chain – Customer
Satisfaction,” International Conference on E-Business and EGovernment,
2010, pp. 3351-3353.
[9] W. Liu, J. Ji and Z. Wang, “Design of Service Supply Chains Based on
Service Products,” Industrial Engineering Journal, vol.11, no. 4, 2008,
pp. 60-65.
[10] P. Doganis, A. Alexandridis, P. Patrinos, and H. Sarimveis, “Time
Series Sales Forecasting for Short Shelf-life Food Products Based on
Artificial Neural Networks and Evolutionary Computing,” Journal of
Food Engineering, vol.75, no.2, 2006, pp. 196-204.
[11] L. Aburto, and R. Weber, “Improved supply chain management based
on hybrid demand forecasts,” Applied Soft Computing., vol. 7, no.1,
2007 pp. 136-144.
[12] J. K. Sivillo and D. P. Reilly, “Forecasting Consumer Product Demand
with Weather Information: A Case Study,” Journal of Business
Forecasting, Winter2004/2005, vol.23, no.4, December 2004, pp. 22.
[13] F. L. Chen, and T.Y. Ou, “Gray Relation Analysis and Multilayer
Functional Link Network Sales Forecasting Model for Perishable Food
in Convenience Store,” Expert Systems with Applications, vol.36, no.3,
part 2, 2009, pp. 7054-7063.
[14] G. E. P. Box, and G. M. Jenkins, “Time Series Analysis: Forecasting and
Control,” Prentice Hall PTR, Upper Saddle River, NJ, 1994.
[15] C. Chatfield, “The Holt-Winters Forecasting Procedure,” Applied
Statistics, vol.27, 1978, pp. 264-279.
[16] J. Shlens, “A tutorial on principal component analysis, Systems
Neurobiology Laboratory, “Salk Institute for Biological Studies La Jolla,
2005. (accessed April 2014)
[1] G. L. Robertson, “Shelf Life of Packaged Foods, Its Measurement and
Prediction,” Developing New Food Products for Changing Market
Place, 2000.
[2] P. Meulstee, and M. Pechenizkiy, “Food Sales Prediction: "If Only It
Knew What We Know",” Data Mining Workshops, 2008. ICDMW '08.
IEEE International Conference on 2008, pp. 134-143.
[3] M. Shukla, and S. Jharkharia, “Agri-fresh Produce Supply Chain
Management: A State-of-the-art Literature Review.” International
Journal of Operations & Production Management: IJOPM,” The
Official Journal of the European Operations Management Association,
EUROMA, vol.33, no.2, 2014, pp. 114-158.
[4] S. A. Smith, S. H. Mcintyre and D. D. Achabal, “A Two-Stage Sales
Forecasting Procedure Using Discounted Least Squares,” Journal of
Marketing Research, vol.31, no.1, 1994.
[5] J. N. Jia, and Z. P. Liu, “Study on Demand Forecasting of Enterprise
Management Human Resources Based on MRA-OLS,” Management
and Service Science (MASS), International Conference, 2010, pp. 1-4.
[6] Y. Meng, F. R. Zhang, P. L. An M. L. Dong and Z. Y. Wang, “Industrial
land-use efficiency and planning in Shunyi, Beijing.” Landsc Urban
Plan, vol.85, 2008, pp. 40-48.
[7] X. H. Xu, and H. Zhang, “Forecasting Demand of Short Life Cycle
Products by SVM,” Management Science and Engineering, ICMSE
2008. 15th Annual Conference Proceedings, International Conference on
2008, pp. 352-356.
[8] B. Guo, and N. Han, “Competitiveness of Supply chain – Customer
Satisfaction,” International Conference on E-Business and EGovernment,
2010, pp. 3351-3353.
[9] W. Liu, J. Ji and Z. Wang, “Design of Service Supply Chains Based on
Service Products,” Industrial Engineering Journal, vol.11, no. 4, 2008,
pp. 60-65.
[10] P. Doganis, A. Alexandridis, P. Patrinos, and H. Sarimveis, “Time
Series Sales Forecasting for Short Shelf-life Food Products Based on
Artificial Neural Networks and Evolutionary Computing,” Journal of
Food Engineering, vol.75, no.2, 2006, pp. 196-204.
[11] L. Aburto, and R. Weber, “Improved supply chain management based
on hybrid demand forecasts,” Applied Soft Computing., vol. 7, no.1,
2007 pp. 136-144.
[12] J. K. Sivillo and D. P. Reilly, “Forecasting Consumer Product Demand
with Weather Information: A Case Study,” Journal of Business
Forecasting, Winter2004/2005, vol.23, no.4, December 2004, pp. 22.
[13] F. L. Chen, and T.Y. Ou, “Gray Relation Analysis and Multilayer
Functional Link Network Sales Forecasting Model for Perishable Food
in Convenience Store,” Expert Systems with Applications, vol.36, no.3,
part 2, 2009, pp. 7054-7063.
[14] G. E. P. Box, and G. M. Jenkins, “Time Series Analysis: Forecasting and
Control,” Prentice Hall PTR, Upper Saddle River, NJ, 1994.
[15] C. Chatfield, “The Holt-Winters Forecasting Procedure,” Applied
Statistics, vol.27, 1978, pp. 264-279.
[16] J. Shlens, “A tutorial on principal component analysis, Systems
Neurobiology Laboratory, “Salk Institute for Biological Studies La Jolla,
2005. (accessed April 2014)
@article{"International Journal of Business, Human and Social Sciences:70531", author = "Yamini Raju and Parminder S. Kang and Adam Moroz and Ross Clement and Ashley Hopwell and Alistair Duffy", title = "Investigating the Demand for Short-shelf Life Food Products for SME Wholesalers", abstract = "Accurate forecasting of fresh produce demand is one
the challenges faced by Small Medium Enterprise (SME)
wholesalers. This paper is an attempt to understand the cause for the
high level of variability such as weather, holidays etc., in demand of
SME wholesalers. Therefore, understanding the significance of
unidentified factors may improve the forecasting accuracy. This
paper presents the current literature on the factors used to predict
demand and the existing forecasting techniques of short shelf life
products. It then investigates a variety of internal and external
possible factors, some of which is not used by other researchers in the
demand prediction process. The results presented in this paper are
further analysed using a number of techniques to minimize noise in
the data. For the analysis past sales data (January 2009 to May 2014)
from a UK based SME wholesaler is used and the results presented
are limited to product ‘Milk’ focused on café’s in derby. The
correlation analysis is done to check the dependencies of variability
factor on the actual demand. Further PCA analysis is done to
understand the significance of factors identified using correlation.
The PCA results suggest that the cloud cover, weather summary and
temperature are the most significant factors that can be used in
forecasting the demand. The correlation of the above three factors
increased relative to monthly and becomes more stable compared to
the weekly and daily demand.", keywords = "Demand Forecasting, Deteriorating Products, Food
Wholesalers, Principal Component Analysis and Variability Factors.", volume = "9", number = "6", pages = "2051-5", }