Skip to main content
SpringerLink
Log in

Improving Food Supply Chain using Hybrid Semiparametric Regression Model

  • Chapter
  • First Online:
Supply Management Research

Part of the book series: Advanced Studies in Supply Management ((ASSM))

  • 4368 Accesses

Zusammenfassung

A demand variability in food retail sector affects production, ordering, and purchasing decisions in the entire upstream food supply chain, which in turn result in food waste and stock-outs. The volatility in demand for perishable fresh foods mainly occurs due to the demand influencing factors such as seasonality, temporary price reductions, holidays, and festivals. In particular, own- and cross-price deal effects between products are some of the important causes of bullwhip effect in the food supply chain. Therefore, it is necessary to develop a forecasting model which considers all the demand influencing factors in a proper way to improve the forecast accuracy. The main objectives of this study is (i) to improve the standard semiparametric regression (SR) model into a hybrid auto regressive integrated moving average – semi parametric regression (ARIMA-SR) model and (ii) to assess the price deal effects. For the purpose of investigation, the daily sales data of perishable fresh foods from a retail store in Germany is used. From the obtained results, it has been identified that the ARIMA-SR model has high adjusted R2 and low forecast error, when compare to the existing traditional models.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 44.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 59.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Literatur

  • Abraham, M.M., Lodish, L.M., 1987. Promoter: An Automated Promotion Evaluation System. Marketing Science 6, 101–123.

    Google Scholar 

  • Aburto, L., Weber, R., 2003. Demand forecast in a supermarket using a hybrid intelligent system. Design and application of hybrid intelligent systems.

    Google Scholar 

  • Aburto, L., Weber, R., 2007. Improved supply chain management based on hybrid demand forecasts. Applied Soft Computing 7, 136–144.

    Google Scholar 

  • Agnew, M., Thornes, J., 1995. The weather sensitivity of the UK food retail and distribution industry. Meteorological Applications 2, 137–147.

    Google Scholar 

  • Ali, Ö.G., Sayın, S., van Woensel, T., Fransoo, J., 2009. SKU demand forecasting in the presence of promotions. Expert Systems with Applications 36, 12340–12348.

    Google Scholar 

  • Arunraj, N.S., Ahrens, D., Fernandes, M., Müller, M., 2014. Time Series Sales Forecasting In Food Retail Industry. In: The 34th International Symposium on Forecasting (ISF 2014). Rotterdam, The Netherlands.

    Google Scholar 

  • Babu, C.N., Reddy, B.E., 2014. A moving-average filter based hybrid ARIMA–ANN model for forecasting time series data. Applied Soft Computing 23, 27–38.

    Google Scholar 

  • Blattberg, R.C., Briesch, R., Fox, E.J., 1995. How Promotions Work. Marketing Science 14, G122–G132.

    Google Scholar 

  • Blattberg, R.C., Wisniewski, K.J., 1989. Price-Induced Patterns of Competition. Marketing Science 8, 291–309.

    Google Scholar 

  • Briesch, R.A., Krishnamurthi, L., Mazumdar, T., Raj, S.P., 1997. A Comparative Analysis of Reference Price Models. Journal of Consumer Research 24, 202–214.

    Google Scholar 

  • Cools, M., Moons, E., Wets, G., 2009. Investigating variablility in daily traffic counts using ARIMAX and SARIMA (X) models: assessing impact of holidays on two divergent site locations. Transportation Research Record: Journal of the Transportation Research Board 2136, 57–66.

    Google Scholar 

  • Eriksson, M., 2012. Retail Food Wastage a Case Study Approach to Quantities and Causes. Swedish University of Agricultural Sciences, Uppsala.

    Google Scholar 

  • Foekens, E.W., Leeflang, P.S.H., Wittink, D.R., 1994. A comparison and an exploration of the forecasting accuracy of a loglinear model at different levels of aggregation. International Journal of Forecasting 10, 245–261.

    Google Scholar 

  • Foekens, E.W., S.H. Leeflang, P., Wittink, D.R., 1998. Varying parameter models to accommodate dynamic promotion effects. Journal of Econometrics 89, 249–268.

    Google Scholar 

  • Gilliland, M., 2011. Business Forecasting Effectiveness. Analytics 21–25.

    Google Scholar 

  • Gilliland, M., Sglavo, U., 2010. Worst Practices in Business Forecasting. Analytics 12–17.

    Google Scholar 

  • Gooch, M., Felfel, A., Marenick, N., 2010. Food Waste in Canada. Value Chain Management Centre.

    Google Scholar 

  • Gupta, S., Cooper, L.G., 1992. The Discounting of Discounts and Promotion Thresholds. Journal of Consumer Research.

    Google Scholar 

  • Hanssens, D.M., Leeflang, P.S.H., Wittink, D.R., 2005. Market response models and marketing practice. Applied Stochastic Models in Business and Industry 21, 423–434.

    Google Scholar 

  • Hyndman, R.J., 2010. The ARIMAX model muddle [WWW Document]. URL http://robjhyndman.com/hyndsight/arimax/ (accessed 7.22.15).

  • Hyndman, R.J., Athana-sopou-los, G., 2013. Forecasting: principles and practice. OTexts.

    Google Scholar 

  • Hyndman, R.J., Athanasopoulos, G., Razbash, S., Schmidt, D., Zhou, Z., Khan, Y., Bergmeir, C., Wang, E., 2014. R Package: forecast.

    Google Scholar 

  • Kongcharoen, C., Kruangpradit, T., 2013. Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX) Model for Thailand Export. In: 33rd International Symposium on Forecasting, South Korea. pp. 1–8.

    Google Scholar 

  • Kranert, M., Hafner, G., Barabosz, J., Schneider, F., Lebersorger, S., Scherhaufer, S., Schuller, H., Leverenz, D., 2012. Determination of discarded food and proposals for a minimization of food wastage in Germany, University Stuttgart. Stuttgart.

    Google Scholar 

  • Lee, M., Hamzah, N., 2010. Calendar variation model based on ARIMAX for forecasting sales data with Ramadhan effect. In: Proceedings of the Regional Conference on Statistical Sciences 2010 (RCSS’10). Kota bharu, Malaysia, pp. 349–361.

    Google Scholar 

  • Leeflang, P.S.H., Wittink, D.R., 2000. Building models for marketing decisions: Past , present and future. International Journal of Research in Marketing 17, 105–126.

    Google Scholar 

  • Liu, L.-M., Bhattacharyya, S., Sclove, S.L., Chen, R., Lattyak, W.J., 2001. Data mining on time series: an illustration using fast-food restaurant franchise data. Computational Statistics & Data Analysis 37, 455–476.

    Google Scholar 

  • Lomax, W., Hammond, K., East, R., Clemente, M., 1997. The measurement of cannibalization. Journal of Product & Brand Management 6, 27–39.

    Google Scholar 

  • Luxhøj, J.T., Riis, J.O., Stensballe, B., 1996. A hybrid econometric—neural network modeling approach for sales forecasting. International Journal of Production Economics 43, 175–192.

    Google Scholar 

  • Martínez-Ruiz, M.P., Mollá-Descals, A., Gómez-Borja, M.A., Rojo-Álvarez, J.L., 2006a. Using daily store-level data to understand price promotion effects in a semiparametric regression model. Journal of Retailing and Consumer Services 13, 193–204.

    Google Scholar 

  • Martínez-Ruiz, M.P., Mollá-Descals, A., Gómez-Borja, M.A., Rojo-Álvarez, J.L., 2006b. Evaluating temporary retail price discounts using semiparametric regression. Journal of Product & Brand Management 15, 73–80.

    Google Scholar 

  • Martínez-Ruiz, M.P., Rojo-Álvarez, J.L., Gimeno-Blanes, F.J., 2011. Evaluation of Promotional and Cross-Promotional Effects Using Support Vector Machine Semiparametric Regression. Systems Engineering Procedia 1, 465–472.

    Google Scholar 

  • Mazumdar, T., Raj, S.P., Sinha, I., 2005. Reference Price Research: Review and Propositions. Journal of Marketing 69, 84–102.

    Google Scholar 

  • Mena, C., Adenso-Diaz, B., Yurt, O., 2011. The causes of food waste in the supplier–retailer interface: Evidences from the UK and Spain. Resources, Conservation and Recycling 55, 648–658.

    Google Scholar 

  • Mena, C., Terry, L. a., Williams, A., Ellram, L., 2014. Causes of waste across multi-tier supply networks: Cases in the UK food sector. International Journal of Production Economics 152, 144–158.

    Google Scholar 

  • Pektaş, A.O., Kerem Cigizoglu, H., 2013. ANN hybrid model versus ARIMA and ARIMAX models of runoff coefficient. Journal of Hydrology 500, 21–36.

    Google Scholar 

  • Peter, Ď., Silvia, P., 2012. ARIMA vs . ARIMAX – which approach is better to analyze and forecast macroeconomic time series ? In: Proceedings of 30th International Conference Mathematical Methods in Economics. Karviná, Czech Republic, pp. 136–140.

    Google Scholar 

  • Peters, J., 2012. Improving the promotional forecasting accuracy for perishable items at Sligro Food Group B . V . Eindhoven University of Technology, Netherlands.

    Google Scholar 

  • Quested, T., Johnson, H., 2009. Household Food and Drink Waste in the UK. Banbury, UK.

    Google Scholar 

  • Schröder, K.J., 2012. Cannibalization on the German yoghurt market. Christian-Albrechts-Universität zu Kiel.

    Google Scholar 

  • Sethuraman, R., 1996. A model of how discounting high-priced brands affects the sales of low-priced brands. Journal of Marketing Research 33, 399–409.

    Google Scholar 

  • Shukla, M., Jharkharia, S., 2013. Applicability of ARIMA models in wholesale vegetable market: An investigation. International Journal of Information Systems and Supply Chain Management 6, 105–119.

    Google Scholar 

  • Silva-Risso, J.M., Bucklin, R.E., Morrison, D.G., 1999. A Decision Support System for Planning Manufacturers’ Sales Promotion Calendars. Marketing Science 18, 274–300.

    Google Scholar 

  • Srinivasan, S.R., Ramakrishnan, S., Grasman, S.E., 2005a. Incorporating cannibalization models into demand forecasting. Marketing Intelligence & Planning 23, 470–485.

    Google Scholar 

  • Srinivasan, S.R., Ramakrishnan, S., Grasman, S.E., 2005b. Identifying the effects of cannibalization on the product portfolio. Marketing Intelligence & Planning 23, 359–371.

    Google Scholar 

  • Stenmarck, Å., Hanssen, O.J., Silvennoinen, K., Katajajuuri, J.-M., Werge, M., 2011. Initiatives on prevention of food waste in the retail and wholesale trades. Stockholm.

    Google Scholar 

  • Thesis, 1995. Modeling the Impact of Sales Promotion on Store Profits. Stockholm School of Economics.

    Google Scholar 

  • van Donselaar, K., van Woensel, T., Broekmeulen, R., Fransoo, J., 2006. Inventory control of perishables in supermarkets. International Journal of Production Economics 104, 462–472.

    Google Scholar 

  • van Heerde, H.J., Leeflang, P.S.H., Wittink, D.R., 2001. Semiparametric Analysis to Estimate the Deal Effect Curve. Journal of Marketing Research 38, 197–215.

    Google Scholar 

  • van Heerde, H.J., Leeflang, P.S.H., Wittink, D.R., 2002. How Promotions Work: SCAN*PRO-Based Evolutionary Model Building. Schmalenbach Business Review 54, 198–220.

    Google Scholar 

  • van Heerde, H.J., Leeflang, P.S.H., Wittink, D.R., 2004. Decomposing the Sales Promotion Bump with Store Data. Marketing Science 23, 317–334.

    Google Scholar 

  • Yuan, Y., Capps Jr., O., Nayga Jr., R.M., 2009. Assessing the Demand for a Functional Food Product: Is There Cannibalization in the Orange Juice Category? Agricultural and Resource Economics Review 38, 153–165.

    Google Scholar 

  • Žliobaitė, I., Bakker, J., Pechenizkiy, M., 2012. Beating the baseline prediction in food sales: How intelligent an intelligent predictor is? Expert Systems with Applications 39, 806–815.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Fakultät für Angewandte Wirtschaftswissenschaften (School of Management), THD - Technische Hochschule Deggendorf, Edlmairstraße 6-8, 94469, Deggendorf, Deutschland

    Dr. Nari Sivanandam Arunraj & Prof. Dr. Diane Ahrens

Authors
  1. Dr. Nari Sivanandam Arunraj
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Prof. Dr. Diane Ahrens
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nari Sivanandam Arunraj .

Editor information

Editors and Affiliations

  1. Fakultät Wirtschaftswissenschaften, Universität Würzburg Fakultät, Würzburg, Germany

    Ronald Bogaschewsky

  2. Fakultät für WOW, Univ der Bundeswehr München , Neubiberg, Germany

    Michael Eßig

  3. Fakultät Wirtschaftswissenschaften, Technische Universität Dresden , Dresden, Germany

    Rainer Lasch

  4. Universität St. Gallen, St. Gallen, Switzerland

    Wolfgang Stölzle

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Fachmedien Wiesbaden

About this chapter

Cite this chapter

Arunraj, N.S., Ahrens, D. (2017). Improving Food Supply Chain using Hybrid Semiparametric Regression Model. In: Bogaschewsky, R., Eßig, M., Lasch, R., Stölzle, W. (eds) Supply Management Research. Advanced Studies in Supply Management. Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-15280-2_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-658-15280-2_10

  • Published:

  • Publisher Name: Springer Gabler, Wiesbaden

  • Print ISBN: 978-3-658-15279-6

  • Online ISBN: 978-3-658-15280-2

  • eBook Packages: Business and Economics (German Language)

Publish with us

Policies and ethics

Search

Navigation