Economic Development, Technological Change, and Growth
Abstract
This study examines the application of structured forecasting methods to determine accurate
demand forecasts using 12 monthly sales figures of a moderate busy pharmacy. The date were analysed
using some forecasting techniques; Moving Average Method, Exponential Smoothing Method and
Least Square Method. Also, the performances of the forecasting methods were evaluated using some
accuracy measures such as Mean Absolute Deviation (MAD), Mean Square Error (MSE) and Mean
Absolute Percentage Error (MAPE) to. The findings reveal that exponential smoothing method which
results to least forecast error is the best method. Hence, the pharmacy is advised to adopt this best
forecasting method to determine its monthly demand forecasts. Pharmacy operators should maintain
sound sales and inventory records; it is easier if the system can be computerized but it could be
expensive to operate for small pharmacy outlet.
References
Adebanjo, D. & Robin, M. (2000). Identifying the problems in forecasting consumer demand in the fast
moving consumer goods sector. Benchmarking: An international journal, 7(3), pp. 223-230.
Adedayo, A.O.; Ojo, O. & Obamiro, J.K. (2006) Operations research in decision analysis and
production management. Lagos: Pumark Nigeria Limited.
Armstrong, J.S. & Collopy, F. (1992). Error measures for generalizing about forecasting methods:
Empirical comparisons. International journal of forecasting, 8(1), pp. 69-80.
Armstrong, J.S. (2001). Principles of Forecasting: A Handbook for researchers and practitioners.
Boston: Kluwer.
Armstrong, S.J. & Brodie, J.R. (1999). Quantitative methods in marketing. 2nd Edition. London:
International Thompson Business Press.
Brockwell, P.J. & Davis, R.A. (1996). Introduction to time series and forecasting. Springer.
Cássia, R.; Da Veiga; Da Veiga, P.C & Duclós, C.L. (2015). The accuracy of demand forecast models
as a critical factor in the financial performance of the food industry.
Chao, C.; Jamie, T. & Jonathan M.G. (2017). A new accuracy measure based on bounded relative error
for time series forecasting.
Chindia, E.W.; Wainaina, G.; F.N. Kibera, N.F. & G.P. Pokhariyal, P.G. (2014). Forecasting
techniques, operating environment and accuracy of performance forecasting for large manufacturing
Firms in Kenya. International Journal of Managerial Studies and Research, 2(7), pp. 83-100.
Danese, P. & Kalchschmidt, M. (2011). The role of the forecasting process in improving forecast
accuracy and operational performance. International journal of production economics, 131(1), pp. 204-
214.
Doganis, P.; Alexandridis, A.; Patrinos, P. & Sarimveis, H. (2006). Time series sales forecasting for
short shelf-life food products based on artificial neural networks and evolutionary computing. Journal
of Food Engineering, 75(2), pp. 196–204.
Gene R. & George W. (1999). The Delphi technique as a forecasting tool: issues and analysis.
International Journal of Forecasting, 15, pp. 353–375.
Gupta, A.; Maranas, C.D. & McDonald, C.M. (2000). Mid-term supply chain planning under demand
uncertainty: customer demand satisfaction and inventory management. Computers and Chemical
Engineering, 24(12), pp. 2613–2621.
Gupta, M. & Minai, H.M. (2018). An Empirical Analysis of Forecast Performance of GDP Growth in
India. Global Business Review.
Hibon, M. & Evgeniou, T. (2005).To combine or not to combine: selecting among forecasts and their
combinations. International Journal of Forecasting, 21, pp. 15–24.
Hyndman, R.J. & Koehler, A.B. (2006). Another look at measures of forecast accuracy. International
Journal of Forecasting, 22(4), pp. 679-688.
Kakhischmidt, M.; Zotter, C. & Vergenti, R. (2005). Inventory management in a multi-echelon spart
parts supply chain. International Journal of Production Economics, 81/82, pp. 165-181.
Kalchschmidt, M. (-). Demand forecasting practices and performance: evidence from the gmrg
database, Department of economics and technology management, Università degli Studi di Bergamo,
Viale Marconi 5, pp. 24044, Dalmine (BG) Italy.
Kumar, R. & Dalgobind, M. (2014). Application of Proper Forecasting Technique in Juice Production:
A Case Study. Global Journal of Researches in Engineering, 13(4), pp. 42-47
Macgregor, D.G. (2001). Decomposition in judgmental forecasting and estimation. Norwell, MA:
Kluwer Academic Publishers.
Mahmoud, E. (2006). Accuracy in forecasting: A survey. Journal of Forecasting, 3(2), pp. 139-159.
Makridakis, S. & Hibon, M. (2000). The M-3 competition: results, conclusions and implications,
International Journal of Forecasting, 16, pp. 451-476.
Makridakis, S. (1993). Accuracy measures: theoretical and practical concerns. International Journal of
Forecasting, 9(4), pp. 527-529.
Markridas, S.; Wheelwright, S.C. & Hyndman, R.J. (1998). Forecasting- Methods and Applications.
New York: John Wiley and sons.
Mathai, V.A.; Agarwal, A.; Angamoalli, V.; Narayanan, S. & Dhakshayani, E. (2016). Development
of new methods for measuring forecast error. International Journal of Logistics Systems and
Management, 24(2), pp. 213–225.
Matsumoto, M. & Ikeda, A. (2015). Examination of demand forecasting by time series analysis for auto
parts remanufacturing. Journal of Remanufacturing, 5(1), pp. 10 -19.
Michael, L.; Paul, G.; Marcus, O.C. & Dilek, O. (2006). Judgmental forecasting: A review of progress
over the last 25 years. International Journal of Forecasting, 22(3), pp. 493-518.
Monahan, M.K. (2014). Aircraft demand forecasting, Masters Theses - current dissertations and theses.
A Master thesis submitted to Department of Industrial Engineering and Operations Research, University
of Massachusetts Amherst.
Moon, M.A.; Mentzer, J.T. & Smith, C.D. (2003). Conducting a sales forecasting audit. International
Journal of Forecasting, 19, pp. 5–25.
Mukttash, A. & Samhoun, M. (2011). Supply planning improvement: a causal forecasting approach.
Journal of Applied Sciences, 11(12), pp. 2207-2214.
Nijat, M.; Peter, F. & Peter, L. (2016). Evaluating forecasting methods by considering different
accuracy measure. Procedia Computer Science, 95, pp. 264-271.
Parzen, E. & Winkler, R (1982). The accuracy of extrapolation (time series) methods: Results of a
forecasting competition. Journal of Forecasting, 1, pp. 111–153.
Paul, G. (2014). Using naïve forecasts to assess limits to forecast accuracy and the quality of fit of
forecasts to time series data. Working Paper, University of Bath.
Pisal, Y.; Anulark, P. & Amnaj, C. (2001). Demand Forecasting and Production Planning for Highly
Seasonal Demand Situations: Case Study of a Pressure Container Factory. Science Asia, 27, pp. 271-
278.
Powers, D.M. (2011). Evaluation: from precision, recall and F-m easure to ROC, informedness,
markedness and correlation.
Pradeep, K.S. & Rajesh, K. (2014). The Evaluation of Forecasting Methods for Sales of Sterilized
Flavoured Milk in Chhattisgarh. International Journal of Engineering Trends and Technology, 8(2),
pp. 98-112.
Rakesh, K. & Dalgobind, M. (2013). Application of proper forecasting technique in Juice production:
A case study. Global Journal of Researches in Engineering. Industrial Engineering, 13(4), pp. 1-6.
Satya, P.; Ramasubramanium V. & S.C. Mehta, S.C. (2007). Statistical models for milk production in
India. Journal of Indian Society of Agriculture and Statistical, 61(2), pp. 80-83.
Sokolova, M. & Lapalme, G.A (2009). Systematic analysis is of performance measures forclassification
tasks. Information Processing & Management, 45(4), pp. 427-437.
Spedding, T.A. & Chan, K.K. (2000). Forecasting demand and inventory management using Bayesian
time series. Integrated Manufacturing Systems, 11(5), pp. 331-339.
Terui, N. & Van Dijk, H.K. (2002).Combined forecasts from linear and nonlinear time series models.
International Journal of Forecasting, 18 (3), pp. 421–438.
Van Sommeren, F.A.H. (2011). Improving forecast accuracy- Improving the baseline forecast for
Cheese products by use of statistical forecasting. A master thesis submitted to supply chain Department
of Friesland Campina Cheese, University of Twente.
Wacker, G.J. & Lummus, R.R. (2002). Sales forecasting for strategic resource planning. International
Journal of Operations & Production Management, 22(9), pp. 1014-1031.
Xu, B. & Ouenniche, J. (2012). Performance evaluation of competing forecasting models: A
multidimensional framework based on MCDA. Expert Systems with Applications, 39(9), pp. 8312-
8324.
Zhao, X.; Xie, J. & Leung, J. (2002). The impact of forecasting model selection on the value of
information sharing in a supply chain. European Journal of Operational Research, 142(2), pp. 321-
344.
moving consumer goods sector. Benchmarking: An international journal, 7(3), pp. 223-230.
Adedayo, A.O.; Ojo, O. & Obamiro, J.K. (2006) Operations research in decision analysis and
production management. Lagos: Pumark Nigeria Limited.
Armstrong, J.S. & Collopy, F. (1992). Error measures for generalizing about forecasting methods:
Empirical comparisons. International journal of forecasting, 8(1), pp. 69-80.
Armstrong, J.S. (2001). Principles of Forecasting: A Handbook for researchers and practitioners.
Boston: Kluwer.
Armstrong, S.J. & Brodie, J.R. (1999). Quantitative methods in marketing. 2nd Edition. London:
International Thompson Business Press.
Brockwell, P.J. & Davis, R.A. (1996). Introduction to time series and forecasting. Springer.
Cássia, R.; Da Veiga; Da Veiga, P.C & Duclós, C.L. (2015). The accuracy of demand forecast models
as a critical factor in the financial performance of the food industry.
Chao, C.; Jamie, T. & Jonathan M.G. (2017). A new accuracy measure based on bounded relative error
for time series forecasting.
Chindia, E.W.; Wainaina, G.; F.N. Kibera, N.F. & G.P. Pokhariyal, P.G. (2014). Forecasting
techniques, operating environment and accuracy of performance forecasting for large manufacturing
Firms in Kenya. International Journal of Managerial Studies and Research, 2(7), pp. 83-100.
Danese, P. & Kalchschmidt, M. (2011). The role of the forecasting process in improving forecast
accuracy and operational performance. International journal of production economics, 131(1), pp. 204-
214.
Doganis, P.; Alexandridis, A.; Patrinos, P. & Sarimveis, H. (2006). Time series sales forecasting for
short shelf-life food products based on artificial neural networks and evolutionary computing. Journal
of Food Engineering, 75(2), pp. 196–204.
Gene R. & George W. (1999). The Delphi technique as a forecasting tool: issues and analysis.
International Journal of Forecasting, 15, pp. 353–375.
Gupta, A.; Maranas, C.D. & McDonald, C.M. (2000). Mid-term supply chain planning under demand
uncertainty: customer demand satisfaction and inventory management. Computers and Chemical
Engineering, 24(12), pp. 2613–2621.
Gupta, M. & Minai, H.M. (2018). An Empirical Analysis of Forecast Performance of GDP Growth in
India. Global Business Review.
Hibon, M. & Evgeniou, T. (2005).To combine or not to combine: selecting among forecasts and their
combinations. International Journal of Forecasting, 21, pp. 15–24.
Hyndman, R.J. & Koehler, A.B. (2006). Another look at measures of forecast accuracy. International
Journal of Forecasting, 22(4), pp. 679-688.
Kakhischmidt, M.; Zotter, C. & Vergenti, R. (2005). Inventory management in a multi-echelon spart
parts supply chain. International Journal of Production Economics, 81/82, pp. 165-181.
Kalchschmidt, M. (-). Demand forecasting practices and performance: evidence from the gmrg
database, Department of economics and technology management, Università degli Studi di Bergamo,
Viale Marconi 5, pp. 24044, Dalmine (BG) Italy.
Kumar, R. & Dalgobind, M. (2014). Application of Proper Forecasting Technique in Juice Production:
A Case Study. Global Journal of Researches in Engineering, 13(4), pp. 42-47
Macgregor, D.G. (2001). Decomposition in judgmental forecasting and estimation. Norwell, MA:
Kluwer Academic Publishers.
Mahmoud, E. (2006). Accuracy in forecasting: A survey. Journal of Forecasting, 3(2), pp. 139-159.
Makridakis, S. & Hibon, M. (2000). The M-3 competition: results, conclusions and implications,
International Journal of Forecasting, 16, pp. 451-476.
Makridakis, S. (1993). Accuracy measures: theoretical and practical concerns. International Journal of
Forecasting, 9(4), pp. 527-529.
Markridas, S.; Wheelwright, S.C. & Hyndman, R.J. (1998). Forecasting- Methods and Applications.
New York: John Wiley and sons.
Mathai, V.A.; Agarwal, A.; Angamoalli, V.; Narayanan, S. & Dhakshayani, E. (2016). Development
of new methods for measuring forecast error. International Journal of Logistics Systems and
Management, 24(2), pp. 213–225.
Matsumoto, M. & Ikeda, A. (2015). Examination of demand forecasting by time series analysis for auto
parts remanufacturing. Journal of Remanufacturing, 5(1), pp. 10 -19.
Michael, L.; Paul, G.; Marcus, O.C. & Dilek, O. (2006). Judgmental forecasting: A review of progress
over the last 25 years. International Journal of Forecasting, 22(3), pp. 493-518.
Monahan, M.K. (2014). Aircraft demand forecasting, Masters Theses - current dissertations and theses.
A Master thesis submitted to Department of Industrial Engineering and Operations Research, University
of Massachusetts Amherst.
Moon, M.A.; Mentzer, J.T. & Smith, C.D. (2003). Conducting a sales forecasting audit. International
Journal of Forecasting, 19, pp. 5–25.
Mukttash, A. & Samhoun, M. (2011). Supply planning improvement: a causal forecasting approach.
Journal of Applied Sciences, 11(12), pp. 2207-2214.
Nijat, M.; Peter, F. & Peter, L. (2016). Evaluating forecasting methods by considering different
accuracy measure. Procedia Computer Science, 95, pp. 264-271.
Parzen, E. & Winkler, R (1982). The accuracy of extrapolation (time series) methods: Results of a
forecasting competition. Journal of Forecasting, 1, pp. 111–153.
Paul, G. (2014). Using naïve forecasts to assess limits to forecast accuracy and the quality of fit of
forecasts to time series data. Working Paper, University of Bath.
Pisal, Y.; Anulark, P. & Amnaj, C. (2001). Demand Forecasting and Production Planning for Highly
Seasonal Demand Situations: Case Study of a Pressure Container Factory. Science Asia, 27, pp. 271-
278.
Powers, D.M. (2011). Evaluation: from precision, recall and F-m easure to ROC, informedness,
markedness and correlation.
Pradeep, K.S. & Rajesh, K. (2014). The Evaluation of Forecasting Methods for Sales of Sterilized
Flavoured Milk in Chhattisgarh. International Journal of Engineering Trends and Technology, 8(2),
pp. 98-112.
Rakesh, K. & Dalgobind, M. (2013). Application of proper forecasting technique in Juice production:
A case study. Global Journal of Researches in Engineering. Industrial Engineering, 13(4), pp. 1-6.
Satya, P.; Ramasubramanium V. & S.C. Mehta, S.C. (2007). Statistical models for milk production in
India. Journal of Indian Society of Agriculture and Statistical, 61(2), pp. 80-83.
Sokolova, M. & Lapalme, G.A (2009). Systematic analysis is of performance measures forclassification
tasks. Information Processing & Management, 45(4), pp. 427-437.
Spedding, T.A. & Chan, K.K. (2000). Forecasting demand and inventory management using Bayesian
time series. Integrated Manufacturing Systems, 11(5), pp. 331-339.
Terui, N. & Van Dijk, H.K. (2002).Combined forecasts from linear and nonlinear time series models.
International Journal of Forecasting, 18 (3), pp. 421–438.
Van Sommeren, F.A.H. (2011). Improving forecast accuracy- Improving the baseline forecast for
Cheese products by use of statistical forecasting. A master thesis submitted to supply chain Department
of Friesland Campina Cheese, University of Twente.
Wacker, G.J. & Lummus, R.R. (2002). Sales forecasting for strategic resource planning. International
Journal of Operations & Production Management, 22(9), pp. 1014-1031.
Xu, B. & Ouenniche, J. (2012). Performance evaluation of competing forecasting models: A
multidimensional framework based on MCDA. Expert Systems with Applications, 39(9), pp. 8312-
8324.
Zhao, X.; Xie, J. & Leung, J. (2002). The impact of forecasting model selection on the value of
information sharing in a supply chain. European Journal of Operational Research, 142(2), pp. 321-
344.
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Published
2021-07-01
How to Cite
Collective Authors. (2021). Economic Development, Technological Change, and Growth: Array. Acta Universitatis Danubius. Œconomica, 15(3). Retrieved from https://dj.univ-danubius.ro/index.php/AUDOE/article/view/1215
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