A hybrid demand forecasting model for greater forecasting accuracy: the case of the pharmaceutical industry

Raheel Siddiqui,Muhammad Azmat,Shehzad Ahmed,Sebastian Kummer

doi:10.1080/16258312.2021.1967081

Abstract

ABSTRACT In the era of modern technology, the competitive paradigm among organisations is changing at an unprecedented rate. New success measures are applied to the organisation’s supply chain performance to outperform the competition. However, this lead can only be obtained and sustained if the organisation has an effective and efficient supply chain and an appropriate forecasting technique. Thus, this study presents the demand-forecasting model, i.e., a good fit for the pharmaceutical sector, and shows promising results. Through this study, it is observed that combining forecasting algorithms can result in greater forecasting accuracies. Therefore, a combined forecasting technique ARIMA-HW hybrid1 i.e. (ARHOW) combines the Autoregressive Integrated Moving Average and Holt’ s-Winter model. The empirical findings confirm that ARHOW performs better than widely used forecasting techniques ARIMA, Holts Winter, ETS and Theta. The results of the study indicate that pharmaceutical companies can adopt this model for improved demand forecasting.

Highlights

In the pharmaceutical industry, demand forecasting is essential for optimising and managing complex busi ness processes (MERKURYEVA and ALEXANDER SMIRNOV 2019)
According to Munim and Schramm (2017), the hybrid forecasting model ARIMARCH combining the AutoRegressive Integrated Moving Average (ARIMA) and Autoregressive Conditional Heteroscedasticity (Walters and Archer) model enhances the forecasting accuracy when compared to that yield by the appli ance of a single forecasting model
We develop and introduce a new hybrid forecasting model, combining the ARIMA and the Holt’s models

Summary

Introduction

Demand forecasting is essential for optimising and managing complex busi ness processes (MERKURYEVA and ALEXANDER SMIRNOV 2019). WELLER and CRONE (2012) stated that data are mostly collected for demand fore casting purposes by employing a collaborative infor mation-sharing mechanism. From other sources like sales and operations, collaborative planning forecast ing and replenishment and vendor managing inven tory data help the manufacturers forecast accurately. Deep learning tools can help achieve higher forecast accuracy by creating patterns in deci sion making rather than using a single model to fore cast demand (THOMSON et al 2019). NIKOLOPOULOS et al (2016) worked on pharmaceuti cals brands and generics to forecast the demand For this instance, seven different forecasting techniques have been applied as deep learning tools to analyse the past 21 years’ data set. In con trast, the Naïve technique has a more significant fore casting result for a period of 2 to 5 years

Literature Review

Forecasting methods

Method and Forecasting Models

Conclusion

Findings

Notes on contributors