## 1 Introduction  
In the contemporary world of business, due to frequent technological advancements and operational changes, competition, complexities and challenges have increased drastically [1]. Moreover, easier access to international markets, expeditious technological advancement and the ceaseless requirements of quality products has complicated the scenarios for manufacturers. Hence, to contend with this dynamic but uncertain environment, manufacturers have no choice other than listening their steps to gratify customers' demands on time and efficiently [2]. In this regard, the urge for a more accurate and reliable forecasting approach cannot be overlooked. Plenty of forecasting approaches with innovative ideas have been proposed in the recent past which are broadly classified as qualitative and quantitative techniques. In the qualitative approach, prediction is made on the basis of the market dissection and the judgement of the experienced managers, whereas, the quantitative techniques mainly focus on historical data. However, in the latter forecasting class, the decision-making process is generally based on more or less complex mathematical models [3]. Among the quantitative approaches, researchers have shown particular interest in exponential smoothing techniques characterized by simplicity, robustness and ease of use [4]. Consequently, a number of exponential smoothing techniques have already been proposed, modified and/or improved in literature to anticipate an unknown event, enhance forecasting accuracy, as well as optimize a business plan e.g., production plan, production schedule and inventory control etc. [4; 5; 6; 7; 8; 9]. Nevertheless, with this persisting evolution of techniques, the single and/or simple exponential smoothing model of [PERSON] [10], double and/or trend correction model of [PERSON] [11], triple and/or Holt-Winters approach of [PERSON] [5] are still considered as reliable as well as the widely adapted forecasting approaches in literature. Interestingly, all of these three exponential smoothing techniques are observed to follow the same principle in forecasting i.e., they estimate future events through the extrapolation of historical data patterns. However, with the presence of similarities in principles, these exponential smoothing techniques also demonstrate a number of distinguishing characteristics in their computational processes, parameter counts and responsiveness to different time-series components [9; 10; 11; 12]. For instance, the simple exponential smoothing technique fits well with the time series exhibiting the changing mean having no seasonal and trend components. Whereas, with the presence of a trend component, varying level and growth rate, the [PERSON]'s trend correction model shows its superiority over the simple one. Meanwhile, the Holt-Winters forecasting model is observed to outperform the two former techniques for the time series, having changing seasonality, mean and growth rate [12; 13; 14; 15]. Nevertheless, by featuring the mode of incorporation of these parameters i.e., either in a constant rate or by an incremental amount, the Holt-Winters model is further classified into additive and multiplicative models [9; 12; 13; 14; 16]. Among these, the multiplicative Holt-Winters model, which was originally proposed by [PERSON] in 1960 [5], is adopted for the time series in which the seasonal variations fluctuate along with the mean values [17]; whereas the additive model is chosen for the time series in which seasonal variations do not fluctuate along with the mean values [16]. However, to incorporate the effects of changing level, growth rate and seasonal pattern in forecasting, both of these Holt-Winters approaches practice recursive equations, the appropriateness of which depends largely on the initial values of the level, growth rate and seasonal components [14; 18].