Forecasting Capability – Human Factor and Tools and Systems

Sharing the right and relative information in a wide range with the highest possible quality does not automatically lead to an accurate forecast outcome, how to analyze this information, how to interpret it and how to utilize it are some of the crucial determinants in producing an accurate forecast. Traditionally, forecasting capability is mainly depending on the forecaster’s competence, such as their experience, their skills and even their personalities. With information technology becoming an essential part of today’s businesses, various tools and systems are now consisting a major part of forecasting capability (Fildes & Hastings, 1994, p1&16). As correctly stated by Wang & Pervaiz (2007, p27), a business’s capability to best utilize its human resource and information technology resource creates competitive edge for the entity.

• Human Factors

The forecaster is the leader in the forecasting process (Singh, 2014, p5), to make the right adjustment and the right decisions, they need to possess a good set of skills and capabilities. As Singh (2014) summarized, first of all a good forecaster should have a comprehensive understanding of the business about its products, customers, markets and competition environment; then comes the good understanding of the data that is being fed into and processed by the forecasting systems, such as their meanings, validities and alignment. Secondly, a good forecaster should be highly capable of producing accurate forecast at different aggregation level by correctly utilizing the tools, systems and his knowledge, knowing when and how to change and make sound adjustment. Thirdly, a good forecaster

Chapter 2: Literature Review Lu

18 should always deliver meaningful forecasts with the right metrics to the right audience, and promote single set of forecast numbers to be used throughout the organization. Last but not least, a good forecaster should also be a good communicator with exceptional interpersonal skills to convey the forecasts correctly among the business functions, negotiate and obtain agreement when necessary, as well as educate the business when required. All of these skills and capabilities from the forecasters make significant impact on the forecast accuracy, as the forecasts are after all generated by the forecasters with the support of information technology tools and systems.

Managerial adjustment or judgmental adjustment has been recognized recently as a major factor that influences forecast accuracy with increased studies focusing on this aspect (Onkal, Zeynep & Lawerence, 2012, p693, Trapero et al., 2011, p506, Fildes et al., 2009, p20). Judgmental adjustment is usually taken as a supplement to complete or polish forecasts produced by tools and systems, taking into consideration the factors that are hard to be modeled into systems, such as strategy, special events, competition environment, short term or one off problems etc. (Onkal et al., 2012, p695, Trapero et al., 2011, p490/491). Various studies have come up with mixed findings with some suggesting that management adjustment improves forecast accuracy while others reached the opposite conclusion (Franses & Legerstee, 2011, p537). Franses & Legerstee (2011) examined the impact of managerial adjustment on forecast accuracy at the SKU level over different horizons, and found out that managerial adjustment tend to over manipulate the pure model based forecast, and the shorter the horizon, the worse the impact. It is then recommended when applying managerial or expert adjustment, extra caution needs to be taken to ensure they do add value not the opposite. Franses & Legerstee (2011) also pointed out that expert training in this aspect is essential for managerial adjustment to add true value to forecast accuracy. From another angle, judgmental adjustment can be done by individual forecasters or with a group of experts representing different functions of the supply chain in a collaborative manner. Onkal et al.’s (2012) research on role playing group forecasting showed that no significant difference on forecast accuracy is found between the role playing group and non-role playing group. However, the non-role playing group does make less adjustment over the initial forecast, and the role playing group showed a stronger commitment to their own roles and less agreement on the consensus forecast (Onkal et al.,

Chapter 2: Literature Review Lu

19 2012, p693/698). This result highlighted a potential issue with group forecasting approach, that the forecast accuracy can be affected by individual group members when they have a stronger commitment to their own roles and possess stronger influence abilities over the other group members. The higher level of disagreement between group members may also impact negatively on efficiently delivering a forecast result. Oliva & Watson (2009, p140) highlighted this as intentional bias caused by incentive misalignment and disposition of power. Here, choosing the right people with competencies not only on expertise but also with the right attitude and mindset to put the global objective on top of individual interest is key to ensure a more accurate and efficient forecast delivery.

• Tools and Systems

With the rapid development of information technology, various tools and systems are being applied in almost every part of the business to assist with analysis and decision making, forecast is one of the major areas that tools and systems are involved. Tools and systems make it possible to store and exchange large amount of information, enable fast and complex analysis, and produce forecast in a more efficient manner. However, the impact of tools and systems on forecast accuracy are twofold. On one hand, as expected, tools and systems can process large volume of data more efficiently and accurate than human being; on the other hand, not utilized or programmed properly, tools and systems can become obstacles to forecast accuracy, they may produce totally useless or misleading result (Singh, 2014, p5, Morlidge, 2014, p35). Based on Morlidge’s (2014, p35&36) analysis on M3- forecast competition, it is noted that sophisticated systems do not necessarily guarantee an accurate forecast outcome; however, combined methods usually produce better forecast than the single ones.