A hidden anchor: The influence of service levels on demand forecasts

Demand planning is informed by demand forecasts, service level requirements, replenishment constraints, and revenue projections. “Demand forecasts” differ from “demand plans” in that forecasts only represent the distribution (or the most likely value) of product demand. Motivated by common forecasting practices in industry, our research examines whether forecasters recognize this difference between demand forecasts and demand plans. Based on a lab experiment informed by data from two large FMCG companies, we found that forecasters factor service levels into their demand forecasts, even when they are clearly instructed to predict the most likely demand and incentivized to minimize the forecast error. We establish that this result holds for students and practitioners alike, and show that this behavior is driven by the service level information, and not some other anchor. We use data from a recent industry survey to support the external validity of our key findings

GUIDED JUDGEMENT FOR DEMAND FORECASTING IN THE PRESENCE OF SALES PROMOTIONS

Product forecasts are critical input into procurement, inventory, marketing decisions etc. The use of human judgement is common in the real-world forecasting practice. Human intervention occurs mainly to incorporate contextual information. The literature suggests that a forecasting support system (FSS) that systematically guides the forecaster in applying judgement can improve forecast accuracy. Guidance is the core component of such an FSS. A behaviourally-informed FSS (BIFSS), as defined in this thesis, is an FSS that aims to provide systematic guidance to inform the judgement of a forecaster. This thesis firstly investigates the impact of promotions on product sales and judgemental forecasts using industry data. Then, a novel conceptual framework for developing a BIFSS is presented based on the literature of decision support systems (DSS) and judgemental forecasting literature. Decisional guidance as a crucial element in this framework is selected for further investigation. A lab experiment is employed to examine the effectiveness of two types of guidance, interval guidance and adaptive guidance. The moderating impact of promotions as a major contributor to the complexity of forecasting (shown by using the industry observations) is also considered in the experiment design. Task complexity also varies by changing the noise level in the time series. The results confirm the effectiveness of guidance types in improving forecast accuracy. However, there was not a significant difference between the guidance types. It was also found that providing multiple guidance types does not necessarily result in more accurate forecasts. My analyses provide evidence that guidance is particularly effective under the most complex task setting. The positive effect of guidance under less complex settings is not significant. Providing multiple guidance types can better help forecasters overcome a higher complexity than only providing one guidance type

A simulation-based Data Envelopment Analysis (DEA) model to evaluate wind plants locations

As the world is getting overpopulated and over polluted the human being is seeking to utilize new sources of energy that are cleaner, cheaper, and more accessible. Wind is one of these clean energy sources that is accessible everywhere on the planet earth. This source of energy cannot be stored for later use; therefore, environmental circumstances and geographical location of wind plants are crucial matters. This study proposes a model to decide on the optimum location for a wind farm among the demand area. To tackle the uncertainty related to the geographical position of the nominated location such as wind speed; altitude; mean temperature; and humidity; a simulation method is applied on the problem. Other factors such as the time that a plant is out of service and demand fluctuations also have been considered in the simulation phase. Moreover, a probability distribution function is calculated for the turbine power. Then Data Envelopment Analysis (DEA) performs the selection between all the nominated locations for wind farm. The proposed model takes into account several important elements of the problems. Elements such as land cost; average power received from the wind blowing; demand point population etc. are considered at the same time to select the optimum location of wind plants. Finally, the model is applied on a real case in order to demonstrate its reliability and applicability

A simulation optimization approach to apply value at risk analysis on the inventory routing problem with backlogged demand

Inventory Routing Problem (IRP) is defined as the combination of vehicle routing, inventory management and delivery scheduling decisions. In this study, a model for a basic inventory routing problem is proposed, which controls the risk of exceeding capital dedicated to an IRP with a value at risk (VaR) measure. It is assumed that the structure of the basic model is one to many, the time horizon is single period and fleet composition is homogeneous. The objective of the model is to minimize the expected total cost over a planning horizon at the same time considering the affordable risk. Since a stochastic IRP is an NP-hard problem and considering VaR makes it more complex, it is not feasible to solve the large-scale problems through an exact model. Hence, a new simulation optimization procedure is proposed to solve the problem. Finally, a numerical example is presented to demonstrate its accuracy and applicability