PurposeThe success of a supply chain is highly reliant on effective inventory and ordering decisions. This paper systematically reviews and analyzes the literature on inventory ordering decisions conducted using behavioral experiments to inform the state-of-the-art.Design/methodology/approachThis paper presents the first systematic review of this literature. We systematically identify a body of 101 papers from an initial pool of over 12,000.FindingsExtant literature and industry observations posit that decision makers often deviate from optimal ordering behavior prescribed by the quantitative models. Such deviations are often accompanied by excessive inventory costs and/or lost sales. Understanding how humans make inventory decisions is paramount to minimize the associated consequences. To address this, the field of behavioral operations management has produced a rich body of research on inventory decision-making using behavioral experiments. Our analysis identifies primary research clusters, summarizes key learnings and highlights opportunities for future research in this critical decision-making area.Practical implicationsThe findings will have a significant impact on future research on behavioral inventory ordering decisions while informing practitioners to reach better ordering decisions.Originality/valuePrevious systematic reviews have explored behavioral operations broadly or its subdisciplines such as judgmental forecasting. This paper presents a systematic review that specifically investigates the state-of-the-art of inventory ordering decisions using behavioral experiments.

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This paper contributes to the general consideration of whether a policy of incentivising improved forecasts for renewable energy outputs, and making them more available in the daily electricity market, would be beneficial. Using data from the German electricity market, we investigate the effect of wind and solar energy forecasts errors on imbalance volumes and intraday spot electricity prices. We use ordinary least square regression, quantile regression and autoregressive moving averages to identify these relationships using variables that have a quarter-hourly data granularity. The results show a positive relationship between wind forecast errors and imbalance volumes. We find that wind forecast errors impact spot prices more than solar forecasting errors. Policy incentives to improve the accuracy and availability of renewable energy forecasts should therefore be encouraged.

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