Improving the decision-making process by considering supply uncertainty - a case study in the forest value chain

• Published in: International journal of production research Vol. 62; no. 3; pp. 665 - 684
• Main Authors: Simard, Vanessa, Rönnqvist, Mikael, LeBel, Luc, Lehoux, Nadia
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis 01.02.2024; Taylor & Francis LLC
• Subjects: Accuracy; Case studies; Cost reduction; Decision making; forest value chain; Forestry; Frequency distribution; Normal distribution; Operating costs; Planning; production planning; Productivity; Quality assessment; sample average approximation; Statistical analysis; Stochastic programming; Supply chains; Supply uncertainty; Uncertainty; Value chain; Value engineering
• ISSN: 0020-7543; 1366-588X
• DOI: 10.1080/00207543.2023.2169382

Planning decisions are generally subject to some level of uncertainty. In forestry, data describing the resources available have a major impact on operations performance and productivity. This paper aims to present a method to improve decision-making in the forest supply chain by taking supply uncertainty into account using the results of data quality assessments. The case study describes the operations planning process of a Canadian forest products company dealing with an uncertain volume of wood supply. Three approaches to constructing probability distributions based on data quality are tested. Each approach offers a different level of precision: (1) a frequency distribution of accuracy, (2) a normal distribution based on average accuracy, and (3) a normal distribution based on data quality classification. Using stochastic programming to plan transport and production shows that lower costs can be achieved with a general characterisation of the data accuracy. Not considering uncertainty when planning operations leads to a significant replanning transportation cost. Using classes of data quality to include uncertainty in operations planning contributes to reducing the transportation cost from $15.90/m 3 down to $15.32/m 3 representing 3.6%.