Companies that experience a disruption in their supply chain often face a difficult decision—either accept the information that they have regarding the duration of the disruption, or invest in collecting better information. This choice is not clear since better information may not be attainable, and if it is attainable, it may not improve operational decision-making. In light of this dilemma, we collaborate with a multinational division of a Fortune 500 manufacturing firm to develop stochastic linear programming models that quantify the value of disruption duration information. Our models allow us to examine characteristics of the disrupted part that may be associated with the value of better information. We focus on characteristics that are knowable at the outset of the disruption, as those can help the firm decide whether to invest in collecting better information. Using our research partner's supply chain and production data, we find that the value of information can vary materially—from < 1% to over 99% of the cost of the disruption, underscoring the value of identifying disruptions that are sensitive to information quality. To address this, we use the company's data to identify several part-related characteristics that influence the value of disruption duration information. These findings can help managers to identify parts in their own supply chains whose impact in a disruption is sensitive to different levels of duration information, and allow them to make informed decisions on whether or not to gather better information when a disruption strikes.
- disruption information
- manufacturing operations
- supply chain disruptions
ASJC Scopus subject areas
- Management Science and Operations Research
- Industrial and Manufacturing Engineering
- Management of Technology and Innovation