Characterizing Risk Attitudes of Industrial Managers

We study the risk attitudes of an important segment of the economy: managers. We conduct artefactual field experiments with 130 managers from 12 industrial companies. Our analysis is particularly careful to evaluate alternative models of decision-making under risk. In general, we find that the managers in our sample are moderately risk averse. Assuming a standard EUT model they exhibit similar risk attitudes as other sample populations. However, we find some differences within our sample. Superiors exhibit a higher level of risk aversion than team members that work for them in their department. Comparing purchasing managers with a random sample of non-purchasing managers from different corporate functions such as controlling, sales, engineering and so on, we cannot conclude that they differ from each other. We show that alternative theories of risky behavior provide complementary information on the risk attitude of industrial managers. While an expected utility theory model only characterizes managers as globally risk averse, we learn from a prospect theory model that the managers in our sample are only risk averse for a certain range of payoffs. For other payoffs, they even exhibit risk-seeking behavior. The reference point that determines which outcomes are to be viewed as losses and which as gains is not that induced by the task frame. We show that subjects had implicit expectations about their earning in the experiment, and used these expectations to evaluate the lotteries presented to them. Remarkably, the managers in our sample did not weigh probabilities and they did not exhibit a hypothetical bias in their decisions

Optimizations for Risk-Aware Secure Supply Chain Master Planning

Supply chain master planning strives for optimally aligned production, warehousing and transportation decisions across a multiple number of partners. Its execution in practice is limited by business partners' reluctance to share their vital business data. Secure Multi-Party Computation (SMC) can be used to make such collaborative computations privacy-preserving by applying cryptographic techniques. Thus, computation becomes acceptable in practice, but the performance of SMC remains critical for real world-sized problems. We assess the disclosure risk of the input and output data and then apply a protection level appropriate for the risk under the assumption that SMC at lower protection levels can be performed faster. This speeds up the secure computation and enables significant improvements in the supply chain

Single-period stochastic demand fulfillment in customer hierarchies

To maximize profits, given limited resources, companies commonly divide their overall customer base into different segments and use allocation policies to prioritize the most important segments. Determining the optimal allocations is challenging due to supply lead times and uncertain demand. Another challenge is the multilevel hierarchical structure of the customer segments. In general, available quotas are not determined by a central planner with full visibility of all individual customer segments but rather result from a sequence of allocation steps with an increasing level of granularity. In this paper, we investigate this sequential allocation process. Specifically, we identify crucial information for making allocation decisions in customer hierarchies and propose decentralized methods that lead to near-optimal allocations while respecting the requirements for information aggregation. We evaluate the methods by comparing their information requirements and reflect on the role of information sharing in hierarchical allocation decisions