This paper introduces a variant of the assortment problem, i.e., the Best Allocation Resource Size (BARS) problem, conceived as a multi-objective nonlinear optimization model that jointly minimizes total production-related costs and input complexity by selecting the optimal subset of resource sizes, explicitly balancing cost-efficiency with scrap minimization. Unlike traditional assortment problems, the BARS adopts a manufacturer-led perspective, handling multiple characteristics of the inputs, i.e., it is n-dimensional, and it integrates multiple cost dimensions - including setup, scrap, safety stock, and non-linear purchase costs - under realistic manufacturing constraints, thus offering a flexible, producer-oriented decision-support tool adaptable to diverse industrial contexts. The problem formulation does not incorporate an ex-ante budget constraint, allowing the solution space to be retrospectively filtered by applying cost thresholds - either on aggregate expenditure or individual cost components - thus enabling targeted post-hoc analyses of feasible item sets in line with strategic objectives, including scenarios that allow for controlled increases in spending. The model proposed is solved using an adapted ε-constraint method that, unlike a generic scalarization, is provably exact for bi-objective nonlinear integer programs: a second-order cone reformulation of the safety-stock term allows to determine properly calibrated ε values, ensuring recovery of the complete Pareto front. The efficacy of the proposed model is demonstrated through validation in three real-world case studies spanning different industrial sectors. The developed model serves as a strategic decision tool for management, facilitating an understanding of the costs consequences associated with different sets of selected raw materials.
Greenwashing may occur at multiple stages of a supply chain, as different participants possess varying levels of knowledge about suppliers (i.e., information asymmetry). This phenomenon is common in practice, yet remains underexplored in the literature. This study investigates how information sharing affects greenwashing behavior in supply chains under two-level information asymmetry, defined as a situation in which the downstream firm has more, though still incomplete, knowledge of the supplier than consumers. First, we propose a game-theoretical framework with two-level information asymmetry to investigate greenwashing behavior in a supply chain without information sharing (i.e., non-sharing). It is shown that both the upstream supplier and the downstream firm are motivated to engage in greenwashing, albeit through different approaches. Next, we examine whether two information sharing formats, discriminatory sharing and universal sharing, can eliminate these greenwashing issues. We find that information sharing among all parties, including consumers (i.e., universal sharing), can completely eliminate greenwashing. However, if information is shared only among supply chain members (i.e., discriminatory sharing), it may exacerbate greenwashing. By comparing different information sharing formats, we further find that although universal sharing can completely eliminate greenwashing, it does not emerge as the equilibrium sharing format. Finally, we propose two strategies to address this misalignment and demonstrate their effectiveness, along with the underlying mechanisms.
Experiences from real-world supply chains as well as exhaustive research show that the lack of coordination within supply chains leads to bullwhip effect, in which the magnitude of fluctuations in orders increases in the upstream direction from retailer to the supplier. One potential solution for addressing this phenomenon is information sharing between supply chain actors that can reduce the impact of fluctuations in customer demand on the supply chain. However, sharing too much information might lead to higher vulnerability of stakeholders in negotiations with their partners or to an unwanted leakage of information to competitors. Therefore, relevant actors are often reluctant to share the necessary information.To overcome these problems, we propose a horizontal information sharing model where retailers share demand information in form of average values between each other without revealing the exact values of each individual partner. This could be achieved by, e.g., applying costly cryptographic methods. Therefore, we evaluate the potential effects of this type of information sharing on the whole supply chain by developing a simulation model representing a 4-echelon supply chain, which consists of multiple customers, each served by their own retailer, one wholesaler, one manufacturer and one supplier. We investigate the effect of horizontal information sharing of average customer demand among the retailers on the bullwhip effect and show its effects on the different supply chain actors. In addition, the influences of demand variability and demand correlations between customers, different forecasting methods, safety stock approaches, stochastic lead times and the number of retailers sharing the information on the bullwhip effect are analyzed.
Markets are increasingly demanding greater supply chain transparency, yet this remains a significant challenge for many companies. Understanding what type of information to disclose and the timing of disclosure across the customer journey represents a key decision for companies, particularly when the information pertains to circular initiatives, such as those involved in closed-loop activities. This study, grounded in signaling theory, explores the effect of disclosing closed-loop supply chain information on consumers' intentions. We selected consumers as our unit of analysis for the active role they can play when referring to companies' closed-loop initiatives, such as purchasing recycled products or participating in take-back programs. To test the hypotheses, we conducted two scenario-based experiments with consumer participants, followed by a post-hoc experimental analysis and a post hoc qualitative study with managers to further discuss and validate the findings. The primary aim of the studies is to operationalize supply chain transparency through its visibility and traceability components in a closed-loop supply chain context. A second objective is to examine the effectiveness of transparency in closed-loop supply chains, analyzing how the two components influence consumer outcomes across different phases of the customer journey. Our findings demonstrate a positive indirect effect of the joint visibility–traceability disclosure on participation intention through consumers’ attitudes toward the take-back initiative, whereas the hypothesized effects on purchase intention are not supported. The study contributes to the growing literature on supply chain transparency by extending signaling theory to closed-loop supply chain contexts and offering insights for managers on strategic investments in transparency.
