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Buying firms lack transparency about the supplier relationships in their networks. The applica-tion of dedicated tools such as Supply Network Mapping (SNM) can help to visualize and analyze these relationships. However, the impact of such tools on the purchasing performance has not been explored yet. Moreover, companies with different competitive strategies might have different motivations to use these tools. Therefore, this paper tests the impact of supplier relationship information and SNM on the purchasing performance on a large sample of 624 purchasers. A multi-group analysis in structural equation modeling estimates the impact of a cost leadership versus a differentiation strategy on cost saving and innovation performance. We show that information quality and SNM indeed improve the purchasing performance. Moreover, cost leaders use SNM if they know their supplier relationships with sub-suppliers, while innovation leaders use it if they know their supplier relationships with other customers. Hence, our results prove the usefulness of the SNM tool and give recommendations for its use depending on a company’s competitive strategy.
Most companies have realized the high importance of becoming the preferred customers of their suppliers to obtain preferential resource allocation. However, they cannot evaluate their own customer attractiveness properly. In order to make the assessment of the own customer status possible, this paper analyzes the impact of several information sources on the preferred customer status knowledge, supplier satisfaction knowledge and knowledge of alternative supplier relationships with other customers. Testing these hypotheses on a sample of 624 pur-chasers, we show that people provide more relevant information on the company’s strategic positioning than media. In particular, the suppliers, competitors and other actors are very im-portant information sources. Following our findings, purchasers should adopt their activities in order to better anticipate their suppliers’ intention and the customer treatment that they can expect from their suppliers.
Strategically Aligning Additive Manufacturing Supply Chains for Sustainability and Effectiveness
(2019)
This paper builds on a previously developed framework that integrated additive manufacturing, life-cycle analysis, and value creation (Feldmann & Kirsch, 2019) by exploring conditions related to the life-cycle approach that would require alignment among suppliers, additive manufacturing firms, and customers. This extension creates a bridge to aid implementation of taking a sustainability approach to additive manufacturing. In order to develop this extension, we distinguish between direct/indirect customers and internal/external customers and then create a matrix of incentives and cognitive frames that we believe will help companies interested in large-scale AM improve both the speed and the effectiveness of AM adoption. We provide an organizing framework that managers can use to create a supply chain that is aligned around closed-loop principles that will help speed adoption and move closer to sustainable goals that exist for AM technologies. These include reduced raw material use, reduced scrap and material overage, and reduced rework, and lower transportation costs. The goal is to attain often-conflicting goals of lower long-term costs and decreased environmental footprint. Using our extension, we believe we can provide a useful framework to help managers implementing advanced manufacturing technologies to achieve lower costs and greater environmental sustainability by creating a common supply chain framework around customized, on-demand products.
A major requirement for Credit Scoring models is of course to provide a risk prediction that is as accurate as possible. In addition, regulators demand these models to be transparent and auditable. Thus, in Credit Scoring very simple Predictive Models such as Logistic Regression or Decision Trees are still widely used and the superior predictive power of modern Machine Learning algorithms cannot be fully leveraged. A lot of potential is therefore missed, leading to higher reserves or more credit defaults. This talk presents an overview of techniques that are able to make “black box” machine learning models transparent and demonstrate how they can be applied in Credit Scoring. We use the DALEX set of tools to compare a traditional scoring approach with state of the art Machine Learning models and asses both approaches in terms of interpretability and predictive power. Results show that a comparable degree of interpretability can be achieved while machine learning techniques keep their ability to improve predictive power.