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Joint cost management is a decisive factor for sustainable collaboration in supplier-buyer dyads. For both parties the establishment of an accurate cost estimation (CE) framework supports managing suppliers’ costs as well as manufacturer’s quotation costing. Grounded on resource dependence theory and following a design science research approach, this study aims to develop a multi-perspective CE system inspired by statistical learning , deep learning- decision-making and multi-agent theory. We evaluate our system in a single case and computer simulation study using empirical data coming from observations and archives at a large Bavarian original equipment manufacturer (OEM). The results indicate that our CE approach can select the most significant cost-drivers and predict total costs of parts and assemblies with high accuracy to support the supplier in efficiently managing its costs. In making the CE blackbox model transparent using a combination of model agnostic post-hoc explainable artificial intelligence approaches we create user acceptance for both suppliers and OEMs. All CE-artifacts are ensembled in a multi-agent system being able to automatically manage costs with suppliers and furthermore, as a model extension, can lead to a collaborative price agreement. Our system supports supply chain managers on both sides in entering into a sustainable long partnership.

In the automotive industry, cost estimation of components to be purchased plays an important role for price negotiations with suppliers and, therefore, for cost control within the supply chain. While traditional bottom-up cost estimation is a very time consuming and know-how intensive process, intelligent machine learning methods have the potential to significantly reduce the effort in the cost estimation process. In this paper, a literature review on intelligent cost estimation methods for parts to be procured in the manufacturing industry is carried out by text mining. Following the results of this literature review, building blocks for an intelligent cost estimation system are outlined that comprise cost estimation methods, dimensionality reduction methods, methods for multi-level cost estimation, and methods for interpreting the results of the cost analysis. Regarding cost estimation methods , Artificial Neural Networks and Support Vector Machines outperform established linear regression algorithms. Dimensionality reduction methods like Correlation Analysis or Principal Component Analysis are rarely studied . Nevertheless, they contribute a lot to the reduction of expensively provided input parameters for cost estimation. Methods for multi-level cost estimation, that support cost prediction of parts and assemblies following the construction plan of a vehicle, and methods for interpretation of intelligent cost analytics cannot be found at all in literature. Consequently, in this paper corresponding approaches are derived from the areas of Multitask Learning and Explainable Machine Learning. Finally, a combination of methods considered most suitable for predictive analytics to estimate procurement costs is presented.

Linoleic acid (C18:2(Δ9,12), LA) is crucial for many cell functions in organisms. It has long been a paradigm that animals are unable to synthesize LA from oleic acid (C18:1(Δ9), OA) because they were thought to miss Δ(12)-desaturases for inserting a double bound at the Δ(12)-position. Today it is clear that this is not true for all animals because some insects and other invertebrates have been demonstrated to synthesize LA. However, the ability to synthesize LA is known in only five insect orders and no examples have been reported so far in the Hymenoptera. LA plays a particular role in the parasitic wasp Nasonia vitripennis, because it is the precursor of the male sex pheromone consisting of (4R,5R)- and (4R,5S)-5-hydroxy-4-decanolides. Here we demonstrate by stable isotope labeling that N. vitripennis is able to incorporate externally applied fully (13)C-labeled OA into the male sex pheromone suggesting that they convert initially OA into LA. To verify this assumption, we produced fly hosts (Lucilia caesar) which were experimentally enriched in (13)C-labeled OA and reared male parasitoids on these hosts. Chemical analysis of transesterified lipid raw extracts from hosts and parasitoids revealed that N. vitripennis but not L. caesar contained (13)C-labeled LA methyl ester. Furthermore, male wasps from the manipulated hosts produced significant amounts of (13)C-labeled sex pheromone. These results suggest that N. vitripennis possesses a Δ(12)-desaturase. The additional fitness relevant function as pheromone precursor might have favored the evolution of LA biosynthesis in N. vitripennis to make the wasps independent of the formerly essential nutrient.