Thesis (M.S.)--University of North Carolina Wilmington, 2009. / Title from PDF title page (February 23, 2010)

Récemment, une nouvelle ère du commerce « connecté » arrive, dans laquelle l’offre des services logistiques fluides entre les différents canaux de vente, sera primordiale pour les réseaux de distribution. Dans ce contexte d’omnicanalité, nous focalisons aux liens pouvant être établis entre le logistics model adopté et le business model. Nous formulons l’hypothèse selon laquelle le logistics model adopté peut faire évoluer le business model : P1: le business model a un impact sur le logistics model. P2: Le logistics model a un impact sur le business model. P3: Quand le business model évolue, le logistics model évolue et s’adapte. P4 : L’évolution du logistics model a un impact sur l’évolution du business model. P5 : L’innovation du logistics model peut pousser l’innovation du business model. Une recherche empirique (4 cas) est menée permettant de capter les aspects dynamiques. Des entretiens semi-directifs avec les dirigeants, les responsables logistiques ont été réalisés. Des analyses intra-cas et inter-cas ont été menées. Apports: Au plan théorique, nous définissons les composants des business model et des logistics model dans l’industrie du prêt-à-porter et montrons 4 processus logistiques liés à la proposition de valeur. Nous montrons comment les évolutions ou les innovations logistiques peuvent permettre d’acquérir des compétences et du savoir-faire distinctifs susceptibles de proposer de nouvelles propositions de valeur aux clients et d’améliorer le business model d’une enseigne. Au plan managérial, les distributeurs trouveront matière à modéliser leurs activités et à positionner les services logistiques au centre de leurs business models futurs. / Recently, a new era of “connected commerce” is emerging, where the ability of offering effective and fluid logistics services among the different channels will be a key feature of distribution networks. In the context of omnichannel, we focus on the dynamic links between logistics model adopted by a distribution company and its business model. We make the hypothesis that the adopted logistics model can lead to changes in the business model: P1: the business model impacts the logistics model. P2: the logistics model impacts the business model. P3: the evolution of the business model impacts the evolution of the logistics model. P4: the evolution of the logistics model impacts the evolution of the business model. P5: The innovation of the logistics model impacts the innovation of the business model. To answer the research questions, we conducted a dynamic empirical research (four case studies in the ready-to-wear industry). Semi-structured interview with leaders, and logistics managers were conducted, followed by an intra-case and cross-case analyzes.Implications: At the theoretical level, we define precisely the components of the business model and the logistics model and demonstrate an association between four logistics processes and the value proposition to customers. We show how evolution and innovation of logistics can provide distinctive skills and know-how that can help create new value propositions to customers and improve the company’s business model. At the managerial level, our study offers insights to retailers certain ways to model their activities and to position the logistics services at the center of their future business model.

Doctor of Philosophy / Department of Biological & Agricultural Engineering / Donghai Wang / In 2008 AGCO began a project to develop machinery to harvest biomass for a DOE project called “Integration of Advanced Logistical Systems and Focused Bioenergy Harvesting Technologies to supply Crop Residues and A Herbaceous Energy Crops in a Diversified Large Square Bale Format”. The project considered the harvest of corn stover, wheat straw, switchgrass and energy sorghum. AGCO modified some existing pieces of production hay harvesting equipment and developed a new larger square baler for single pass crop residue harvesting. Field scale tests of the developed equipment occurred in the years 2010, 2011, and 2012. Data collected during these tests included crop harvested, field location, number of hectares harvested, moisture content of harvested biomass, number of bales produced, weight of each bale, time to harvest, model(s) and sizes of machine(s) used, and fuel consumed. Data was collected for different harvesting techniques for crop residues: two-pass vs single-pass harvesting for corn stover and wheat straw. Data was collected for harvesting switchgrass and energy sorghum for comparison purposes. The cropping years were very different over the course of the project due rain fall amounts. The data was analyzed using American Society of Agricultural and Biological Engineer machinery management standards and accepted Agriculture & Applied Economics Association assumptions. Excel spreadsheets were developed to calculate the harvesting costs on a dry Mg basis for each crop that was harvested. Results from the data analysis was used to modify the Integrated Biomass Supply Analysis and Logistics model to predict harvesting costs for crop residues at different yield levels, harvest conditions, and machine settings for single-pass harvesting. A number of conclusions can be drawn from this analysis. First, “take rates” for crop residues can have a significant effect on harvest costs. Low “take rates” can make it economically unfeasible to harvest crop residues in some instances. Second, single-pass harvesting of crop residues is less labor and fuel intensive than multi-pass harvesting. Third, the large yields potential of energy sorghum, which requires more operations to harvest than switchgrass, more economically to harvest than switchgrass. Fourth, operational techniques can be used to offset some crop variability to reduce harvest cost of crop residues. Lastly, a decision tool has been developed to aid producers in the decision of whether to harvest corn stover or not based on cost return estimates.

The purpose is to examine the predictability of Byström's market-based model on the Swedish market and compare it with the classic accounting-based Ohlson's logical model. The study uses a quantitative method for gathering data and the results from the models were analyzed by using the CAP-curve and AR to be able to compare the accuracy of the two different models. Type 1 and Type 2 errors have been defined as two categorization measures to distinguish two types of errors that the models can exhibit. The result showed that both Byström and Ohlson's calculated high degree of Type 1 error and a few of Type 2 errors. In comparison with each other, Byström's market-based model have a better accuracy than Ohlson's model according toCAP-curve. If the models applied in more than 1 year before the bankruptcy, both models shows a result that is not reliable with a low accuracy.