The effect of inventory record inaccuracy in information exchange supply chains

The goal of this paper is to quantify the impact of Inventory Record Inaccuracy on the dynamics of collaborative supply chains, both in terms of operational performance (i.e. order and inventory stability), and customer service level. To do so, we model an Information Exchange Supply Chain under shrinkage errors in the inventory item recording activity of their nodes, present the mathematical formulation of such supply chain model, and conduct a numerical simulation assuming different levels of errors. Results clearly show that Inventory Record Inaccuracy strongly compromises supply chain stability, particularly when moving upwards in the supply chain. Important managerial insights can be extracted from this analysis, such as the role of 'benefit-sharing' strategies in order to guarantee the advantage of investments in connectivity technologies

Value of supplier's capacity information in a two-echelon supply chain

Cataloged from PDF version of article.In traditional supply chain models it is generally assumed that full information is available to all parties involved. Although this seems reasonable, there are cases where chain members are independent agents and possess different levels of information. In this study, we analyze a two-echelon, single supplier-multiple retailers supply chain in a single-period setting where the capacity of the supplier is limited. Embedding the lack of information about the capacity of the supplier in the model, we aim to analyze the reaction of the retailers, compare it with the full-information case, and assess the value of information and the effects of information asymmetry using game theoretic analysis. In our numerical studies, we conclude that the value of information is highly dependent on the capacity conditions and estimates of the retailers, and having information is not necessarily beneficial to the retailers

Value of supplier's capacity information in a two-echelon supply chain

In traditional supply chain models it is generally assumed that full information is available to all parties involved. Although this seems reasonable, there are cases where chain members are independent agents and possess different levels of information. In this study, we analyze a two-echelon, single supplier-multiple retailers supply chain in a single-period setting where the capacity of the supplier is limited. Embedding the lack of information about the capacity of the supplier in the model, we aim to analyze the reaction of the retailers, compare it with the full-information case, and assess the value of information and the effects of information asymmetry using game theoretic analysis. In our numerical studies, we conclude that the value of information is highly dependent on the capacity conditions and estimates of the retailers, and having information is not necessarily beneficial to the retailers

Are we strategically naïve or guided by trust and trustworthiness in cheap-talk communication?

Cheap-talk communication between parties with conflicting interests is common in many business and economic settings. Two distinct behavioral economics theories, the trust-embedded model and the level-k model, have emerged to explain how cheap talk works between human decision makers. The trust-embedded model considers that decision makers are motivated by nonpecuniary motives to be trusting and trustworthy. In contrast, the level-k model considers that decision makers are purely self-interested but limited in their ability to think strategically. Although both theories have been successful in explaining cheap-talk behaviors in separate contexts, they point to contrasting drivers for human behaviors. In this paper, we provide the first direct comparison of both theories within the same context. We show that, in a cheap-talk setting that well represents many practical situations, the two models make characteristically distinct and empirically distinguishable predictions. We leverage past experiment data from this setting to determine what aspects of cheap-talk behavior each model captures well and which model (or combination of models) has better explanatory power and predictive performance. We find that the trust-embedded model emerges as the dominant explanation. Our results, thus, highlight the importance of investing in systems and processes to foster trusting and trustworthy relationships in order to facilitate more effective cheap-talk interactions

HOWCAN MULTI- CHANNEL RETAILING FURTHER EXPAND THEIR PRODUCT RANGE WHILST SUSTAINING CUSTOMER EXPERIENCE AND SATISFACTION FOR BOOTS.COM?

The Nottingham MBA team were assigned to explore online product range extension of Boots.com from marketing, supply chain and financial perspectives. Firstly, from the marketing perspective we explored the market attractiveness, profitability, challenges of expanding the product lines in online industry specifically. Initially we identified the existing customer segments and the emerging customer segments, to better understand the target customers for extending the product line. We also considered market-product life cycle for applying the marketing strategies by considering the market, product and brand parameters by benchmarking with key players in the industry and industry trends. This enabled us to identify the areas of growth for extending the product line for Boots.com. Furthermore, future distribution method '�¢â�¬�� New Fangled Drop ship model was also evaluated to provide efficient delivery for extended product range in the area of supply chain management. After that, key performance indicators (KPI's) for sustaining customer experience and satisfaction across different channels is also suggested. Lastly we analysed the financial implications of extending the product line, by identifying the key future revenue generators for Boots.com. Considering this as the foundation we developed the hypotheses which were to be answered through the study. H1: For online product expansion in e-retailing, strategic market planning process necessitates careful examination of market attractiveness, typically in shaping market attractiveness, factors such as market size, market growth, competition, segmentation, targeting and positioning, margin potential and the company's core competencies. H2: In moving into new markets, the critical success factors include leveraging on brand/reputation and capability. The timing of products and customer segmentation are important for market attractiveness. H3: Good inventory management, cost-effective and efficient delivery and supply for new product protect margin and profitability. H4: In Key Performance Measurement ('KPI') for sustaining customer experience and satisfaction for Boots.com, Channel Alignment, Drop Ship and Efficient Standardised Delivery Methods for all products are used. The hypotheses were answered through a combination of qualitative and quantitative research methods. For analysing Boots.com internally we looked into Boots.com's internal data which was provided to us from all the perspectives of Sales and Marketing, Supply Chain and Financials. For understanding from customer perspective in terms of new products from marketing view point and understanding the customer experience in terms of delivery methods from multi channel retailing and supply chain view point, we conducted various levels of surveys for collecting the data. This cemented our research and analysis of data we collected from the industry reports. The results and finding reinforced our hypotheses in extending the product line from marketing, supply chain and financial perspectives. Finally, our research and analysis enabled us to recommend the key areas of product line extension for Boots.com and the scope of improvement for the future drop ship model. Based on the screening criteria of market attractiveness and business position, we have recommended five products which can be further maximised and taken further. These products are seen to be financially viable based on the projected future cash flow, projected profit margin, and net present value to signify increasing shareholders wealth. For Boots.com, these financial viability must be accompanied by Key Performance Indicators which are intrinsically linked to continuous improvement. To conclude, we also considered Boots.com's future road map for showcasing the future direction by incorporating these recommendations

Transportation interoperable planning in the context of food supply chain

L'alimentation est une nécessité de base de l'être humain, dont la survie dépend de la quantité et de la qualité de la nourriture ingérée. L'augmentation de la population requiert de plus en plus de nourriture, tandis que la qualité est associée aux contraintes des produits alimentaires comme une courte durée de vie ou la sensibilité à la température. L'augmentation de la demande entraîne une augmentation de la production alimentaire, répartie entre plusieurs sites de production appartenant à plusieurs entreprises de taille variée, qui peuvent utiliser les produits d'autres sites pour fabriquer leurs produits finaux. En outre, certains produits alimentaires doivent être transportés entre les sites et les produits finaux distribués à des détaillants et des consommateurs lointains en tenant compte des contraintes de produits alimentaires. Les activités exercées par ces entités incluent entre autres la production, la distribution, la vente, etc. et ces entités forment conjointement dans l'environnement de l'écosystème alimentaire une chaîne pour le traitement, l'emballage ou la livraison de nourriture. Ce réseau s'appelle une chaîne logistique alimentaire (FSC). En raison de leur nature distribuée, les FSC héritent des problèmes classiques des chaînes logistiques, mais doivent en plus gérer les problèmes découlant de la périssabilité des produits. Cette périssabilité rend extrêmement important le traitement d'enjeux tels que le maintien de la qualité, la prévision de la demande, la gestion des stocks (éviter les ruptures de stock ou les stocks excessifs), l’amélioration de l'efficacité du réapprovisionnement, de la production et du transport, la traçabilité et le suivi pour réagir aux perturbations. Il est donc nécessaire d'établir une collaboration entre les entités principales de l'écosystème alimentaire pour traiter tous ces enjeux. En outre, depuis l'arrivée des entreprises de transport spécialisées, un nouveau acteur a émergé appelé transporteur ou fournisseur de logistique. Ces transporteurs doivent collaborer avec les producteurs, les détaillants et même d'autres transporteurs afin de prendre en compte la demande future et les tendances, afin d'organiser leur réseau et les ressources, pour livrer des produits alimentaires en assurant sécurité et qualité. Ainsi, la collaboration est devenue vitale pour les FSC. La collaboration implique une bonne compréhension des informations échangées afin de minimiser les déplacements, le coût et la pollution environnementale. Des problèmes d'interopérabilité surgissent lorsque les partenaires impliqués utilisent des systèmes hétérogènes et différentes normes et terminologies. Les approches de collaborations existantes comme "Vendor Managed Inventory" (VMI) ou "Collaborative Planning Forecasting and Replenishment" (CPFR) ne prennent en compte que deux acteurs de la FSC : le producteur et le détaillant (acheteur et vendeur). En outre, elles ne considèrent pas la planification de la production et des transports comme des tâches de collaboration. En tenant compte des limitations ci-dessus, nous proposons, dans une première partie de cette thèse, une extension du modèle CPFR prennant en compte les aspects production et transport. Ce nouveau modèle C-PRIPT (Collaborative -Planning Replenishment Inventory Production and Transportation) inclut le transporteur et considère la planification de la production et des transports comme des activités de collaboration. Dans la deuxième partie, nous proposons un modèle distribué et interopérable I-POVES (Interoperable - Path Finder, Order, Vehicle, Environment and Supervisor) pour réaliser la planification des transports en collaboration avec les producteurs, les transporteurs et les détaillants, visant à une meilleure utilisation efficace des ressources de transport. Enfin, nous illustrons le fonctionnement du modèle I-POVES en l’appliquant sur un cas étude de chaîne logistique alimentaire. ABSTRACT : Eating is human’s basic necessity whose survival depends on both quantity and quality of food. Increasing population requires increasing in quantity of food, while quality is associated with the food product constraints like short shelf-life, temperature sensitiveness, climate etc. Increasing demand causes increase in food production, which is distributed between several production sites involving several distinct entities from small to large enterprises, where sites may use the intermediate products of other sites to produce the final products. Moreover, food products need to be transported between sites and final products to be distributed to faraway retailer sites and consumers considering the food product constraints. Activities performed by these entities include but not limited to: production, distribution, sales, etc. and these entities form jointly in the environment of food ecosystem a chain for food gathering, processing, packaging, delivery etc. This distributed network of enterprises is called food supply chain (FSC). Due to FSC’s distributed nature, it inherits not only the common problems also faced by other supply chain, but in addition has to deal with the problems arising from the perishability of food products. This perishability nature makes extremely important for FSC, the handling of issues such as maintaining the quality of food products, forecasting the product demand, managing the inventory according to the forecast to reduce out of stock or excessive inventory of products, improving the efficiency of replenishment, production and transportation, taking into account product future demand and tracing and tracking to react to disturbance. Finally, it is necessary to institute collaboration between the main entities of food ecosystem to deal with all of these issues. Furthermore, since the advent of specialized transport enterprises, a new actor has emerged called transporter or logistics provider in the FSC. These transporters have to collaborate with producers, retailers and even other transporters within FSC to take into account product future demands and trends to organise their transport network and resources to make possible the delivery of the food products with security, while maintaining the quality of the food products. Thus, collaboration became vital for FSC. Collaboration involves a good understanding of exchanged information in order to minimizing number of transport travels, cost and environmental pollution. Interoperability problem arises when each of the partners involved in FSC uses heterogeneous systems and uses different standards and terminologies for representing locations, product constraints, vehicles types etc. Furthermore, existing collaborative approaches like Quick Response, Efficient Consumer Response, Vendor Managed Inventory, Collaborative Planning Forecasting and Replenishment (CPFR), etc. take into account only two types of actors of FSC: buyer and seller (producer and retailer). Additionally, they don’t consider the production and transportation planning as collaborative tasks. Taking into account above limitations, we propose, in the first phase of this thesis, an extension of CPFR model, which take into account production and transportation aspects. This new model C-PRIPT (Collaborative -Planning Replenishment Inventory Production and Transportation) includes transporter actor and elaborates production and transportation planning as collaborative activities. In the second phase, we propose a distributed and interoperable transportation planning model I-POVES (Interoperable - Path Finder, Order, Vehicle, Environment and Supervisor) to realise collaborative transportation planning by collaborating producers, transporters and retailers, aiming at a better use of transport resources. Finally, we illustrate the functioning of I-POVES model by applying it on a case study of food supply chain