Inventory cost consequences of variability demand process within a multi-echelon supply chain

The bullwhip effect (Lee et al, 1997a) is a known supply chain phenomenon where small variations in end item demand create oscillations that amplify throughout the chain. Different price elasticity of demand influence different changes of demand when prices of items are changing on the time horizon. The variance of the orders at the end user placed on suppliers or on manufacturer increases with the orders flow upstream in the logistics chain. This creates harmful consequences in inventory levels and all kind of inventory costs that may affect added value of activities along the logistics chain and finally affect Net Present Value of all activities in the chain. Traditional model of dynamic supply chain structures is used for this particular study, based on the seminal work of Forrester Diagrams (Forrester 1961). Simulation platform for supply chain management at stochastic demand developed by Campuzano (2006) has been used. VENSIM Simulation Software was previously used for developing these supply chain dynamic models. In the development platform generalised supply chain models are constructed graphically and also analytically. Our study here is to get a dipper insight into the processes in a logistics chain, measuring the inventory cost consequences due to variability demand amplification

INTEGRATING INVENTORY AND TRANSPORT CAPACITY PLANNING IN A FOOD SUPPLY CHAIN

[EN] The general objective of this paper is to simulate a supply chain to assess the effects that different inventory management policies and transport capacity systems have on costs (transport) and service levels (stockouts). This paper specifically aimed to facilitate the decision-making process about planning distribution capacities, particularly when contracting a transport fleet in a supply chain under uncertainty with a 1-year time horizon by evaluating different types of scenarios, which vary depending on availability of vehicles and obtaining vehicles. The system dynamics simulation model was applied to a real-world food supply chain and can be adopted by chains related to diversified cropping systems. The results provide the best decision alternative in terms of costs and inventory levels by considering the transport capacity life cycle, the time to acquire additional transport capacity, the reorder point in days of stock and the target inventory.This work was supported by the European Commission Horizon 2020 project entitled 'Crop diversification and low-input farming cross Europe: from practitioners' engagement and ecosystems services to increased revenues and value chain organisation' (Diverfarming), grant agreement 728003.Freile, A.; Mula, J.; Campuzano Bolarin, F. (2020). INTEGRATING INVENTORY AND TRANSPORT CAPACITY PLANNING IN A FOOD SUPPLY CHAIN. International Journal of Simulation Modelling. 19(3):434-445. https://doi.org/10.2507/IJSIMM19-3-52343444519

A general outline of a sustainable supply chain 4.0

[EN] This article presents a literature review to identify the current knowledge of supply chains 4.0 from the sustainability perspective. Reviewed papers were classified in terms of objectives, results, and sustainability approaches. Additionally, a critical discussion with the main results and recommendations for further research was carried out. Manufacturing supply chains have been contemplated but agri-food supply chains and chains related to diversified cropping systems have been also considered. In this way, 54 articles were identified and revised, and were classified according to the three main aspects of sustainability: economic, social, and environmental. The classification of articles indicated that more attention has been paid to the environmental aspect in the industry 4.0 (I4.0) context in the literature, while the social aspect has been paid less attention. Finally, reference frameworks were identified, along with the I4.0 models, algorithms, heuristics, metaheuristics, and technologies, which have enabled sustainability in supply chains.This research was supported by the European Commission Horizon 2020 project entitled 'Crop diversification and low-input farming cross Europe: From practitioners' engagement and ecosystems services to increased revenues and value chain organisation' (Diverfarming), grant agreement 728003; and the Spanish Ministry of Science, Innovation, and Universities project entitled 'Optimisation of zero-defects production technologies enabling supply chains 4.0 (CADS4.0)' (RTI2018-101344-B-I00).Cañas, H.; Mula, J.; Campuzano-Bolarín, F. (2020). A general outline of a sustainable supply chain 4.0. Sustainability. 12(19):1-17. https://doi.org/10.3390/su121979781171219Design Principles for Industrie 4.0 Scenarios https://ieeexplore.ieee.org/document/7427673Liao, Y., Deschamps, F., Loures, E. de F. R., & Ramos, L. F. P. 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Procedia Manufacturing, 21, 438-445. doi:10.1016/j.promfg.2018.02.142Müller, J. M., Kiel, D., & Voigt, K.-I. (2018). What Drives the Implementation of Industry 4.0? The Role of Opportunities and Challenges in the Context of Sustainability. Sustainability, 10(1), 247. doi:10.3390/su10010247Kamble, S. S., Gunasekaran, A., & Gawankar, S. A. (2018). Sustainable Industry 4.0 framework: A systematic literature review identifying the current trends and future perspectives. Process Safety and Environmental Protection, 117, 408-425. doi:10.1016/j.psep.2018.05.009Hidayatno, A., Destyanto, A. R., & Hulu, C. A. (2019). Industry 4.0 Technology Implementation Impact to Industrial Sustainable Energy in Indonesia: A Model Conceptualization. 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A Framework of Production Planning and Control with Carbon Tax under Industry 4.0. Sustainability, 10(9), 3221. doi:10.3390/su10093221Birkel, H., Veile, J., Müller, J., Hartmann, E., & Voigt, K.-I. (2019). Development of a Risk Framework for Industry 4.0 in the Context of Sustainability for Established Manufacturers. Sustainability, 11(2), 384. doi:10.3390/su11020384Roda-Sanchez, L., Garrido-Hidalgo, C., Hortelano, D., Olivares, T., & Ruiz, M. C. (2018). OperaBLE: An IoT-Based Wearable to Improve Efficiency and Smart Worker Care Services in Industry 4.0. Journal of Sensors, 2018, 1-12. doi:10.1155/2018/6272793Ardanza, A., Moreno, A., Segura, Á., de la Cruz, M., & Aguinaga, D. (2019). Sustainable and flexible industrial human machine interfaces to support adaptable applications in the Industry 4.0 paradigm. International Journal of Production Research, 57(12), 4045-4059. doi:10.1080/00207543.2019.1572932Zambon, I., Cecchini, M., Egidi, G., Saporito, M. G., & Colantoni, A. (2019). 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Consecuencias del efecto Bullwhip según distintas estrategias de gestión de la cadena de suministro: modelado y simulación

[ESP] El efecto Bullwhip es uno de los principales causantes de las inestabilidades en el proceso de gestión de demanda que se producen a lo largo de la cadena de suministro. El presente artículo expone un modelo capaz de recrear diferentes escenarios para la gestión de demanda en una cadena de sumi- nistro determinada, con independencia del número de niveles definidos en la cadena de suministro considerada. El modelo, realizado utilizando la metodología de la dinámica de sis- temas, incorpora las variables necesarias para simular dicho proceso de gestión de demanda, como por ejemplo: niveles de inventario, ordenes de reabastecimiento, fabricación, previsiones u otras. Se muestra la utilidad del modelo propuesto, comparando los resultados que ofrecen dos escenarios diferentes, como son los representados por una cadena tradicional y el de una cadena reducida. [ENG] The Bullwhip effect is one of the main causes of instability in the manage- ment demand process along the Supply Chain. We introduce a model which is able to reproduce different Supply Chain Management Scenarios within a determinate Supply Chain with whichever the levels of this one. The model has been built using Systems Dynamics Methodology and incorporates the main variables which are required for simulating the Man- agement Demand Process (Inventory levels, Replenishment orders, manu- facturing process, forecasting etc.). This paper demonstrates the utility of the proposed model comparing the results offered by two different scenarios namely Traditional supply chain and Reduced supply chain

Assessing the impact of prices fluctuation on demand distortion within a multi-echelon supply chain

Price fluctuation is a practice commonly used by companies to stimulate demand and a main cause of the Bullwhip effect. Assuming a staggered step demand pattern that responds elastically to retailer’s price fluctuation, and by using a supply chain management dynamic model, we will analyse the impact of these fluctuations on the variability of the orders placed along a traditional multilevel supply chain. Subsequently, the results obtained will serve to propose a forecasting model enabling to calculate the potential variability of orders placed by each echelon on the basis of the price pattern used. Finally, under the hypothesis of an environment of collaboration between the different members of the chain, we propose a predictive model that makes it possible to quantify the distortion of the orders generated by each level.En este artículo se analiza la influencia de la fluctuacion de los precios en la variabilidad de las órdenes generadas a lo largo de una cadena de suministro tradicional multinivel. Para ello, se utiliza un modelo dinámico de gestión de ca- dena de suministro en el que se introduce un patrón de de- manda tipo escalón, que responde elásticamente a la fluc- tuación de los precios ofrecidos por el minorista al cliente final. Posteriormente, utilizando los resultados obtenidos, se propone un modelo de previsión para calcular esa posible variación de las órdenes generadas en cada nivel, a partir del patrón de precios utilizado

EMODnet physics and river runoff data management

Rivers runoff exert a strong influence in their neighbouring coastal area in several ways, modifying the water stratification, introducing significant fluctuations in circulation patterns and modulating the impact of upwelling events. This paper presents data management methods and standards to make harmonised river data available and accessible.Peer Reviewe

EMODnet physics and river runoff data management

Rivers runoff exert a strong influence in their neighbouring coastal area in several ways, modifying the water stratification, introducing significant fluctuations in circulation patterns and modulating the impact of upwelling events. This paper presents data management methods and standards to make harmonised river data available and accessible.Peer Reviewe

A rolling horizon simulation approach for managing demand with lead time variability

[EN] This paper proposes a rolling horizon (RH) approach to deal with management problems under dynamic demand in planning horizons with variable lead times using system dynamics (SD) simulation. Thus, the nature of dynamic RH solutions entails no inconveniences to contemplate planning horizons with unpredictable demands. This is mainly because information is periodically updated and replanning is done in time. Therefore, inventory and logistic costs may be lower. For the first time, an RH is applied for demand management with variable lead times along with SD simulation models, which allowed the use of lot-sizing techniques to be evaluated (Wagner-Whitin and Silver-Meal). The basic scenario is based on a real-world example from an automotive single-level SC composed of a first-tier supplier and a car assembler that contemplates uncertain demands while planning the RH and 216 subscenarios by modifying constant and variable lead times, holding costs and order costs, combined with lot-sizing techniques. Twenty-eight more replications comprising 504 new subscenarios with variable lead times are generated to represent a relative variation coefficient of the initial demand. We conclude that our RH simulation approach, along with lot-sizing techniques, can generate more sustainable planning results in total costs, fill rates and bullwhip effect terms.This work was supported by the European Commission Horizon 2020 project Diverfarming [grant number 728003].Campuzano Bolarin, F.; Mula, J.; Díaz-Madroñero Boluda, FM.; Legaz-Aparicio, Á. (2020). A rolling horizon simulation approach for managing demand with lead time variability. 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Reducing the impact of demand process variability within a multi-echelon supply chain

Forrester analyzed Supply Chain and the different levels existing in it, as well as the participant companies and the role played by each of them inside the chain as a global group, and observed that small variations in end item demand caused oscillations that are amplified throughout the chain. This phenomenon, called the Bullwhip effect, has detrimental consequences on inventory levels and on all kind of inventory costs that may affect the added value of the activities throughout the logistics chain and ultimately affect the Net Present Value of all the activities in the chain. There is a set of collaborative supply chain structures which reduce these harmful consequences within the supply chain. The study presented in this paper quantifies how collaborative supply chain structures reduce the Bullwhip effect in terms of demand variability and inventory cost