Supplier and retailer coordination under stochastic price-dependent demand and fast moving items

We consider a centralized supply chain system consisting of a one supplier and one retailer. The customers’ demand is a compound Poisson process with price-dependent intensity and continuous batch size distribution. The intensity of the customers’ arrivals is assumed to be sufficiently high to use a diffusion approximation of the demand process. We assume that the supplier has complete information about the rational retailer’s behavior in the framework of the newsvendor problem. The objective is to find a joint pricing and ordering policy so as to maximize the retailer’s expected profit and supplier’s profit. The equations for the optimal prices main parts are obtained and the example of the price-intensity dependence is considered

Traditional Inventory Models in an E-Retailing Setting: A Two-Stage Serial System with Space Constraints

In an e-retailing setting, the efficient utilization of inventory, storage space, and labor is paramount to achieving high levels of customer service and company profits. To optimize the storage space and labor, a retailer will split the warehouse into two storage regions with different densities. One region is for picking customer orders and the other to hold reserve stock. As a consequence, the inventory system for the warehouse is a multi-item two-stage, serial system. We investigate the problem when demand is stochastic and the objective is to minimize the total expected average cost under some space constraints. We generate an approximate formulation and solution procedure for a periodic review, nested ordering policy, and provide managerial insights on the trade-offs. In addition, we extend the formulation to account for shipping delays and advanced order information.Singapore-MIT Alliance (SMA

Analysis of the revenue-sharing contract under different power structures with application in the biodiesel niche market

Master'sMASTER OF ENGINEERIN

Optimal control of serial, multi-echelon inventory/production systems with periodic batching

We consider a single-item, periodic-review, serial, multi-echelon inventory system, with linear inventory holding and penalty costs. In order to facilitate shipment consolidation and capacity planning, we assume the system has implemented periodic batching: each stage is allowed to order at given equidistant times. Further, for each stage except the most downstream one, the reorder interval is assumed to be an integer multiple of the reorder interval of the next downstream stage. This reflects the fact that the further upstream in a supply chain, the higher setup times and costs tend to be, and thus stronger batching is desired. Our model with periodic batching is a direct generalization of the serial, multi-echelon model of Clark and Scarf (1960). For this generalized model, we prove the optimality of basestock policies, we derive Newsboy-type characterizations for the optimal basestock levels, and we describe an efficient exact solution procedure for the case with mixed Erlang demands. Finally, we present extensions to assembly systems and to systems with a modified fill rate constraint instead of backorder costs. Subject classification: Inventory/Production: Multi-echelon, stochastic demand, periodic batching, optimal policies.

The extension and exploitation of the inventory and order based production control system archetype from 1982 to 2015

In 1994, through classic control theory, John, Naim and Towill developed the ‘Automatic Pipeline, Inventory and Order-based Production Control System’ (APIOBPCS) which extended the original IOBPCS archetype developed by Towill in 1982 ─ well-recognised as a base framework for a production planning and control system. Due to the prevalence of the two original models in the last three decades in the academic and industrial communities, this paper aims to systematically review how the IOBPCS archetypes have been adopted, exploited and adapted to study the dynamics of individual production planning and control systems and whole supply chains. Using various databases such as Scopus, Web of Science, Google Scholar (111 papers), we found that the IOBPCS archetypes have been studied regarding the a) modification of four inherent policies related to forecasting, inventory, lead-time and pipeline to create a ‘family’ of models, b) adoption of the IOBPCS ‘family’ to reduce supply chain dynamics, and in particular bullwhip, c) extension of the IOBPCS family to represent different supply chain scenarios such as order-book based production control and closed-loop processes. Simulation is the most popular method adopted by researchers and the number of works based on discrete time based methods is greater than those utilising continuous time approaches. Most studies are conceptual with limited practical applications described. Future research needs to focus on cost, flexibility and sustainability in the context of supply chain dynamics and, although there are a few existing studies, more analytical approaches are required to gain robust insights into the influence of nonlinear elements on supply chain behaviour. Also, empirical exploitation of the existing models is recommended

Forecasting Demand for Optimal Inventory with Long Lead Times: An Automotive Aftermarket Case Study

Accuracy in predicting customer demand is essential to building an economic inventory policy under periodic review, long lead-time, and a target fill rate. This study uses inventory and customer service level as a stock control metric to evaluate the forecast accuracy of different simple to more complex predictive analytical techniques. We show how traditional forecast error measures are inappropriate for inventory control, despite their consistent usage in many studies, by evaluating demand forecast performance dynamically with customer service level as a stock control metric that includes inventory holdings costs, stock out costs, and fill rate service levels. A second contribution includes evaluating the utility of introducing more complexity into the forecasting process for an automotive aftermarket parts manufacturer and the superior inventory control results using the Prais-Winsten, an econometric method, for non-intermittent demand forecasting with long-lead times. This study will add to the limited case study research on demand forecasting under long lead times using stock control metrics, dynamic model updating, and the Prais-Winsten method for inventory control

Modelos logísticos estocásticos aplicados a la cadena de suministro: una revisión de la literatura

Context: The analysis of the complexity of the systems involves the evolution of the models that representation of reality, logistics has advanced from a business context to the supply chain, basic models of logistics with deterministic parameters must go represent real behavior, stochastic. In this context, the combination of inventory, location and routing models with a stochastic approach applied to supply chains appears. Method: A systematic review of the literature was developed in the bibliographic databases, ScienceDirect, ScholarGoogle, SpringerLink, Scopus, SemanticScholar, ResearchGate and Scielo, of the 72 referenced articles, 65 % between 2015 and 2019. Results: From the models identified and described, a taxonomy of the models is proposed and classified into 4 kinds, three dyadic models Location Inventory Problem (LIP), Inventory Routing Problem (IRP), Location Routing problem (LRP) and a triadic model Location Inventory Routing Problem (LIRP). The stochastic parameters used in the models, the types of models, the solution methods, the contemplated objective functions, and the number of echelons in the supply chain are established, from which taxonomies of the different types of models are proposed. Lines of work for future research is presented. Conclusions: The evolution from deterministic to stochastic models represents an increase in complexity which forces the development of new solution methods with ability to find feasible solutions. The development of models with news measurements of performance as environmental, social and humanitarian have been of recent interest. In the last period, triadic multi-product and multi-period models take on relevance.Contexto: El análisis de la complejidad de los sistemas conlleva la evolución de los modelos de representación de la realidad, la logística ha avanzado de un contexto empresarial a la cadena de suministro, los modelos básicos de logística con parámetros determinísticos requieren representar el comportamiento real estocástico. En este sentido, aparecen la combinación de los modelos de inventario, la localización y el ruteo con enfoque estocástico aplicados a cadenas de suministro. Método: Se desarrolló una revisión sistemática de la literatura en las bases de datos bibliográficas ScienceDirect, ScholarGoogle, SpringerLink, Scopus, SemanticScholar y Scielo, así como en ResearchGate. De los 79 artículos referenciados, el 65 % comprenden entre el 2015 y 2019. Resultados: Se identifican y describen los modelos, a partir de lo cual se propone una taxonomía en cuatro combinaciones, tres de modelos diádicos: LIP, IRP, LRP y un modelo tríadico: LIRP. Se identifican los parámetros estocásticos utilizados en los modelos, los tipos de modelos, los métodos de solución, las funciones objetivo contempladas y el número de eslabones de la cadena contemplados, a partir de los cuales se proponen taxonomías de los diferentes tipos de modelos. Por último, se presentan líneas de trabajo para futuras investigaciones. Conclusiones: La evolución de modelos determinísticos a estocásticos representa un incremento en la complejidad, lo que obliga a desarrollar nuevos métodos de solución con capacidad de encontrar soluciones factibles. Ha sido de reciente interés el desarrollo de modelos y problemas con medidas de desempeño ambiental, social y riesgo humanitario, en el último periodo toman relevancia modelos tríadicos multiproducto y multiperiodo

Properties of the Periodic Review (R, T) Inventory Control Policy for Stationary, Stochastic Demand

This paper compares the commonly used periodic review, replenishment interval, order-up-to (R, T ) policy to the continuous review, reorder point, order quantity, (Q, r) model. We show that long-run average cost function for the single-product (R, T ) policy has a structure similar to that of the (Q, r) model. Consequently, many of the useful properties of the latter model are applicable. In particular, the optimal cost is insensitive to the choice of the replenishment interval, T, provided the optimal order-up-to level, R, corresponding to T is used. For instance, a suboptimal T obtained from a deterministic analysis increases costs by no more than 6.125%. For continuous demand, we analytically prove that use of a (R, T)policy instead of the optimal policy increases costs by at most 41.42% in the worst case. Computational experiments on Poisson demand demonstrate that the average-case relative error of using a (R,T)policy is under 7.5%. This relative error is lower when the demand rate and leadtime are high and the fixed order costs are either very low or very high. When coordination of order placement epochs is desirable, the (R,T) policy may sometimes be preferred to the (Q, r) policy. In this context, we illustrate application of our single-product results to more complex systems. In particular, we show that a simple power-of-two, (R,T) based heuristic for the stochastic multiproduct joint replenishment problem has a worst-case performance guarantee of 1.5. A similar result is explored for a special case of a two-echelon serial inventory systeminventory/production, joint replenishment, worst-case analysis