A Distributed Retail Beer Game for Decision Support System

AbstractA beer game is a simulation tool for the study of Supply Chain Management (SCM) issues used by the students of MIT. It has been augmented over the time to make it industry ready for decision making and risk management. Apart from smooth information and material flow among the distributed partners excess inventory is still an issue to control. In this paper, an attempt is made to improvise the Beer Game model to a Petri Net model for risk analysis and decision making. A successful simulation of the Petri Net model on efficient redistribution of stock towards inventory management is presented in this paper. The paper also establishes that the analysis is done in polynomial time

The dynamics of somatic exocytosis in monoaminergic neurons.

Some monoaminergic neurons can release neurotransmitters by exocytosis from their cell bodies. The amount of monoamine released by somatic exocytosis can be comparable to that released by synaptic exocytosis, though neither the underlying mechanisms nor the functional significance of somatic exocytosis are well understood. A detailed examination of these characteristics may provide new routes for therapeutic intervention in mood disorders, substance addiction, and neurodegenerative diseases. The relatively large size of the cell body provides a unique opportunity to understand the mechanism of this mode of neuronal exocytosis in microscopic detail. Here we used three photon and total internal reflection fluorescence microscopy to focus on the dynamics of the pre-exocytotic events and explore the nature of somatic vesicle storage, transport, and docking at the membrane of serotonergic neurons from raphe nuclei of the rat brain. We find that the vesicles (or unresolved vesicular clusters) are quiescent (mean square displacement, MSD ∼0.04 μm(2)/s) before depolarization, and they move minimally (<1 μm) from their locations over a time-scale of minutes. However, within minutes of depolarization, the vesicles become more dynamic (MSD ∼0.3 μm(2)/s), and display larger range (several μm) motions, though without any clear directionality. Docking and subsequent exocytosis at the membrane happen at a timescale (∼25 ms) that is slower than most synaptic exocytosis processes, but faster than almost all somatic exocytosis processes observed in endocrine cells. We conclude that, (A) depolarization causes de-tethering of the neurotransmitter vesicles from their storage locations, and this constitutes a critical event in somatic exocytosis; (B) their subsequent transport kinetics can be described by a process of constrained diffusion, and (C) the pre-exocytosis kinetics at the membrane is faster than most other somatic exocytosis processes reported so far

Modeling the Bullwhip Effect in a Multi-Stage Multi-Tier Retail Network by Generalized Stochastic Petri Nets

Bullwhip effect (BWE) refers to the accumulation of stock flowing up and down along the supply chain management (SCM). It reduces the operating efficiency of the chain and blocks the operating resources. Some of the common causes of BWE are demand order variations, long lead times, competence defects between supply chain links, lack of communication among links in the chain, etc. There have been efforts to overcome these issues. However, very little work has been reported based on formal representation and analysis of resource flow in the supply chain system. In this work, a novel framework is proposed using Generalized Stochastic Petri-net (GSPN) model towards handling this issue in a distributed scenario. The analysis on the stochastic nets allows identifying the bottlenecks in the supply chain echelons along with customer relationship management (CRM). This has been used to rebuild infrastructure with the end-objective of reducing the BWE

Modeling Demand Forecast Variance in a Distributed Supply Chain Network using Generalized Stochastic Petri Nets

There is a trade-off in Supply Chain Management Systems between efficiency and demand variability. When no variation occurs in consumer need, order cycle, product portfolios, and in distribution lead time, then the supply chain would be just a routine business process. Unfortunately, in practice this is not often the case. Thus, ranking demand variability is one of the prime challenges to reduce the safety stock without affecting the customer demand. This paper studies supply chain demand variability with multiple suppliers, manufacturers, distributors, wholesalers, retailers, and customers as tiers, and each stage as echelon that faces stochastic demand volatility. A Generalized Stochastic Petri-Net (GSPN) model is proposed in a distributed scenario to synchronize the response capabilities among the players in the chain, and to lower down the supplier demand variance with scheduled ordering policies. Maintaining a uniform inventory stock throughout the chain has two main effects: the bullwhip effect (BWE) will be negligible, and uncertainty in decision making at each echelon will be reduced substantially

Modeling demand forecast variance in a distributed supply chain network using generalized stochastic petri nets

There is a trade-off in Supply Chain Management Systems between efficiency and demand variability. When no variation occurs in consumer need, order cycle, product portfolios, and in distribution lead time, then the supply chain would be just a routine business process. Unfortunately, in practice this is not often the case. Thus, ranking demand variability is one of the prime challenges to reduce safety stock without affecting customer demand. This paper studies supply chain demand variability with multiple suppliers, manufacturers, distributors, wholesalers, retailers, and customers as tiers, and each stage as an echelon that faces stochastic demand volatility. A Generalized Stochastic Petri-Net (GSPN) model is proposed in a distributed scenario to synchronize the response capabilities among the players in the chain, and to lower down the supplier demand variance with scheduled ordering policies. Maintaining a uniform inventory stock throughout the chain has two main effects: the bullwhip effect (BWE) will be negligible, and uncertainty in decision making at each echelon will be reduced substantially

Modeling Demand Forecast Variance in a Distributed Supply Chain Network using Generalized Stochastic Petri Nets

There is a trade-off in Supply Chain Management Systems between efficiency and demand variability. When no variation occurs in consumer need, order cycle, product portfolios, and in distribution lead time, then the supply chain would be just a routine business process. Unfortunately, in practice this is not often the case. Thus, ranking demand variability is one of the prime challenges to reduce the safety stock without affecting the customer demand. This paper studies supply chain demand variability with multiple suppliers, manufacturers, distributors, wholesalers, retailers, and customers as tiers, and each stage as echelon that faces stochastic demand volatility. A Generalized Stochastic Petri-Net (GSPN) model is proposed in a distributed scenario to synchronize the response capabilities among the players in the chain, and to lower down the supplier demand variance with scheduled ordering policies. Maintaining a uniform inventory stock throughout the chain has two main effects: the bullwhip effect (BWE) will be negligible, and uncertainty in decision making at each echelon will be reduced substantially