Decision support framework for supply chain planning with flexible demand (original) (raw)
Related papers
2016
In this paper a mathematical model will be presented for the integrated planning of supply, production and distribution problem in a multi-level supply chain which consists of producer, warehouse and customer (retailer) in uncertainty of demand situation. The proposed model provides decision making on uncertain and varying markets with regards to capacity, supply and delivery flexibility. Demand is considered to be a random variable with normal distribution and market frequencies have been incorporated into the model within various scenarios. Planning perspective, in the proposed model, has been divided into a series of strategic decision making periods with them each includes a number of tactical decision making periods and time value of money have been inserted into the model with regard to interest rate. Due to model's complexity in large scales, to solve the model we deployed particle swarm meta-heuristic optimization algorithm.
Quantitative models for supply chain planning under uncertainty: a review
The International Journal of Advanced Manufacturing Technology, 2009
Managing uncertainty is a main challenge within supply chain management. Therefore, it is expected that those supply chain planning methods which do not include uncertainty obtain inferior results if compared with models that formalise it implicitly. This article presents a review of the literature related to supply chain planning methods under uncertainty. The main objective is to provide the reader with a starting point for modelling supply chain under uncertainty applying quantitative approaches. We have defined a taxonomy to classify models from 103 bibliographic references dated 1988-2007. Finally, some conclusions about the works analysed have been drawn and future lines of research have been identified. This work has been carried out in the framework of a project funded by the Spanish Ministry of Science and Technology entitled 'Hierarchical methodology in the context of uncertainty in the collaborative planning of a supply-distribution chain/network. Application to the ceramic sector'. Ref.
Proceedings of the 54th Hawaii International Conference on System Sciences, 2021
Public and private organizations cope with a lot of uncertainties when planning the future of their supply chains. Additionally, the network of stakeholders is now intensely interconnected and dynamic, revealing new collaboration opportunities at a tremendous pace. In such a context, organizations must rethink most of their supply chain planning decision support systems. This is the case regarding strategic supply chain capacity planning systems that should ensure that supply chains will have enough resources to profitably produce and deliver products on time, whatever hazards and disruptions. Unfortunately, most of the existing systems are unable to consider satisfactorily this new deal. To solve this issue, this paper develops a decision support system designed for making strategic supply chain capacity planning more dynamic to cope with hyperconnected and uncertain environments. To validate this decision support system, two industrial experiments have been conducted with two European pharmaceuticals and cosmetics companies.
Operations planning and flexibility in a supply chain
Production Planning and …, 2005
In dynamic competitive markets, the flexibility of manufacturing system networks such as supply chains (SCs) is particularly interesting. The SC flexibility considered in this paper takes into account two main aspects: the process flexibility of each SC firm and the logistics flexibility concerning the possible connections between suppliers, assemblers and markets. Different configurations of an SC are proposed, in correspondence to different degrees of the process and logistics flexibility. The effects of SC flexibility are then investigated on the operations planning performance of an SC subject to production capacity uncertainty and coping with demand volume and mix variability. In particular, an optimization model is defined to analyse the SC performance in every SC configuration. Managerial guidelines, supporting the management of selecting the appropriate degrees of flexibility and the corresponding SC configuration to be adopted, are finally obtained.
The paper presents simulation-based methodology for analysis and optimisation of multiechelon supply chain planning policies over the product life cycle. It is aimed to analyse an efficiency of a specific planning policy at the product life cycle phases and to optimise the cyclic planning policy at the product maturity phase. Specific software prototypes and applications are described in the paper. The presented research is funded by the ECLIPS Specific Targeted Research Project of the European Commission "Extended Collaborative Integrated Life Cycle Supply Chain Planning System".
A simulation based optimization approach to supply chain management under demand uncertainty
Cost effective supply chain management under various market, logistics and production uncertainties is a critical issue for companies in the chemical process industry. Uncertainties in the supply chain usually increase the variance of profits (or costs) to the company, increasing the likelihood of decreased profit. Demand uncertainty, in particular, is an important factor to be considered in the supply chain design and operations. To hedge against demand uncertainty, safety stock levels are commonly introduced in supply chain operations as well as in supply chain design. Although there exists a large body of literature on estimating safety stock levels based on traditional inventory theory, this literature does not provide an effective methodology that can address the complexity of real CPI supply chains and that can impact the current practice in their design, planning and scheduling. In this paper, we propose the use of deterministic planning and scheduling models which incorporate safety stock levels as a means of accommodating demand uncertainties in routine operation. The problem of determining the safety stock level to use to meet a desired level of customer satisfaction is addressed using a simulation based optimization approach. An industrial-scale case problem is presented to demonstrate the utility of the proposed approach.
A review of modelling approaches for supply chain planning under uncertainty
Service Systems and Service …, 2011
Since 1959 in which one of the earliest attempts to address the problem of developing a coordinated link in a supply chain (SC) was performed by [1], managing SC performance has been a main challenge among enterprises. Supply chain planning (SCP), as one of the most important processes within the supply chain management (SCM) concept, has a great impact on firms' success or failure. SCP decision has been greatly influenced by the presence of uncertainty from the intricate nature and dynamic relationship among various stages involved in the SC network. This paper aims to present an extensive review of the existing literature to acquire a deep understanding of modelling approaches used in the area of SCP under uncertainty. The research main objective is to provide a classification framework based on the following elements: problem types, sources of uncertainty, performance measures, and modelling approaches that were exploited by previous researchers. We have conducted a survey of various journal papers dated from 1993 to 2012. In conclusion, some guidelines regarding future areas of research have been identified.
Foundations of Computing and Decision Sciences
With the globalization of markets and increasing competition in global markets, the attempts of organizations to survive in this market has increased and has resulted in the emergence of the philosophy of Supply Chain Management. There is uncertainty in the reliability of supply chain facilities for reasons such as natural disasters, terrorist attacks, labor errors, and weather conditions. Therefore, when making strategic decisions, the system will continue to operate with minimal damage. Over the course of this study, the uncertainty of supplier layers in the supply chain has been modeled. To meet that aim, the issue of supply chain, including producers, warehouses, suppliers and consumers are considered. To calculate the cost of breakdowns due to the non-functioning of distributors, the scenario-building method has been utilized. Finally, the desired model is solved with Gomez software and the results are presented. The result of the study demonstrate the efficiency of this model ...
Simulation and optimization of supply chains: alternative or complementary approaches?
OR Spectrum, 2009
Discrete-event simulation and (mixed-integer) linear programming are widely used for supply chain planning. We present a general framework to support the operational decisions for supply chain networks using a combination of an optimization model and discrete-event simulation. The simulation model includes nonlinear and stochastic elements, whereas the optimization model represents a simplified version. Based on initial simulation runs cost parameters, production, and transportation times are estimated for the optimization model. The solution of the optimization model is translated into decision rules for the discrete-event simulation. This procedure is applied iteratively until the difference between subsequent solutions is small enough. This method is applied successfully to several test examples and is shown to deliver competitive results much faster compared to conventional mixed-integer models in a stochastic environment. It provides the possibility to model and solve more realistic problems (incorporating dynamism and uncertainty) in an acceptable way. The limitations of this approach are given as well.