Random Demand Satisfaction in Unreliable Production–Inventory–Customer Systems

A method for calculating the probability of customer demand satisfaction in production–inventory–customer systems with Markovian machines, finite finished goods buffers, and random demand is developed. Using this method, the degradation of this probability as a function of demand variability is quantified. In addition, it is shown by examples that the probability of customer demand satisfaction depends primarily on the coefficient of variation, rather than on the complete distribution, of the demand.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/44112/1/10479_2004_Article_5254653.pd

Joint production, quality control and maintenance policies subject to quality-dependant demand

This thesis is a strive to find a proper solution, using the stochastic optimal control means for an unreliable production system with product quality control and quality-dependent demand. The system consists of a single machine producing a single product type (M1P1) subject to breakdowns and random repairs and must satisfy a non-constant rate of customer demand, which response to the quality of parts received. Since the machine produces with a rate of noncompliant products, an inspection of the products is made to reduce the number of bad parts that would deliver to the customer. It is done continuously and consists of controlling a fraction of the production. Approved products are put back on the production line, while bad products are discarded. The intended objective of this study is to provide optimal quality control and production policy, which maximize the net revenue consisting of the gross revenue, the cost of inventory, the cost of shortage, the cost of the inspection, the cost of maintenance and the cost of no-quality parts. Main decision variables are the sampling rate of the quality control system as well as the threshold of finished product inventory. The demand function reacts to the average outgoing quality level (AOQ) of finished products. In the third chapter of this study, preventive maintenance and dynamic pricing policies are added up to the optimal policy, cited above. To achieve the optimal points of the policy, which maximize our net production revenue, a simulation approach is implemented as an experimental design and its results were used in response surface methodology. To implement the experiment design (simulation approach) which thoroughly reflects model considerations such as its continuous nature and the variety, first, a continuous variable for the probability of defectiveness was introduced, functioning with the age of machine up until its next breakdown maintenance. Second, so as to reflect the effect of quality control process that results in Average Outgoing Quality rather than simple defectiveness possibility, this function (AOQ) was built based on instant behavior of mentioned function above as its independent variable. Third, due to the use of prospect theory assumptions in building a demand function that responds to the level of client delivered defectiveness (AOQ), a responsive continuous function was created for the demand, reacting to the level of product quality by determining it's needed per time amount. Finally. To illustrate the machine’s manufacturing policy based on Hedging Point, finished product inventory variable was introduced in the experiment design. In a nutshell, we have a production system that has been designed in a way that by raising its age (At), leads to more possibility of defectiveness and less demand in time units. This manner continuous up until the next maintenance action of the system, which restores all factors to their initial conditions. By use of the simulation approach of optimization an experiment is designed and implemented to control decision variables of the policy and maximize the objective function of average net revenue (ANR). Decision variables are statistically and practically in the matter of consideration such as finished product inventory threshold (Z), the proportion of inspection (F) and PM thresholds (Mk or Pk)

Supply chain flexibility in the special purpose vehicle industry.

Supply chains of the Special Purpose Vehicle (SPV) industry are complex and with many constraints. Since the SPV industry is a special field of operation, there is no classical supply chain strategy which is appropriate. It is possible to apply concepts of industries with similar requirements but there is a high loss of time and money because these classical concepts do not fit to the SPV industry. Even strategies of the conventional automobile industry cannot be transferred. Therefore, there is the need to develop a supply chain concept for companies of the SPV industry. As a first step, basic knowledge about supply chain management is provided. Based on this, special supply chain characteristics of the SPV industry are analyzed in detail. A profound research shows that the focus of the developed supply chain should be on flexibility. High supply chain flexibility addresses the specific difficulties related to the SPV industry. These are for example individual customer requirements and uncertain demand. Therefore appropriate flexibility methods are derived which are called variant, volume and time flexibility. For the implementation, several formulas and strategies are derived. This supply chain concept is a basic concept. It can be adapted to the environment of different SPV companies. For the application of the derived formulas, MATLAB codes are provided. These MATLAB scripts and functions are also used for a performance evaluation. Therefore, economic parameters, which are same important for all companies, are used. Thus, all improvements and strategies in this research are evaluated mathematically. A performance evaluation with realistic input values shows that the following savings are expected for the three flexibility types: · volume flexibility: 47% · variant flexibility: 42% · time flexibility: 42% A comprehensive example with all the flexibility types shows that overall savings of about 18% can be realized. This comprehensive example includes further new approaches like an asymmetrical flexibility and a method to order the optimal quantity at the posterior point of time which is explained and introduced. The savings due to the individual flexibility types, which are mentioned above, are related to costs and thus very high at first glance. Furthermore, these results depend on input variables, which reflect realistic examples. Thus, these values can be different in other example. They are however appropriate indicators to show that the new supply chain strategy for the SPV industry is profitable, reliable, stable and flexible. Thus, the new approach is a research contribution, which leads to clear benefits in reality

The Impact of Lean Six Sigma on the Overall Results of Companies

Lean Six Sigma represents a management approach for driving innovating processes inside a company in order to achieve superior results. It involves a practical analysis based on facts, aiming the innovation and growth, not only the efficiency of processes. It is a long term process of gradual and continuous improvement. The application of Lean Six Sigma in companies led to attaining superior financial performance by addressing new needs, by differentiating the products and services or by adjusting the business lines to new processes. Quality is more than making things without errors. It is about making a product or service meet the individual perception of a customer about the quality or value. Therefore, in what regards Lean Six Sigma, the concern is not only to "do the things right" but also to "do the right things right". We focus on the impact of implementing the Lean Six Sigma approach on companies, seeking for what changes and benefits it brings. The key elements it aims at are achieving the best quality, the lowest cost, getting the shortest lead-time, stressing on waste elimination. The requirements of a company for its implementation and the strategy to obtain the maximum practical outcome are investigated. Furthermore, we conduct a comparison analysis with the other methods of the total quality management and see why Lean Six Sigma is a more desirable approach.Lean Six Sigma, fact-based analysis, innovation, strategy, quality, gradual and continuous process.

The impact of supply chain complexity on manufacturing plant performance

This paper puts forth a model of supply chain complexity and empirically tests it using plant-level data from 209 plants across seven countries. The results show that upstream complexity, internal manufacturing complexity, and downstream complexity all have a negative impact on manufacturing plant performance. Furthermore, supply chain characteristics that drive dynamic complexity are shown to have a greater impact on performance than those that drive only detail complexity. In addition to providing a definition and empirical test of supply chain complexity, the study serves to link the systems complexity literature to the prescriptions found in the flexibility and lean production literatures. Finally, this research establishes a base from which to extend previous work linking operations strategy to organization design [Flynn, B.B., Flynn, E.J., 1999. Information-processing alternatives for coping with manufacturing environment complexity. Decision Sciences 30 (4), 1021–1052]

What it takes to design a supply chain resilient to major disruptions and recurrent interruptions

Global supply chains are more than ever under threat of major disruptions caused by devastating natural and man-made disasters as well as recurrent interruptions caused by variations in supply and demand. This paper presents an optimization model for designing a supply chain resilient to (1) supply/demand interruptions and (2) facility disruptions whose probability of occurrence and magnitude of impact can be mitigated through fortification investments. Numerical results and managerial insights obtained from model implementation are presented. Our analysis focuses on how supply chain design decisions are influenced by facility fortification strategies, a decision maker’s conservatism degree, demand fluctuations, supply capacity variations, and budgetary constraints. Finally, examining the performance of the proposed model using a Monte Carlo simulation method provides additional insights and practical implications

Modeling and analyzing a health care supply chain system

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.A Thesis presented on Improving and Redefining the Supply Chain in Healthcare, defining existing problems with current Supply Chain applications, and reviewing current applications and trends in the Supply Chain culture within the Manufacturing Industries and Healthcare Industries. Research of successful applications of new, and improvements to existing supply chain methodologies are presented. The concept of a future Supply Chain Management System, extending the boundaries of conventional Healthcare Supply Chain to include both conventional customer (Healthcare Materiel and Capital Assets Requestors and Distributors) and the nonconventional (the Healthcare Beneficiary), is presented in detail. Final discussions and conclusions of this concept are offered as a review of this concept for the reader of this manuscript