MODELING SCRAP SOURCING DECISIONS GIVEN UNCERTAIN DEMAND (original) (raw)
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Optimal production-shipment decisions for the finite production rate model with scrap
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This paper is concerned with the decision-making on the optimal production batch size and optimal number of shipments for a finite production rate model with random scrap rate. The classic finite production rate (FPR) model assumes a continuous inventory issuing policy for satisfying product demand and perfect quality for all items produced. However, in a real life vendor-buyer integrated production-inventory system, a multiple shipment policy is practically used in lieu of the continuous issuing policy, and it is inevitable to generate defective items during a production run. All nonconforming items produced are assumed to be scrap, and the finished (perfect quality) products can only be delivered to customers if the whole lot is quality assured at the end of the production run. The fixed-quantity multiple instalments of the finished batch are delivered to customers at a fixed interval of time. Mathematical modelling is employed and the renewal reward theorem is used to cope with the variable production cycle length. The long-run average cost for the proposed model is derived, and its convexity is proved by the use of the Hessian matrix equations. A closed-form optimal production-shipment policy for such an imperfect FPR model is obtained and a special case is discussed. Finally, a numerical example is provided to demonstrate the model's practical usage.
Operational Strategies for Increasing Secondary Materials in Metals Production Under Uncertainty
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Increased use of secondary raw materials in metals production offers several benefits including reduced cost and lowered energy burden. The lower cost of secondary or scrap materials is accompanied by an increased uncertainty in elemental composition. This increased uncertainty for different scraps, if not managed well, results in increased risk that the elemental concentrations in the final products fall outside customer specifications. Previous results show that incorporating this uncertainty explicitly into batch planning can modify the potential use of scrap materials while managing risk. Chance constrained formulations provide one approach to uncertainty-aware batch planning; however typical formulations assume normal distributions to represent the compositional uncertainty of the materials. Compositional variation in scrap materials has been shown to have a skewed distribution and, therefore, the performance of these models, in terms of their ability to provide effective planning, may then be heavily influenced by the structure of the compositional data used. To address this issue, this work developed several approximations for skewed distributional forms within chance constrained formulations. We explored a lognormal approximation based on Fenton's method; a convex approximation based on Bernstein inequalities; and a linear approximation using fuzzy set theory. Each of these methods was formulated 2 and case studies executed using compositional data from an aluminum remelter. Results indicate that the relationship between the underlying structure/distribution of the compositional data and how these distributions are formulated in batch planning can modify the use of secondary raw materials.
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Due to legislative requirements, environmental concerns, and market image, the disposition of end-of-life e-scrap is attracting tremendous attention in many parts of the world today. Effective management of returned used product flows can have a great impact on the profitability and resulting financial viability of associated e-scrap reverse production systems. However, designing efficient e-scrap reverse production systems is complicated by the high degree of uncertainty surrounding several key factors. Very few examples of this complex design problem are documented in the academic literature. This paper contributes as analysis of a new, large-scale application that designs an infrastructure to process used televisions, monitors, and computer central processing units (CPUs) in the state of Georgia in the U.S. The case study employs a scenario-based robust optimization model for supporting strategic e-scrap reverse production infrastructure design decisions under uncertainty. A mixed integer linear programming (MILP) model is used to maximize the system net profit for specified deterministic parameter values in each scenario, and then a min-max robust optimization methodology finds a robust solution for all of the scenarios.
Sourcing Decision in a Multi-Period Model under Demand and Supply Uncertainty
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Determining the optimum number of suppliers and the optimum quantities to order from each of them is a critical problem for any supply chain. This paper has mathematically arrived at conditions to identify the appropriate sourcing strategy in a multi-period scenario for a stochastic supply and a stochastic demand environment. It has also obtained the total order quantity, optimum number of suppliers and order allocation to each of the suppliers under both uncertain demand and supply in the multiple-period context. Through a numerical analysis this work could bring interesting managerial insights about the sourcing strategies.
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Operational uncertainties create disincentives for use of recycled materials in metal alloy production. One that greatly influences remelter batch optimization is variation in the raw material composition, particularly for secondary materials. Currently, to accommodate compositional variation, firms commonly set production targets well inside the window of compositional specification required for performance reasons. Window narrowing, while effective, does not make use of statistical sampling data, leading to sub-optimal usage of recycled materials. This paper explores the use of a chance-constrained optimization method, which allows explicit consideration of statistical information on composition. The framework and a case study of cast and wrought production with available scrap materials are presented. Results show that it is possible to increase the use of recycled material without compromising the likelihood of batch errors, when using this method compared to conventional window narrowing. This benefit of the chance-constrained method grows with increase in compositional uncertainty and is driven by scrap portfolio diversification.
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Inventory is crucial in maintaining a smooth production process and meeting consumer demand for manufacturing companies. This research focuses on production problems involving defects, rework, and scrap items in stochastic demand. This research aims to develop a production-availability model by minimizing the expectation of total cost (ETC). The model includes four main decision variables, namely production quantity (Q), safety factor (k), production rate (P), and rework rate (P1). This research uses the Aquila optimizer algorithm to optimize the objective function. It compares with the heuristic procedure and Harris Hawk optimization algorithm. The results showed that the Aquila optimizer algorithm successfully optimized the production-availability problem. A comparison between algorithms indicates that the Aquila optimizer algorithm performs equivalently to the Harris Hawk optimization algorithm and outperforms the heuristic procedure. Sensitivity analysis shows that increasing demand uncertainty increases ETC and k. At the same time, it can decrease Q.
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Scrap management is an important aspect of any manufacturing process, as it can significantly impact costs, inventory, and product quality. SAP, one of the leading enterprise software providers, offers various solutions for managing scrap, including assembly scrap, component scrap, and operation scrap. This paper provides a comprehensive review of these three types of scrap handling in SAP and analyzes their effects on manufacturing processes. The paper begins by outlining the differences between assembly scrap, component scrap, and operation scrap in SAP. It then examines various strategies for handling these types of scrap, including manual data entry, automated data collection, and integration with inventory management and production planning. The paper also discusses the benefits and drawbacks of each strategy, such as increased data accuracy, reduced inventory costs, and improved production planning. The paper draws on published research articles, case studies, and industry reports to analyse the effects of using these strategies. The analysis focuses on the impact of scrap handling on key performance indicators (KPIs) such as cost, quality, and inventory levels. The paper highlights how different strategies can impact these KPIs and identifies best practices for optimizing scrap management in SAP. Overall, this review paper provides valuable insights into the various ways to handle scrap in SAP and the effects of using these strategies. By examining assembly scrap, component scrap, and operation scrap handling in detail, this paper serves as a useful resource for companies seeking to improve their scrap management capabilities in SAP.
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This paper presents a risk-based approach for quality control planning of complex discrete manufacturing processes, to prevent massive scraps to occur. An analytical model is developed to optimize the quality control plan (QCP) subject to inspection capacity limitation and risk exposure objectives. The problem is then formulated as a constrained capacity allocation problem. A dedicated heuristic that solves a simplified instance of an industrial case study, from semiconductor manufacturing, is presented to provide insights into the applicability and the operational use of the approach and its potential gains in terms of risk exposure reduction. The main advancement resulting from this work is the proposal of a model of quality control allocation and an understandable algorithm to prevent the production of excessive amounts of scrap. The industrial illustration shows a decrease in potential losses by a factor of 3.
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