Optimized Production Technology Research Articles

Production planning and control in multi-stage assembly systems: an assessment of Kanban, MRP, OPT (DBR) and DDMRP by simulation

Multi-stage assembly systems where the demand for components depends on the market-driven demand for end products, are commonly encountered in practice. Production Planning and Control (PPC) systems for this production context include Kanban, Materials Requirement Planning (MRP), Optimised Production Technology (OPT), and Demand Driven MRP (DDMRP). All four of these PPC systems are widely applied in practice and literature abounds on each of these systems. Yet, studies comparing these systems are scarce and remain largely inconclusive. In response, this study uses simulation to assess the performance of all four PPC systems under different levels of bottleneck severity and due date tightness. Results show that MRP performs the worst, which can be explained by the enforcement of production start dates. Meanwhile, Kanban and DDMRP perform the best if there is no bottleneck. If there is a bottleneck then DDMRP and OPT perform the best, with DDMRP realising lower inventory levels. If there is a severe bottleneck, then the performance results for DDMRP and OPT converge. This identification of contingency factors not only resolves some of the inconsistencies in the literature but also has important implications for the applicability of these four PPC systems in practice.

Phenotypic and molecular insights into heat tolerance of formulated cells as active ingredients of fungal insecticides.

Formulated conidia of insect-pathogenic fungi, such as Beauveria and Metarhizium, serve as the active ingredients of fungal insecticides but are highly sensitive to persistent high temperatures (32-35°C) that can be beyond their upper thermal limits especially in tropical areas and during summer months. Fungal heat tolerance and inter- or intra-specific variability are critical factors and limitations to field applications of fungal pesticides during seasons favoring outbreaks of pest populations. The past decades have witnessed tremendous advances in improving fungal pesticides through selection of heat-tolerant strains from natural isolates, improvements and innovations in terms of solid-state fermentation technologies for the production of more heat-tolerant conidia, and the use of genetic engineering of candidate strains for enhancing heat tolerance. More recently, with the entry into a post-genomic era, a large number of signaling and effector genes have been characterized as important sustainers of heat tolerance in both Beauveria and Metarhizium, which represent the main species used as fungal pesticides worldwide. This review focuses on recent advances and provides an overview into the broad molecular basis of fungal heat tolerance and its multiple regulatory pathways. Emphases are placed on approaches for screening of heat-tolerant strains, methods for optimizing conidial quality linked to virulence and heat tolerance particularly involving cell wall architecture and optimized trehalose/mannitol contents, and how molecular determinants can be exploited for genetic improvement of heat tolerance and pest-control potential. Examples of fungal pesticides with different host spectra and their appropriateness for use in apiculture are given. KEY POINTS: • Heat tolerance is critical for field stability and efficacy of fungal insecticides. • Inter- and intra-specific variability exists in insect-pathogenic fungi. • Optimized production technology and biotechnology can improve heat tolerance. • Fungal heat tolerance is orchestrated by multiple molecular pathways.

Governing the dynamics of multi-stage production systems subject to learning and forgetting effects: A simulation study

Managing production systems where production rates change over time due to learning and forgetting effects poses a major challenge to researchers and practitioners alike. This task becomes especially difficult if learning and forgetting effects interact across different stages in multi-stage production systems as rigid production management rules are unable to capture the dynamic character of constantly changing production rates. In a comprehensive simulation study, this paper first investigates to which extent typical key performance indicators (KPIs), such as the number of setups, in-process inventory, or cycle time, are affected by learning and forgetting effects in serial multi-stage production systems. The paper then analyses which parameters of such production systems are the main drivers of these KPIs when learning and forgetting occur. Lastly, it evaluates how flexible production control based on Goldratt’s Optimised Production Technology can maximise the benefits learning offers in such systems. The results of the paper indicate that learning and forgetting only have a minor influence on the number of setups in serial multi-stage production systems. The influence of learning and forgetting on in-process inventory and cycle time, in contrast, is significant, but ambiguous in case of in-process inventory. The proposed buffer management rules are shown to effectively counteract this ambiguity.

Ethanol production technology by fermentation form fiber residues.

We studied the effects of various factors on the yield of ethanol from enzymatic hydrolysate of Tara fiber residues by the single factor experiment,and then optimized ethanol production technology by orthogonal experiments. The optimum conditions were the ratio of sodium alginate-yeast of 1 ∶ 2 g · g~(- 1),substrate sugar concentration of 30%,the amount of yeast of 1. 5 g,and fermentation time of 120 h,with the yield of ethanol of 83. 68%.

Measuring total-factor CO2 emission performance and technology gaps using a non-radial directional distance function: A modified approach

Implementing the “common but differentiated responsibilities” principle for CO2 mitigation requires an understanding of CO2 emission performance and technology gaps in different countries. This paper uses a meta-frontier and non-radial directional distance function to propose an alternative three stage approach to measure total-factor CO2 emission performance and technology gaps. The first stage calculates the CO2 emission performance of the group frontier. The second stage uses these results to calculate the technology gap ratio. The third stage integrates data from the first two stages to calculate CO2 emission performance under the meta-frontier. This approach is easier to apply than other approaches in the literature, and effectively avoids the phenomenon of the technology gap ratio being greater than unity. The proposed approach can also decompose CO2 emission performance loss into two categories: management inefficiency and technical gaps. To demonstrate the method, an empirical analysis using data for 54 countries was conducted. The study highlighted three main findings. First, upper-middle income countries did not perform as well as high income countries and lower-middle income countries. Second, high income countries generally enjoy optimized production technology, whereas the lower-middle income countries generally had the lowest technological levels. Third, both management inefficiency and technical gaps negatively impacted CO2 emission performance, but management inefficiency played a dominant role.

Applying Mendelian rules in rapeseed (Brassica napus) breeding

Rapeseed is one of the most important sources of edible oil, raw material for industry, as well as feed. The yield and quality of rapeseed have significantly been improved in recent decades as a result of intensive breeding and optimized production technology. The application of Mendel's rules in introducing monogenic traits has also contributed to success in rapeseed breeding. Rule 1, which refers to the uniformity of F1 generation, is now the basis of widespread development of rapeseed hybrids. Rule 2, dealing with genetic segregation in the F2 generation, is the basis for understanding the process of breeding lines. Rule 3, regarding the independent segregation of genes and traits, while exempting linked traits, is the basis of combining different desirable properties by selection. In the last few decades, the systematic use of Mendel's rules has contributed to the improvement of many properties of rapeseed, including tolerance to biotic and abiotic stress, yield and seed quality. Particular progress has been made in breeding for resistance to diseases, including the identification of molecular markers for marker-assisted selection. The next objective of rapeseed breeding is to create varieties with improved tolerance to environmental stress (e.g. frost, heat, and drought). Based on Mendel's rules, classical breeding methods and the latest developments in the field of molecular genetics and breeding, future progress is expected in the field of rapeseed breeding with an emphasis on polygenic, quantitative traits such as biomass, seed, and oil yield.

Economic Manufacturing Quantity and Its Integrating Implications

Several contradictions are noted among the Economic Order Quantity (EOQ), Just‐In‐Time (JIT), and Optimized Production Technology (OPT) approaches and the economic framework for profit maximization. A fundamental model referred to as the Economic Manufacturing Quantity (EMO) is developed and examined for its integrating implications for the three approaches. An implication for the classic EOQ approach is that the balance between setup and inventory carrying costs is valid when a production facility is operating at or below a certain critical level but not when operating above that level. An implication for the JIT approach is that one must reduce setup cost at non‐bottlenecks and setup time at bottlenecks to reduce inventory. An implication for the OPT approach is that trade‐offs between setup and inventory carrying costs may indeed be ignored while determining process batch sizes, provided each facility in a production system is operating at or above Its critical level. Economic theoretic analysis of the EMO model provides a basis for unification of JIT which advocates stability in operating level as a key to improved productivity and quality, and OPT that advocates maximizing operating level with resultant emphasis on bottlenecks as a key to increased profits. This unifying basis states that a profit‐maximizing production facility or system will operate at the full and stable level as long as market demand remains relatively sensitive to price and operating at the full (maximum) level provides positive unit contribution.

The technology gap of Chinese regions' energy efficiency and spatial convergence—Based on the hybrid meta-frontier data envelopment analysis

This paper applies the hybrid meta-frontier data envelopment analysis model to estimate the energy efficiency of the eastern, central, western, and northeastern regions in China as well as all Chinese provinces during 2003–2012. We further compare the energy efficiency under the regional frontier and the meta-frontier for different provinces and then examine the convergence situation among these four regions in China as well as the national energy efficiency. The results show the evidence of big gaps in the energy efficiency between the provinces under both the regional frontier and the national meta-frontier. In general, the highest energy efficiency lies in eastern China, followed by western China and then northeastern China, with the lowest in central China. Using the technology gap ratio, the findings present that eastern China achieves the national potential optimized production technology, whereas the central, western, and northeastern regions are still some distance away from their own national potential optimized production technology with considerable room for improvement. As for the variation trends of energy efficiency, those of the central and northeastern regions have a convergence tendency, whereas the energy efficiencies of eastern and western China show the characteristics of slight divergence. Moreover, the convergence tendency of energy efficiency for the whole country has not yet been formed. Based on the above research conclusions, we propose a number of energy policy recommendations for China's government in order to improve energy efficiency.

Applying the Theory of Constraints in the South African Coal Supply Chain

The study explored possible areas for applying the Theory of Constraints in the South African coal mining industry supply chain with the aim of improving effectiveness, efficiency and profitability (throughput). The origin of the Theory of Constraints was explored including various aspects of its application such as the system theory, capacity management, thinking/approach and throughput. Various types of plant classifications were reviewed to ascertain how coal supply chain would be classified. The five step processes pioneered by Goldratt for identifying and alleviating constraints were explored as would be applied in the coal supply chain. The other areas studied included constraint based approach, coal infrastructure, demand management and optimised production technology. The Theory’s intended impacts on the coal mining industry supply chain collaboration were also observed. DOI: 10.5901/mjss.2014.v5n9p131

When less is better: Insights from the product mix dilemma from the Theory of Constraints perspective

Perhaps due to its origins in a production scheduling software called Optimised Production Technology (OPT), plus the idea of focusing on system constraints, many believe that the Theory of Constraints (TOC) has a vocation for optimal solutions. Those who assess TOC according to this perspective indicate that it guarantees an optimal solution only in certain circumstances. In opposition to this view and founded on a numeric example of a production mix problem, this paper shows, by means of TOC assumptions, why the TOC should not be compared to methods intended to seek optimal or the best solutions, but rather sufficiently good solutions, possible in non-deterministic environments. Moreover, we extend the range of relevant literature on product mix decision by introducing a heuristic based on the uniquely identified work that aims at achieving feasible solutions according to the TOC point of view. The heuristic proposed is tested on 100 production mix problems and the results are compared with the responses obtained with the use of Integer Linear Programming. The results show that the heuristic gives good results on average, but performance falls sharply in some situations.

Measurement of Manufacturing System Complexity Based on Key Resources

In the previous complexity measurement models of manufacturing system (MS), all the resources in enterprise were taken into account, so the result is that the models required large amount of information, which makes the model difficult to expand in practice. As the manufacturing system efficiency is determined by key resources. Only improving the key resources production efficiency can the MS efficiency be improved. So the manufacturing system complexity has to been measured based on the key resources firstly. The Optimized Production Technology is applied to the complexity measurement of MS and a new complexity measurement was proposed. With the information entropy, a static complexity measurement model and a dynamic model are built based on key resources. A case was used to verify the feasibility of the developed model. The advantages and shortcomings of the manufacturing system's complexity measurement were deduced based on key resources at last.

Applying different control approaches for resources with high and low utilisation: a case study of the production of complex products with stochastic processing times

Research on dispatching rules has mainly focused upon deterministic job shop situations or small assembly environments and has ignored operational factors. Recent work has examined the relative performance of dispatching rules in companies that produce several families of complex products with stochastic processing times. Previous work has used common control policies for all resources. In practice, it is common for there to be a wide variation in the utilisation of machines. The Optimised Production Technology (OPT) philosophy suggests that particular attention should be paid to bottleneck resources. This paper investigates the effect of applying different manufacturing control approaches for resources with high and low utilisation. The work uses data obtained from an Engineer-To-Order (ETO) capital goods company that produces complex products in low volume. Processing times are assumed to be normally distributed. A range of scenarios are considered in which an increasing proportion of resources are considered to be 'bottlenecks'. The results show that the mean tardiness of products decreases significantly when the highly utilised machines are carefully controlled. However, there is only marginal benefit in carefully controlling the other machines.

Major Problems and Improvement Approaches in Indian Batch Manufacturing Firms: Inferences from an Exploratory Study

Manufacturing plays an important rule in the economic and industrial development of a country. Effective planning and control of manufacturing is the key to developing a good production base. The objectives of this paper are to (i) highlight the problems of manufacturing planning and control (MPC) in batch manufacturing firms in India (ii) identify improvement approaches considered by them to reduce effectiveness of these problems. A questionnaire was developed and distributed, and the respondent firms were classified into four categories of batch manufacturing firms, namely, mechanical, electrical, electronic and chemical. The three most severe problems of MPC in Indian batch manufacturing firms found as the lack of timely availability of raw materials, fluctuations in demand and meeting the due date of delivery. Among generic approaches, materials requirement planning (MRP) is popular, just in time (JIT) is being implemented and optimized production technology (OPT) is almost unknown. Among the specific approaches considered by them are vendor development, predictive/preventive maintenance programs, total quality management, and technology including new processes and equipment.

Optimum production schedule and profit maximisation using the concept: theory of constraints

The Theory of Constraints (TOC) is an example of a management philosophy built upon a limited number of assumptions and designed to provide a process of continuous ongoing improvement. The assumption forming one foundation of TOC is that a system's outputs are determined by its constraints. The assumptions forming another foundation are new definitions for throughput, inventory and operating expense. These definitions are designed to support the goal of the organisation, which, according to Goldratt, is to make money. TOC, previously referred to as Optimized Production Technology (OPT), is a production control methodology that maximises profits in a plant with a demonstrated bottleneck. The process used by TOC to determine product mix that will maximise profitability is a very simple series of steps. In this article, a case study from an industry is considered to demonstrate how the application of concepts of TOC will maximise profit for an organisation. This paper further explains how TOC plays a vital role in increasing performance through a limited number of assumptions designed to provide a continuous process of improvement, as emphasised in Total Quality Management (TQM). This paper also shows the steps involved in solving a typical TOC problem along with optimisation of resources for increased demand conditions. This work has been carried out on an IBM/PC compatible system.

Conditions for the successful implementation of finite capacity/MRPII hybrid control systems

Manufacturing resource planning II (MRPII) is the most widely employed methodology for production planning and control. Many authors, however, have stated that the fundamental basis of MRPII is flawed: that is to say, scheduling is based on fixed manufacturing lead times/lot sizes with infinite capacity. There has also been great interest in alternative methodologies such as optimized production technology (OPT) which plans to finite capacity. As a result, there are now several software houses who offer finite capacity scheduling (FCS) tools designed to operate alongside MRPII packages. These provide detailed finite capacity planning while retaining the database infrastructure of MRPII. Such hybrids claim to give the benefits of FCS to established MRPII users without the need for re-implementation. This paper describes the operation of such hybrids. Based on a case study drawn from the automotive components industry, it discusses the pitfalls that might be experienced without careful consideration of planning methods in relation to Bill of Material (BOM) structures. In particular, it shows that for a hybrid to be successful, it is necessary to consider the system as a whole, rather than considering FCS as a simple addition to MRPII.

OPT scheduling performances: A case study

A deep insight into Optimized Production Technology (OPT) is sought for by analysing a case study regarding a leading producer of high quality acetate yarns for the textile market. The case study highlights the importance of setting proper values for OPT management parameters in order to achieve desired performances in terms of both system efficiency as well as system effectiveness. A suitable experimental design and a statistical analysis framework allowed the recognition of the impact of single OPT management parameters. Furthermore, the optimal set-points of these parameters were determined in order to cope with resource constraints in a critical manufacturing department. Scheduling performances achievable as output of 'computeraided' scheduling with OPT were confronted with traditional 'man-made' scheduling on the basis of due date performances and setup-related parameters. The results achieved allowed to avoid problems due to a scheduling procedure that underutilized OPT scheduling characteristics.

Focusing material requirements planning (MRP) towards performance

This paper looks at the successes and disappointments of MRP. It studies numerous articles to determine what the key shortcomings of MRP are. Next, it investigates if these failures are correctable, and what the consequences of not correcting these deficiencies means. This article considers alternatives that have been discussed in the current literature. Last of all this article discusses whether the improvements these alternatives suggest are sufficient to make MRP worth salvaging, or whether MRP is a system that needs to be discarded in favor of systems such as JIT (Just-in-Time), Optimized Production Technology (OPT), Theory of Constraints (TOC), and Bottleneck Allocation Methodology (BAM).

AN EXPOSITION OF MULTIPLE CONSTRAINT SCHEDULING AS IMPLEMENTED IN THE GOAL SYSTEM (FORMERLY DISASTERTM)

Since Eli Goldratt first appeared on the scene in the late 1970s, his ideas concerning production management have generated a huge amount of interest, controversy, and misunderstanding. These ideas have been proliferated under several names such as optimized production technology (OPT), drum‐buffer‐rope (DBR), synchronized manufacturing (SM), and theory of constraints (TOC). Although there seems to be general agreement on the importance of how capacity‐constrained resources are scheduled, research aimed at advancing the state of the art for the specific problem addressed by DBR continues to be limited by prior misunderstandings and the lack of a rigorous examination by the academic community. This paper seeks “to advance the state of research on constraint scheduling in several ways. First, it presents a concise history of the evolution of DBR. It then explains the use of rods in constraint scheduling. Next, it presents in detail the solution algorithm incorporated by the Goldratt Institute in their production software and, finally, relates that algorithm to alternative methods. In the process of these activities, several lingering misconceptions are resolved.

Production planning and control in textile industry: A case study

This paper presents an interactive model based system for the management of production in textile production systems focusing on the Master Production Scheduling problem. Because of the special characteristics of the industry, that is mainly the multi-phase process with multiple units per phase, different planning horizons and different production requirements for each phase, the scheduling of these systems becomes quite complex. Apart from a comprehensive presentation of the set of the modules the system is composed of, together with their interrelationships, the above characteristics are analyzed, and their impact on the production control system is explained. The system is also related to two well-known production control systems, namely MRP-II and Optimised Production Technology. The system's attributes are presented with the aid of data structure diagrams, while the complete algorithm concerning the Master Production Scheduling module, in a pseudo-code form, and the corresponding part of the database are illustrated in Appendix A.

The impact of OPT and TPM on the economic production quantity

In manufacturing systems, the material flow is influenced by a number of factors, such as batching policies, capacity of machines, machine breakdowns, etc. Realizing the role of batching policies and reliability of machines in production systems, a mathematical model is presented here for determining optimal batching policies with the objective of improving the speed of material flow considering machine breakdowns and batch splitting and forming. This model is employed for studying (i) the significance of total preventive maintenance (TPM); (ii) the use of the optimized production technology (OPT) concept in batching policies; and (iii) the influence of a set-up cost reduction programme on the performance of manufacturing systems. The basic criterion considered for optimizing the batch sizes is the minimization of total system cost (TSC). An example problem is solved to explain the application of the model.