An approximate summarization method of process data for acquiring knowledge to improve product quality

This paper describes a machine learning approach for a manufacturing database. The method is presented in the Nb-Ti superconducting wire domain. A Nb-Ti superconducting wire is produced by iterating the drawing and heat treatment operations. The purpose is to obtain approximate summarization of process data that describes how a production schedule can be improved for better product quality. The method consists of the following steps: First, define a ranking function for a production schedule. Then, generate 'positive' and 'negative' instances for improving a production schedule by comparing a pair of schedules and their ranking values in the database. Using a machine learning technique, called 'ID3', a 'modification patterns' are obtained that generalize the data for better production quality. The final step is to extract approximate information from the induced patterns, which is both desirable for easier understanding by human experts and necessary to avoid being too much influenced by excessive details or disturbances. Two criteria are proposed, correctness and applicability indices, for this approximation.     

An approach to learning from both good and poor factory performance in a Kanban-based just-in-time production system

In a JIT manufacturing environment it may be desirable to learn from an archived history of data that contains information that reflects less than optimal factory performance. The purpose of this paper is to use rule induction to predict JIT factory performance from past data that reflects both poor (saturated or starved) and good (efficient) factory performance. Inductive learning techniques have previously been applied to JIT production systems (Markham et al. , Computers and Industrial Engineering, 34 , 717-726, 1998; Markham et al. , International Journal of Manufacturing Technology Management, 11 (4), 239-246, 2000), but these techniques were only applied to data sets that reflected a well-performing factory. This paper presents an approach based on inductive learning in a JIT manufacturing environment that (1) accurately classifies and predicts factory performance based on shop factors, and (2) identifies the important relationships between the shop factors that determine factory performance. An example application is presented in which the classification and regression tree (CART) technique is used to predict saturated, starved or efficient factory performance based on dynamic shop floor data. This means that the relationship between the variables that cause poor factory performance can be discovered and measures to assure efficient performance can then be taken.     

Longitudinal Analysis of Inhibitors of Manufacturer Delivery Performance

This article examines demand, manufacturing, and supply factors proposed to inhibit manufacturer delivery execution. Extant research proposes many factors expected to harm delivery performance. Prior cross‐sectional empirical research examines such factors at the plant level, generally finding factors arising from dynamic complexity to be significant, but factors arising from detail complexity to be insignificant. Little empirical research examines the factors using product‐level operating data, which arguably makes more sense for analyzing how supply chain complexity factors inhibit delivery. For purposes of research triangulation, we use longitudinal product‐level data from MRP systems to examine whether the factors inhibit internal manufacturing on time job rates and three customer‐oriented measures of delivery performance: product line item fill rates, average delivery lead times, and average tardiness. Our econometric models pool product line item data across division plants and within distinct product families, using a proprietary monthly dataset on over 100 product line items from the environmental controls manufacturing division of a Fortune 100 conglomerate. The data summarize customer ordering events of over 900 customers and supply chain activities of over 80 suppliers. The study contributes academically by finding significant detail complexity inhibitors of delivery that prior studies found insignificant. The findings demonstrate the need for empirical research using data disaggregated below the plant‐level unit of analysis, as they illustrate how some factors previously found insignificant indeed are significant when considered at the product‐level unit of analysis. Managers can use the findings to understand better which drivers and inhibitors of delivery performance are important.     

Decentralized WIP-oriented manufacturing control (DEWIP)

DEWIP is a manufacturing control system for job shop environments aiming at achieving short and reliable lead times by establishing WIP control loops between the manufacturing work centres. The paper describes the mode of function, the setting of parameters and simulation results of the new manufacturing control system. The setting of parameters is done with the aid of the funnel model and the theory of logistic operating curves, both developed at the Institute of Production Systems at the University of Hanover. The simulation is conducted using industrial data and makes it possible to assess DEWIP with regard to lead times, WIP level, performance and schedule reliability. DEWIP is compared both with an uncontrolled process and with the manufacturing control systems Load oriented order release (LOOR), Conwip and Polca. The results suggest that DEWIP and the models employed for the setting of parameters are suitable for job shop production and therefore offer a valuable alternative to prevailing centralized manufacturing control systems.     

Period batch control: Classic,not outdated

Period Batch Control was developed and first applied during the Second World War. The historic roots are discussed and the principles of this classic production planning approach explained, PBC compared to other production control concepts, and it is demonstrated that it may still be fruitfully applied, especially in combination with cellular manufacturing. It is therefore considered as a classic, but not outdated production planning concept.     

Manufacturing cycle time reduction using balance control in the semiconductor fabrication line

In semiconductor manufacturing, wafer fabrication is the most complicated and important process, and is composed of several hundred process steps and involves several hundred machines. The productivity of the manufacturing process depends mainly on controlling the balance of WIP (work-inprogress) flow to achieve maximum throughput under short manufacturing cycle times. This paper discusses how to determine the proper WIP level for operations, against which balance status can be measured. Balance measurement can be applied in mathematical modelling for bottleneck scheduling and operations management of the fabrication line. Performances are evaluated through computational experiments to show that balance driven management leads to 15-33% more production in 21% shorter manufacturing cycle time than production driven management.     

Visualization of Innovation in Global Supply Chain Networks

This methodological note identifies and describes a data‐driven visualization approach to study innovations in supply chain networks (ISCN). We demonstrate its value and applicability with illustrative examples to pertinent structure‐related ISCN research questions in the global electronics industry. Our visualization approach can be used to reveal and understand important clusters, patterns, trends, and outliers of ISCN not necessarily identified with traditional methods. The broader aim of this note is to demonstrate the complementary value of emerging visual analytic approaches in managerial decision‐making contexts and describe how actionable insights can be achieved.     

Robustness evaluation of multisite distributed schedule with perturbed virtual jobshops

A method for evaluating the robustness of medium term multisite distributed schedule is presented. The method of robustness evaluation is based on steps and tools associated with the conceptual model of a MultiSite Reactive Production Activity Control (MSR-PAC). The MSR-PAC makes it possible to study the sensitivity of the scheduled plans in presence of perturbations. These must be evaluated before being dispatched to the shop-floors. At present, to the best of the authors' knowledge, there is no means to make this evaluation in the distributed systems such as extended enterprise. The monitoring of errors is based on the discrepancy between the two flow-shape functions that model respectively the dynamics of the scheduled manufacturing orders and the state of the perturbed production. The MSR-PAC is based on a multisite agent system and on the monitoring of the perturbed virtual jobshops. The method can also be used for controlling short term distributed production activities.     

A survey on bank branch efficiency and performance research with data envelopment analysis

The banking industry has been the object of DEA analyses by a significant number of researchers and probably is the most heavily studied of all business sectors. Various DEA models have been applied in performance assessing problems, and the banks' complex production processes have further motivated the extension and improvement of DEA techniques. This paper surveys 80 published DEA applications in 24 countries/areas that specifically focus on bank branches. Key issues related to the design of DEA models in these studies are discussed. Much advice is included on how to design future experiments and studies in this domain. A number of areas where further research could be fruitful are suggested.     

A two-phase robot selection procedure

In this paper, a two-phase methodology is proposed for robot selection. In phase 1, data envelopment analysis is used as a means to determine the technically efficient robot alternatives, considering cost and technical performance parameters. Using data envelopment analysis permits us to consider the fact that the performance parameters specified by the vendors are generally unattainable in practice. In the second phase, a fuzzy robot selection algorithm is utilized to rank the technically efficient robots according to both predetermined objective criteria and additional vendor-related subjective criteria. The algorithm is based on calculating fuzzy suitability indices for the technically efficient robot alternatives, and then, ranking the fuzzy indices to select the best robot alternative. A comprehensive example is provided to illustrate the decision procedure. The algorithm proposed in here is also applicable to a broader area of decision problems, e.g. facility site selection, determination of the best CNC machine or flexible manufacturing system among a set of mutually exclusive alternatives.     

EF-based strategies for productivity improvements at wet-etch stations

Abstract

In complex manufacturing environments, Cycle Time (CT) reductions obtained at critical production steps could generate considerable productivity improvements and lead to capacity increases at no investment costs. A wet-etch station operating in a real semiconductor manufacturing plant has been modelled to investigate the impact of different assignment strategies on CT performance. Assignment strategies based on the “Earliest Finish” (EF) concept have been developed and their productivity efficiency compared with a workload balance-based strategy. The EF-based strategies differ from each other for both computational complexity and amount of real-time information required. Encouraging results obtained for a high detail level EF strategy have inspired the development of conceptually similar strategies characterised by reduced implementation efforts. Fundamental implementation constraints, suggested by the manufacturing engineers familiar with the wet-etch station, have been taken into account throughout the different stages of this study. CT reductions ranging between 11% and 28% could be achieved at the modelled wet-etch station.     

The permutation flow shop problem with just in time production considerations

This research presents a variation to the permutation flow shop problem where Just In Time (JIT) production requirements are taken into account. The model developed in this research employs dual objectives. In addition to the traditional objective of minimizing the production makespan, minimization of Miltenburg's material usage rate is also incorporated. In this model, multiple units of any product are permitted in the production sequence. However, the minimization of material usage rates attempts to prevent batch scheduling of products and allows unit flow of products as required in demand flow manufacturing. A solution method is proposed for determining an optimal production sequence via an efficient frontier approach and Simulated Annealing (SA). Test problems and specific performance criteria are used to assess the solutions generated by the proposed method. Experimental results presented in this paper show that the use of the efficient frontier and SA provide solutions that approach the optimal solution for the performance measures used in this research.     

Lean Six Sigma applied to process performance and improvement model for the development of electric scooter water-cooling green motor assembly

Abstract

In response to the environmental issues triggered by global warming, worldwide companies gradually put the factor of carbon emission into the process of product lifecycle, developing green technology or adopting cleaner production aimed at sustainable development. Lean Six Sigma has advantages of cutting waste and facilitating process improvements as well as system analysis, helping enterprises create the overall business benefits in the value chain. Used in the renewable energy industry, it can promote the triple bottom line (TBL), the performance of sustainable production for corporate profit, social responsibility and environmental responsibility. Therefore, this work took the process performance of the electric scooter water-cooling green motor manufactured in Taiwan with the world’s highest density of scooters as a case study. The developed performance evaluation and improvement model for manufacturing scheduling and process quality achieved the goal of economic benefits of enhancing process quality performance by shortening manufacturing scheduling and reducing process variations with Lean Six Sigma. Besides, they could respond to the policy of energy saving and carbon reduction – replacing the traditional scooters of high-carbon emissions with the electric scooters of low emissions. Furthermore, they could bring enterprises into harmony with economic benefits, ecological benefits and social benefits.     

Experimental earliness/tardiness production planning with the due window system

In order to use the philosophy of JIT to improve the production planning method of MRP-II, we propose the experimental software system of the earliness/tardiness produc tion planning problem with due window. By means of the approaches and model reported in this paper, the optimal production planning can be achieved. The recommended model extends the problem of due window from the shop scheduling level into the aggregated planning level of mass manufacturing systems. Simulation results have demonstrated that the experimental software is a useful tool for the production management of repetitive manufacturing enterprises.     

Management performance: An empirical study of the manufacturing companies in Taiwan

This paper proposes a methodology to measure the management performance of the firms of an industry. The idea is to break down the very complicated concept of management into subjects such that the performance of each subject can be measured. The performances of all subjects are then aggregated to form an overall performance of management. The weights associated with the subjects which play a decisive role in aggregation are determined from a two-stage method. This method takes into account both the experience of the experts and the reality reflected by the data collected from the firms. Therefore, the weights generated are more acceptable to the firms. An example of the manufacturing firms in Taiwan illustrates how management is broken down into subjects and how their performances are aggregated. A regression analysis shows that management, in addition to capital and labor, makes a significant contribution to organizational performance. Although the discussion of this paper is confined to the manufacturing industry in Taiwan, the methodology can be applied to other industries and other countries.     

Redistributed Manufacturing and the Impact of Big Data: A Consumer Goods Perspective

Abstract

Digitalization and the growth of big data promise greater customization as well as change in how manufacturing is distributed. Yet, challenges arise in applying these new approaches in consumer goods industries that often emphasize mass production and extended supply chains. We build a conceptual framework to explore whether big data combined with new manufacturing technologies can facilitate redistributed manufacturing (RDM). Through analysis of 24 consumer goods industry cases using primary and secondary data, we investigated evolving manufacturing configurations, their underlying drivers, the role of big data applications, and their impact on the redistribution of manufacturing. We find some applications of RDM concepts, although in other cases existing manufacturing configurations are leveraged for high volume consumer goods products through big data analytics and market segmentation. The analysis indicates that the framework put forward in the paper has broader value in organizing thinking about emerging interrelationships between big data and manufacturing.     

How can we ascertain,understand and interpret the performance level of a production system? A visual method: 'The plan of preferences'

The general context of this study is the parameterizing of the production system. The aim is to regulate the physical flow according to objectives, usually expressed in terms of performance indicator values. Performance control requires mastery of the consequences of the choices made. It then becomes essential to be able to articulate the potential effects of the various decisions. The existing approaches to solving this problem all imply the use of means of simulation capable of assessing the value of the performance indicators for each configuration of the action parameters. Taking this assumption as a basis, and focusing on the user, a multiple criteria approach is developed towards the understanding and schematization of the behaviour of a given system. The particularity of this approach lies in combining both data analysis and user expertise. The instrumentation approach uses tools for data analysis and assistance in decision making to achieve an original visualization of the structure of performance levels in a plan, called the plan of preferences .     

Flexible reasoning using sensible agent-based systems: A case study in job flow scheduling

Planning and control systems for highly dynamic and uncertain manufacturing environments require adaptive flexibility and decision-making capabilities. Modern distributed manufacturing systems assess the utility of planning and executing solutions for both system goals (e.g. minimize manufacturing production time for all parts or minimize WIP) and local goals (e.g. expedite part A production schedule or maximize machine X utilization). Sensible Agents have the ability to alter their autonomy levels to choose among a set of decision models in order to handle the differences between local and system goals. In this paper, Sensible Agents are applied to a production planning and control problem in the context of job shop scheduling and decision model theory. Sensible Agents provide for trade-off reasoning mechanisms among system and local utilities that are flexible and responsive to an agent's abilities, situational context and position in the organizational structure of the system.     

The Effects of a Combined Goal Setting,Feedback and Incentive Intervention on Job Performance in a Manufacturing Environment

Abstract

Baked anodes are used in finished aluminum production. Employees of a large multinational aluminum smelter were responsible for the production of more than 3500 baked anodes per week. During a 74 week long baseline (A) condition, production of unusable anodes exceeded 300 per week or 8.6 percent of their nominal 3500 anode production requirement. A problem analysis suggested that this high rate of defective anodes might have been due to weak antecedents, inefficient work procedures and weak performance contingencies. An intervention package that included a combination of goal setting, performance feedback, and tangible rewards was designed to strengthen antecedents and consequences of job performance. A performance feedback system was introduced with and remained constant across the three types of performance contingent rewards and three successively higher goal levels. The three reward and goal combinations were introduced to “strengthen” antecedents and performance contingencies within an A-B1-B2-B3-A with reversal to baseline conditions design. Anode reject rates were lower (better performance) during the three intervention phases compared to both the pre- and post-intervention baseline phase data. A dramatic performance improvement (lowered rate of rejects) was observed during the B3 phase that included the highest performance goal. These data demonstrate that a treatment combination of specific goal setting, feedback and tangible rewards can “strengthen” antecedents and performance contingencies resulting in improved objective performance in a manufacturing environment.     

MRP system performance under lumpy demand environments

Material requirements planning (MRP) systems are deemed to deal with master schedules with lumpy demand patterns better than any other production scheduling system. Past studies have advocated important advantages of using MRP systems. The objective of this paper is to look into the impact of patterns of demand lumpiness on the performance of MRP systems by a simulation study. Results show that there is an important threshold point in terms of degree of lumpiness at which MRP system performance starts to deteriorate in the operating conditions considered. If master production schedules (MPS) can be controlled by manufacturers, MRP users should exercise caution to introduce demand lumpiness in MPS to improve system performance. If not, MRP users should then examine the given lumpiness and choose an appropriate lot-sizing rule that has been shown to take advantage of the effect of demand lumpiness.