From ERP to advanced resource planning: Improving the operational performance by getting the inputs right.

In this paper, we show that the planning and decision support capabilities of the MPC (Manufacturing Planning and Control) system, which forms the core of any ERP package, may be greatly enhanced by including an Advanced Resource Planning (ARP) module as an add-on at the midterm planning level. This ARP module enables to estimate the impact of variability, complexity and dynamic system behavior on key planning parameters. As such, it yields realistic information both for short-term planning purposes and for reliable lead time quotations. We show how dynamic behavior impacts the operational performance of a manufacturing system, and discuss the framework for incorporating the ARP module into the ERP system.Planning; Operational performance; Performance; International; Science;

Advanced resource planning as decision support module to ERP.

In this paper, we show that the planning and decision-support capabilities of the MPC (Manufacturing Planning and Control) system, which forms the core of any ERP (Enterprise Resource Planning) package, may be substantively enhanced by including a Decision Support Module (DSM) as an add-on at the midterm planning level. This DSM, called Advanced Resource Planning (ARP), serves as parameter setting process as well as tool for improving the structure of the ERP system itself. The ultimate goal of the DSM is to yield realistic information both for scheduling, sales and marketing, strategic and operational decision making and suppliers and customers.

Materials for advanced turbine engines. Volume 1: Advanced blade tip seal system

Project 3, the subject of this technical report, was structured toward the successful engine demonstration of an improved-efficiency, long-life, tip-seal system for turbine blades. The advanced tip-seal system was designed to maintain close operating clearances between turbine blade tips and turbine shrouds and, at the same time, be resistant to environmental effects including high-temperature oxidation, hot corrosion, and thermal cycling. The turbine blade tip comprised an environmentally resistant, activated-diffussion-bonded, monocrystal superalloy combined with a thin layer of aluminium oxide abrasive particles entrapped in an electroplated NiCr matrix. The project established the tip design and joint location, characterized the single-crystal tip alloy and abrasive tip treatment, and established the manufacturing and quality-control plans required to fully process the blades. A total of 171 blades were fully manufactured, and 100 were endurance and performance engine-tested

Implementing the Lean Sigma Framework in an Indian SME: a case study

Lean and Six Sigma are two widely acknowledged business process improvement strategies available to organisations today for achieving dramatic results in cost, quality and time by focusing on process performance. Lately, Lean and Six Sigma practitioners are integrating the two strategies into a more powerful and effective hybrid, addressing many of the weaknesses and retaining most of the strengths of each strategy. Lean Sigma combines the variability reduction tools and techniques from Six Sigma with the waste and non-value added elimination tools and techniques from Lean Manufacturing, to generate savings to the bottom-line of an organisation. This paper proposes a Lean Sigma framework to reduce the defect occurring in the final product (automobile accessories) manufactured by a die-casting process. The proposed framework integrates Lean tools (current state map, 5S System, and Total Productive Maintenance (TPM)) within Six Sigma DMAIC methodology to enhance the bottom-line results and win customer loyalty. Implementation of the proposed framework shows dramatic improvement in the key metrics (defect per unit (DPU), process capability index, mean and standard deviation of casting density, yield, and overall equipment effectiveness (OEE)) and a substantial financial savings is generated by the organisation

Composite structural materials

Technology utilization of fiber reinforced composite materials is discussed in the areas of physical properties, and life prediction. Programs related to the Composite Aircraft Program are described in detail

Competitiveness of Indian Manufacturing: Finding of the 2001 National Manufacturing Survey

In this paper we present findings of the second national survey on the competitiveness of Indian manufacturing. The paper develops hypotheses on the competitiveness of firms in the manufacturing sector and addresses some key questions on the characteristics of world class firms in India. We analyze the processes and practices that such firms have adopted to become world class. More important, we highlight firm level practices that are preventing Indian firms from becoming globally competitive. The findings point towards three distinct aspects of manufacturing management that define the capabilities of the firm, i.e., strategies related to dynamic control of shop floors, network linkages and innovation. It is found that firms that build distinctive technological and managerial capabilities in these domains are able to compete globally. The paper provides a comparison with manufacturing capabilities of competitors in China and draws lessons for organizing large scale manufacturing. It also provides an assessment of the changes that have happened in manufacturing priorities and strategies in India since our last survey that was conducted in 1997 and highlights the implications of these changes.

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested