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Timeline Years

DCMME | Timeline | Interviews

FOSTERING THE CENTER’S DEVELOPMENT

HELPING REVITALIZE U.S. MANUFACTURING

HELPING REVITALIZE U.S. MANUFACTURING

HELPING REVITALIZE U.S. MANUFACTURING

HELPING REVITALIZE U.S. MANUFACTURING

A TRIP TO INDIA & OTHER LEARNING OPPORTUNITIES

A TRIP TO INDIA & OTHER LEARNING OPPORTUNITIES

STUDENT FOCUSED. FACULTY DIRECTED

STUDENT FOCUSED. FACULTY DIRECTED

A CENTER THAT CELEBRATES MANUFACTURING

Use the years above to take the site back in time and use the menu below for more details about the center during that year.

MIDAS Project

This project focused on (a) modeling and understanding the demand process for parts faced by the US Coast Guard (USCG) Central Repair facility from bases using transactional information from USCG databases, (b) Understanding and integrating these demands with transactional data regarding the maintenance procedures for parts and (c) building statistical demand models using these two databases in order to separate demands into predictable and nonpredictable demand streams that permit use of a process control view of demands and budgets. A significant source of intellectual and pedagogical value of this project was the actual transactional data of demands for five years for about 40 parts in the system that accounted for about 50 % of the total dollar value of transactions. The MTM students spent time as part of their internship at the USCG facility in Elizabeth City, NC and worked with personnel there as well as faculty at Purdue to document and transfer the databases. DCMME Staff Member Steve Shade played a key role in managing the contracting process between USCG and Purdue’s Division of Sponsored programs who administered the finances for the $ 150,000 project. The statistical analysis, data modeling and code developed to automate the analysis of part demands permitted us to suggest alternate models of system monitoring and supplier coordination. A quick summary of the project (a) The analysis suggests the value of a Data driven planning process, (b) analysis of the data provides a Process Control view of demands for parts, which suggests monitoring demands to check if they are within confidence interval (over 65 % of the parts had all observed demands falling within the confidence interval) and (c) a Proactive Planning and parts staging based approach can be implemented in order to separate predictable and unpredictable demand streams (an average of 38 % of the demands were in the predictable category with a range from 10 % to 66 % of demands across parts) and (d) a budgeting approach that is bottom up, i.e. uses part level demand confidence intervals to develop a budgeting process (around 51.6 % of the repair budget is accounted for by predictable demands). The project was a success, based on feedback from USCG, pedagogical value to student interns and intellectual capital for the faculty.

 

BICEPS - Benchmarking Indices of Supply Chain Efficiency & Performance Study

 

Our goal is to develop a series of metrics that can enable a firm to benchmark supply chain performance. We believe that an ultimate goal for these benchmark statistics is to incorporate both financial metrics as well as operational metrics to enable comparison across industries and to learn from other industry best practices. However, our approach is to reach this goal in a phased manner. (1) Phase 1: This phase will focus on developing benchmark metrics across entities in the grocery supply chain. Our goal in year 1 will focus on development of an instrument that enables us to understand the role of manufacturers, distributors, transportation companies and retailers in the grocery supply chain. (2) Phase 2: This phase will focus on expanding this benchmarking study to include firms in other industries. Our goal in year 2 will be to expand this study where we will incorporate issues we have learned in Phase 1 as well as develop best practice caselets that can enable learning across companies and industries. (3) Phase 3: This phase will incorporate both financial metrics as well as operational metrics in the benchmarking study. In this phase, we will thus focus on linking some financial measures with operational measures to understand how commitments regarding financial metrics might affect operational performance etc.

While the actual evolution of the work is planned to evolve in phases, we believe that it is important to start work immediately on all fronts. To that end, we have already started on work to take firms across different industry segments and assess how supply chain metrics could link to financial measures such as gross margin, sales uncertainty etc. Our goal is to build on this publicly available data through the use of a well-designed survey across industry of detailed supply chain performance data. In what follows, we preview the kind of analyses we expect to do as we move forward across phases.

While there have been several studies that focus on the use of surveys to assess individual responses to issues regarding supply chain performance, there are few studies that rely on comprehensive data collected from competing firms.

 

Automated Intelligent Manufacturing Systems (AIMS) Research

 

Purdue faculty members Kemal Altınkemer (Associate Professor in Management Information Systems), Okan Ersoy (Professor in Electrical Engineering), and Herb Moskowitz (Lewis B. Cullman Distinguished Professor of Manufacturing Management and Director of the Dauch Center for the Management of Manufacturing Enterprises) are currently conducting research into Automated Intelligent Manufacturing Systems (AIMS). The goal of this research is to move one step forward in advanced manufacturing; namely, in helping to define and envision the factory of the future by developing and assessing the value of deploying an Automated Intelligent Manufacturing System to facilitate continuous process improvement. AIMS will be designed to employ the development and use of artificial neural networks as a means of continuously monitoring, providing feedback, and adjusting system parameters and configurations to contemporaneously optimize (i.e., achieve what is theoretically possible) system performance, minimize cost, maximize quality (yield) and productivity (minimize cycle time). Six Sigma, as a business strategy and quality discipline, is highly amenable to such an approach, in the sense that it combines the advantages of what both humans and rule-based (non-human, automated) systems can do best in process management. What makes the approach feasible and indeed desirable is the fact that IT has enabled organizations to automate their data/information gathering systems in manufacturing (and in other domains). What remains to be done is to automate the analysis and synthesis of this information to enhance the decision process. Since much of such analysis is statistical in nature, it is plausible that this task be automated, freeing the individual (or teams) to perform more creative activities, which complement and support the analytical and decision-making process.

  

Center for International Business Education and Research (CIBER)

  

The Purdue University Center for International Business Education and Research (CIBER) was established in 1993 at the Krannert Graduate School of Management and is part of a national network of thirty Centers at universities across the country. CIBERs support research in international business and provide services and programs to assist United States businesses succeed in the global marketplace. Purdue’s CIBER supports academic, research, and business outreach programs in collaboration with the schools of Management, Liberal Arts, Agriculture and Engineering, as well as with representatives of the business community and the government of the State of Indiana.

CIBER Objectives:

Instill in students the motivation, confidence, and knowledge to think and compete globally; stimulate international research to enhance U.S. global competitiveness; educate Indiana managers regarding international trade challenges and opportunities; increase global awareness among the general public