Keywords

Inventory level, lead time, throughput of products, simulation, Arena, erratic demand, variable and uncertain demand, stochastic demand, dynamic demand, demand forecast, Multi-channel Manufacturing, Quick response, Lean manufacturing, Just in

Abstract

To handle uncertainties and variabilities in production demands, many manufacturing companies have adopted different strategies, such as varying quoted lead time, rejecting orders, increasing stock or inventory levels, and implementing volume flexibility. Make-to-stock (MTS) systems are designed to offer zero lead time by providing an inventory buffer for the organizations, but they are costly and involve risks such as obsolescence and wasted expenditures. The main concern of make-to-order (MTO) systems is eliminating inventories and reducing the non-value-added processes and wastes; however, these systems are based on the assumption that the manufacturing environments and customers' demand are deterministic. Research shows that in MTO systems variability and uncertainty in the demand levels causes instability in the production flow, resulting in congestion in the production flow, long lead times, and low throughput. Neither strategy is wholly satisfactory. A new alternative approach, multi-channel manufacturing (MCM) systems are designed to manage uncertainties and variabilities in demands by first focusing on customers' response time. The products are divided into different product families, each with its own manufacturing stream or sub-factory. MCM also allocates the production capacity needed in each sub-factory to produce each product family. In this research, the performance of an MCM system is studied by implementing MCM in a real case scenario from textile industry modeled via discrete event simulation. MTS and MTO systems are implemented for the same case scenario and the results are studied and compared. The variables of interest for this research are the throughput of products, the level of on-time deliveries, and the inventory level. The results conducted from the simulation experiments favor the simulated MCM system for all mentioned criteria. Further research activities, such as applying MCM to different manufacturing contexts, is highly recommended.

Notes

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Graduation Date

2004

Semester

Fall

Advisor

Kulonda, Dennis

Degree

Master of Science in Mechanical Engineering (M.S.M.E.)

College

College of Engineering and Computer Science

Department

Industrial Engineering and Management Systems

Degree Program

Industrial Engineering and Management Systems

Format

application/pdf

Identifier

CFE0000240

URL

http://purl.fcla.edu/fcla/etd/CFE0000240

Language

English

Release Date

December 2004

Length of Campus-only Access

None

Access Status

Masters Thesis (Open Access)

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Engineering Commons

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