National Textile Center
Year 8 Proposal
Project No.: I98-S12
Competency: Intelligent Systems
Analysis of Apparel Production Systems to Support Quick Response Replenishment
Project Team:
Leader: Russell E. King: Expertise QR Systems; Analysis of Production Systems
Email: king@eos.ncsu.edu, Phone: (919) 515-5186
Members: [Name/organization/expertise]
Thom J. Hodgson NCSU Production/Inventory Control; Scheduling
Trevor J. Little NCSU Apparel Production Systems; EDI and Quick Response
Carol G. Carrere NCSU Apparel Production Systems
D. Michelle Benjamin [TC]2 Simulation Modeling; Apparel Manufacturing
Tim Currin [TC]2 Simulation Modeling; Software Development
Objective:
The apparel manufacturer is often blamed as the weak link the apparel supply chain. Typically a small to medium size enterprise, the manufacturer must deal with the conflicting objectives of the larger fabric suppliers and retailers. The goals of this project are to provide analysis and prototype software to understand better the role of the manufacturing configuration and production planning/execution system in supporting quick response replenishment to retail, as well as to quantify the impact of the fabric supply system on the ability to meet retail orders.
Earlier work by part of our team led to the development of an analysis tool, The Sourcing Simulator, that is used to quantify financial, inventory and service performance at retail for a line of garments. Analysis with this tool has supported quantitatively what Quick Response proponents have touted for years, i.e, a flexible and rapid, apparel supply system leads to superior performance at retail. In fact, studies with The Sourcing Simulator have shown that in some cases the retailer can afford to pay a QR vendor as much as 50% or more per garment and still achieve gross margins in excess of that which is achieved using a lower cost traditional, and often offshore, vendor.
In this proposal, we address a natural question that arises from the Sourcing Simulator work, that is, How can the manufacturer achieve cost effective and flexible QR replenishment? We will analyze, in detail, existing manufacturing systems such as Progressive Bundle, Modular and Team Sewing to understand what form best supports a quick response supplier in terms of cost and performance. We will also develop analytic models to understand optimal manufacturing planning and execution policies under a variety of operating scenarios. We will develop realistic systems that are characteristic of the optimal policies.
Relevance to NTC Mission:
The almost daily erosion of apparel manufacturing in the U.S. is well documented. The trend is to seek lower labor rates since most retail merchants look at pre-season gross margins and thus wholesale cost is often the determining factor in a sourcing decision. Part of our team has spent the last two years using the Sourcing Simulator tool to convince retailers that wholesale cost is not necessarily the best measure to use. While this has been effective, retailers note that most apparel manufacturers are not currently able to provide QR replenishment. Manufacturers are typically staff lean and, thus, cannot directly support their own research and development. The objective of this project is to provide the basic research necessary to help manufacturers understand which manufacturing system bests supports their business both from a financial and service viewpoint, and to develop execution systems that support that system.
A major thrust of this work directly involves students in this research and with partnering industry (see industry outreach section). The goal is the development of understanding and expertise within the students so that they can transfer this to industry as they join it.
Finally, we have put together a diverse team involving personnel from the College of Textiles and the College of Engineering at N.C. State University along with expertise from the Textile Clothing Technology Corporation ([TC]2). This provides us with the breadth of expertise to be successful in this project.
State of the Art:
Extensive market analysis of the tools available to support apparel production systems and direct contact with manufacturers through [TC]2revealed a dearth of solutions both in terms of the functionality and affordability for small to medium size enterprises. Existing analysis software for the apparel supply chain is insufficient to the real needs. Also, tools the enable tactical coordination along the supply chain do not exist.
Approach:
For the past several years under separate NTC funding, part of our team has been developing tools for and performing analysis of apparel supply and retailing. This led to the development of the Sourcing Simulator package. Over the last year this tool has been taken to industry and based upon feedback modified to meet the requirements from both a retailer’s and manufacturer’s standpoint. The tool provides a thorough analysis of the performance at retail of a line of garments based upon a set of user inputs. A major use of the tool is to understand the financial and service impact of the choice for the manufacture of the garments. The tool allows the user to understand the true value to the retailer of a sourcing decision including the impact of speed, flexibility and quality of stock replenishment as well as the cost of lost sales and price markdowns. It can answer such questions as: How much more can the retailer afford to pay a Quick Response vendor for a garment and still achieve better performance than a traditional source? or What is the value of reducing the supply lead time by two weeks? For the manufacturer it addresses the impact of shortening lead times, decreasing both finished goods and raw material inventory, collaboration with the retailer, reduce order minimum and lead times from the mill supplying fabric. Using the tool the manufacturer can answers questions like: What is the value to me and my customer if the mill can reduce order minimums? or How does POS data shared by the retailer impact the amount of inventory carried?
Other members of our team began work, at the strategic level, to evaluate various manufacturing systems including Progressive Bundle, Line Systems, Modular and Unit Production Systems. The goal was an understanding of how well each supports order streams from retail that would result under QR partnerships.
[TC]2’s tactical simulation models of various configurations of modular manufacturing (The Modular Manufacturing Simulation System and The TeamMate Series have been widely used and accepted by industry. The Manufacturing process that can be modeled include standup/handoff and sit down team sewing. Machine breakdowns, setups, lot sizing and processing types are included.
The natural evolution of this work is to extend the analysis to the detail necessary to thoroughly understand the manufacturing domain from small, single plant manufacturers to brand manufacturers involving a set of manufacturing contractors. Issues range from selecting the appropriate manufacturing system or systems, to capacity allocation, to raw material supply, to capacitated resource scheduling.
This project involves four efforts. First, we are characterizing existing alternative manufacturing systems for the range of manufacturing domains. This involves working with our industry partners to characterize a set of representative operating scenarios. During the first year of this project, a literature review was completed regarding alternative manufacturing systems for (5) manufacturing domains including Progressive Bundle Systems w/flow lines, Modular, Progressive Bundle Systems w/automation incorporated into skill centers, and Line manufacturing. Information regarding batch order release quantities, resource and assembly requirements, and other operating constraints was catalogued. In addition, a study to characterize the major channels to market is being completed.
Second, we are developing analytic models of the manufacturing systems to understand optimal operating policies. A multi-attribute analysis model (via an Excel spreadsheet) of the (5) manufacturing systems (above) has been developed in regard to measures of, and strategic emphasis upon, three manufacturing variables: volume, cost, and time. Through earlier research efforts we have developed a methodology using Markov Decision Processes that has been very effective. We will extend this methodology to apparel manufacturing domains. Characterization of optimal policies is a crucial part of the process in gaining insight into effective and implementable candidate systems.
The third effort involves development of simulation models to test the policies gleaned from the analytic models in a more realistic and robust environment. The goal here is to develop a relatively generic and flexible software tool to allow analysis of a number of environments. A number of models ranging from strategic to operational are being developed. At the strategic level, the Sourcing Simulator has been modified to include the manufacturer (as described above). It is in the final stages of testing and will be released late this Fall. Using the tool we developed a replenishment program with a major retailer and domestic manufacturer. The program was a success and plans for a more extensive program are progressing. At the tactical level, an initial set of policies gleaned from the analytic model described in (2) was tested for a range of replenishment demand volume levels over a 14 week replenishment period, focussing upon the capabilities of each of the (5) manufacturing systems to deliver EXACT replenishment quantities. Extensions were made to a strategic dynamic simulation model developed in SIMProcess by NCSU College of Textiles faculty to accommodate this analysis. The specific model describes the sewn product manufacturer’s requirements to produce at the rate demanded by the point-of-sale environment and examines important an production strategy for manufacturers that produce garments close to the market where they are purchased. At the operational level, a plant-floor-scheduling tool is being developed to optimize operation sequencing and allocation of resources (machines, people, etc.) across the apparel supply chain. As producers and suppliers share more of their operational data, the opportunities for cooperative optimization increase dramatically. The system we are developing is designed for cooperative optimization. This is a key issue for increased competitiveness of the domestic apparel supply chain.
Finally, using the simulation models we are analyzing the various candidate manufacturing systems under the range of scenarios identified as part of the first effort described above.
It has been our observation that to be successful we must directly engage industry in the research. We have already established contacts with a number of apparel manufacturers and retailers who are eager to participate. However, to maintain the level of involvement necessary for the long-term goals, we must provide short-term results. In our experience, this is best achieved through software rich in GUIs (graphical user interfaces) that enables industry to use the tool to understand and benefit from the research. To that end we will develop new prototype software that can be used by manufacturers to evaluate various manufacturing strategies in the context of their own business. We have a long history of developing software tools as part of past research and will use the technique of user-development feedback to directly involve industry in the development.
This Year’s Goal:
Outreach to Industry:
Through our involvement in the DAMA project we have spent a great deal of time this year meeting with the companies discussing the issues related to Quick Response manufacturing. We are working with a set of manufacturers and have identified several others willing to provide test case scenarios and data as well as guidance. As mentioned earlier we will develop usable software tools to help maintain industry interest in the research.
We will also continue our involvement with the DAMA project. Over the last year part of our team has been working daily DAMA personnel. DAMA and [TC]2 will provide important avenues to both transfer the research to industry and engage new industry partners.
New Resources Required:
Beyond salaries for faculty and students, most of the resources necessary for this project are in place with the exception of the following. Resources are needed to subcontract a portion of the work to [TC]2 and to purchase additional computing hardware and software to support the students.