Facilities Design The Facilities We Plan Today Essay

Length: 11 pages Sources: 5 Subject: Business - Management Type: Essay Paper: #14755123 Related Topics: Designs, Toyota, Flow Chart, Decision Support System
Excerpt from Essay :

Facilities Design

The facilities we plan today must help an organization achieve supply chain excellence (Tompkins, et al. 2003, pg 3). Product design activities begin with conceptualization, where ideas for new product are generated based on market information or from existing technology. Product selection restricts new products to those ideas which pass the tests of market potential, financial viability, and producibility. In many cases product selection analysis may be quite subjective in nature and based on somewhat limited information. Once a new product idea has been selected for implementation, a preliminary design is developed. During this stage the product is specified completely. As part of the process many tradeoffs between cost and product performance are considered. A preliminary set of drawings may also be completed. The preliminary design is then operationalized as a prototype. The prototype is tested in an attempt to verify market and technical performance.

Several iterations through the prototyping process may be necessary. Engineering changes initiated as a result of prototype testing are then incorporated as part of the final design. During the final design phase, drawings and final specifications are developed. Design reviews are typically conducted after the preliminary design and final design stages during the design process. These formal reviews are aimed at determining whether a proposed design will perform successfully during use, can be produced at low cost, and is suitable for prompt, low-cost field maintenance. Usually the reviews are conducted by a team consisting mainly of specialists who are not directly associated with the development of the design. These specialists are generally in great demand and short supply in the organization (Shelton, 2008. Pg 42-43).

CASE STUDY I -- The Northrop Corporation:

Facilities Design Analysis

At a company like Northrop, the choice of technology deals with the set of processes, tools, methods, procedures, and equipment that will be utilized within the process type that has been selected. Once these issues have been resolved, the focus turns to two micro level decisions -- process flow analysis and facility layout. In process flow analysis we deal with both material and information flows from input through transformation to the final product. As a result of this analysis improved methods or procedures may be discovered. Based on the type of process selected and flow patterns developed as a result of process flow analysis, a facility layout is arrived at. These micro-level decisions affect decisions in other parts of operations including scheduling, job design, inventory levels, and quality control procedures.

As is the case for product design, consulting, design, and planning expert systems can be useful in supporting process design decisions. Consulting systems can be useful in advising designers on the links among corporate strategy, manufacturing strategy, process selection, and choice of technology. Design systems may be useful in determining the combination of parameters that will minimize the chance of manufacturing imperfection. They may also help in removing special causes and setting the process capability. Planning systems, again, may help in rationalizing the complete design process. Many of these process design activities fall under the responsibility of industrial engineering. Council on Competitiveness (2006), provides an interesting review of potential uses of expert systems in industrial engineering. Being a massive manufacturing company, Manufacturing Process Planner is a system developed for internal use by the Northrop Corporation that aids in the planning process for the manufacture of the approximately 20,000 parts that go into a fighter plane.

In control of quality during manufacturing consulting, control, debugging, diagnosis, interpretation, monitoring, and repair systems may prove useful. Such systems already have been successfully used in industry. As product difficulty grows there is likely to be an advanced incidence of field harms. Franz, et al. (2002 pg 2159) believe that 20 to 30% of problems concerning fitness for use are attributable to field factors such as inadequate operating or maintenance procedures, human error during maintenance, inaccessibility to repair, and defective spare parts....

...

There exists here a great opportunity to reduce these problems through the use of expert systems in training and consulting capacities. Training systems can be directed both at service technicians and the user. Consulting systems can be used for the diagnosis and troubleshooting of operating problems. Many applications of this nature are already in place, including systems to diagnose steam turbine generator problems marketed by Westinghouse; a Toyota system to troubleshoot automobile engine problems; and COMPASS, a GTE developed system for assisting switch maintenance personnel by analyzing operating data and recommending appropriate maintenance actions.

The effect of shorter lead and cycle times can be evaluated within the company's broader supply chain perspective. This is the final step since the inventory, materials, and manufacturing infrastructures must be substantially in place to support the company's, suppliers', and customers' elements of the overall cycle time. However, in actual practice, policy, variability reduction, and cycle time reduction may be implemented in any order or mix that makes business sense.

At Northrop, whereas models provide snapshots over time, simulations provide a dynamic operational view of the company's material handling and storage processes. Plant- wide simulations begin with a CAD layout of the facility. Including elevations of storage systems, conveyors, and equipment enables a 3-D effect to be produced. Actual vehicle accelerations, operating speeds, decelerations, blocking effect of traffic in aisles, work station delivery and pickup distributions, and operator or automated load/unload times can all be simulated. Changing the operating variables, altering flow paths, volumes, and rates within the facility, and changing the number or assignments of operators can be evaluated in order to fine-tune lay- outs, equipment, and personnel levels (Anderson et al. 2009 p 169-170).

Northtrop uses simulations are developed in situations where the number and type of equipment may be in question, especially with regard to future upgrades and modifications. Production lines, buffer staging and storage systems, and material handling systems operating in constrained environments and subject to variation due to operator or other factors are likely candidates for simulation. A common application is to determine the number of automated guided vehicles required to de- liver unit loads to work stations located throughout the facility.

Companies such as Northtrop commit millions of dollars in facility layout modifications, new construction, and capital equipment investment on the basis of analyses, models, and simulations. Their product line being billion dollar products justify the high costs of facilities design. Management commits this level of investment only when convinced that the conclusions and recommendations are based on a valid representation of current and/or future conditions. While the decision-makers may not take the time to completely understand the actual mathematics and software logic, they do need to be confident that a structured methodology has been followed. They must trust the modeler. Confidence in the modeler is complicated by the fact that no two people will ever perform an analysis or develop a model in exactly the same way.

This can lead to different results based on the same requirements. Following a structured methodology is not so much to guarantee that the same results will be achieved with the same requirements, but that a valid result will be developed. Anyone who ever developed a bid spec and solicited proposals for an integrated system will attest to the fact that there is no such thing as the single right solution. Even with differences, there should be a range of outputs where modelers agree (Manufacturing Systems, 1993, pg. 104).

They should certainly agree on any aspect of the model which can be verified against current operations. If there are widely divergent conclusions and recommendations based on the same requirements, something is probably wrong. Having and using a structured methodology enables results to be understood, repeated, and evaluated (Shimokawa et al., 2007). The importance of graphing relationships pictorially via figures, tables, charts, and graphs cannot be overemphasized. Decision-makers trust a modeler's conclusions and recommendations more when the mathematics are translated into a visual format. This is one of the reasons why graphics-based simulations are such powerful tools. Another commonly used technique at Northrop, when developing System Requirements Documents (SRDs) and bid specs is to represent flow rates via block diagrams as shown below.

Flow in Figure 1.1.

This is a rather sterile treatment of flows that is utilized at the Northtrop Corporation and does a disservice to those readers who are not intimately familiar with the facility and analysis. To make matters worse, separate pages are often used to segregate full from empty moves and carton, tote, pallet, and rack moves. Over- laying the same flows on a facility layout as shown in the Facility Flow version makes a lot more sense. In fact, providing a feel for move distances and from/to points highlights the long moves and any reverse flows.

CASE STUDY II - Toyota Motors:

Facilities Design Analysis

Production is the heart and soul of Toyota and its precision guided, Facilities Planning initiatives. All Toyota lines are exactly alike, and it is impossible to mistake any one of them for a non-Toyota assembly line. Of course, cars sitting on the line are…

Sources Used in Documents:

Bibliography

Anderson, Kenneth R., David E. Coleman, C. Ray Hill, Andrew P. Jaworski, Patric Love, Douglas A. Spindler, and Marwan Simaan (2009). Knowledge-based statistical process control. Innovative Applications of Artificial Intelligence Herbert Schorr and Alain Rapport (eds). Menlo Park, CA: AAAI Press/The MIT Press, 169-181.

Council on Competitiveness (2006), Building on Baldrige: American Quality for the 21st Century. Washington, D.C.

Franz, L.S. And S.T. Foster, Jr. (2002), Utilizing a Knowledge-Based Decision Support System as Total Quality Management Consultant. International Journal of Production Research Vol. 30, No. 9, 2159-2171.

Kojima, Schoichi, Hiroatsu Hara, Nobuo Matsuda, Yoshitatsu Mori, Michiaki Nishimura, and Yasuhiko Yasuda. (1991) A Scheduling Expert System for Paper Production J.K. Lee, R. Mizoguchi, D. Narasimhalu, and D.S. Yeung, eds. Operational Expert System Applications in the Far East New York, Pergamon Press, 122-132.


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