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). Our focus is on scheduling systems, which in general take the data input and, through a series of algorithms, provide the most optimum means of utilizing materials, machinery, staff, etc. so that the finished product is on time and on budget. The more advanced the technological tool, the more it does. For instance, some can determine priority tasks, risks of completion, communication between departments involved in manufacturing, expectations, modeling of machine efficiency, and even adherence to productions and quality standards (Betz, 2011, pp. 170-4).
Because of the wide variety of manufacturing needs there are also wide varieties of technological solutions to scheduling issues. In most cases, the issues focus on efficiency and being able to adapt to a large amount of information that will improve resource utilization, do much more with existing resources, and make better decisions faster. There are a number of ancillary "tools" that can be used to inform, such as tablet computers, laptops, smartphones, real time imaging, etc. All of these tools improve based on the processing speed and memory capacity of the hardware platform, and the ability for the software to take advantage of additional memory and speed. The more computing power that can be linked together, the higher the performance level of the software and scheduling complexity. Indeed, when parallel task scheduling, for instance, is used, the even very complex and multi-stage manufacturing processes can be made far more efficient (Krueger, P., et al., 1994).
Different platforms, then, allow for different ways of implementing scheduling issues, all dependent upon the nature and complexity of the desired result. Some of the more common platforms used in scheduling are:
Enterprise Portals -- Web-based interfaces that allow for multi-departmental collaboration. Portals are partitioned to allow for key performance indicators, alters, to time constrained manufacturing efforts.
Production Planning Software -- Often a subset of supply chain planning, this collects real-time data from multiple sources across the organization's supply change; converting the data into information on how the raw materials are moving through the organization to create the product.
Finite Capacity Scheduling -- Scheduling software that allows for manufacturing resources that are finite. It allows for other constraints like personnel, regulatory pressures, materials, etc. Plant information and control input data and the scheduling software finds solutions with outputs to a variety of devices (McClellan, 2003, 170-4).
Conclusions- Essentially, the need for technological solutions, and usually a dedicated it department, for manufacturing in the modern world focuses on the complex nature of scheduling processes that require two or more processes to share resources, find variations and solutions when raw materials vary, and fall into a class of problems that are difficult to solve based on procedures and operational complexity. To do this, more and more technological solutions use algorithmic methods that view scheduling problems so that an objective (time, duration, speed, quality) can be minimized or maximized. Mathematics is used to solve these issued and find optimal solutions. The more computing and technological power, the more the complex solutions can be found and optimized (Mendez, C., et al., 2006). Production, manufacturing and the use of new composite materials has inspired numerous approaches to scheduling over the last few decades. From the Gantt charts to modern super-computers, all share one thing in common: using technological solutions to synthesize scheduling approaches to maximize efficiency and profit (Hermann, 2007).
Betz, F. (2011). Managing Technological INnovation: Competitive Advantage from Change. New York: John Wiley.
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Hermann, J. (2007, March). The Legacy of Taylor, Gantt and Johnson: How to Improve Production Scheduling. Retrieved from the Institute for Systems Research: http://drum.lib.umd.edu/bitstream/1903/7488/4/25813_cov.pdf
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Krueger, P., et al. (1994). Job Scheduling is More Important than Processor Allocation. Parallel and Distributed Systems, 5(5), 488-97. Retrieved from http://ieeexplore.ieee.org/xpl / login.jsp?tp=&arnumber=282559&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D282559
Lopez, P., & Roubellat, F. (2010). Production Scheduling. New York: Wiley.
McClellan, M. (2003). Collaborative Manufacting Using Real Time Information to Support the Supply Chain. Boca Raton, FL: St. Lucie Press.
Mendez, C., et al. (2006). State of the Art Review of Optimization Methods. Computers and Chemical Engineering, 30(2), 913-46. Retrieved from http://numero.cheme.cmu.edu/uploads/MendezBatchReview.pdf[continue]
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