The process industry is characterized by having relatively few raw materials that can explode into a variety of end products, coproducts, and by-products. A proven method for scheduling this type of output has been developing over the past 25 years under the guidance of Drs. Sam Taylor and Steve Bolander. This type of scheduling is known as PFS. The close integration of the internal schedules and the reliability of constrained capacity usage coupled with the impact of outside events have positioned the process industry as the leader in APS systems.
APS engines include the business application of simulation, heuristics(best of business rules), linear programming (LP), as well as constraint controlled intuitive modeling (e.g., fuzzy logic). These sophisticated mathematical modeling tools embrace all aspects for the business that can be impacted by a SCM implementation. Integrating demand information from the customer’s customer to the supplier’s supplier is enhanced by also incorporating additional dimensions of the business such as the revenue chain. This allows more comprehensive “what if” scenario planning in evaluating new market potentials, corporate take-over return on investment analysis, price sensitivity analysis, and logistics configuration analysis.
The first PFS tools were implemented in the late 1980s and focused on the consumer good industry. The calculation models were rather simplistic and attempted to provide the single best answer based on the model input. As computers have become more sophisticated, the models representing the business have become more sophisticated as well. It is now possible to create almost virtual reality for the business to evaluate alternate plans. This simulator functionality can be easily expanded to simulate the entire enterprise. Like running a real business, a single, large model is insufficient to represent all the integrated functions within an enterprise. Effective PFS systems contain the ability to define multiple models to best describe the business.
The three main approaches for solving the process industry scheduling problem include simulation, heuristics, and optimization. Simulation attempts to represent in a computer the interrelationships of a business. An effective simulator directly relates to the business and allows the iteration of many different decisions to determine the impact on the enterprise. The simulator can be as simple as a spreadsheet that provides what if capability for different production schedule. The openness of ERP systems provides the opportunity to download information from the main system into spreadsheets for seamless manipulation in what if analysis. The simulator can handle important trade-offs analytically and clearly identify the impact of certain decision. This quantifiable analysis allows decisions to be made on the basis of fact and they make fewer decisions based on intuition. At times, the amount of data becomes overwhelming,causing difficulty in building a mental picture of a particular problem or solution.
The simulator can be used to develop a more sophisticated model to reflect a particular enterprise. These simulators can provide an excellent teaching tool, such as the one included in this book, or can help in managing a real business by allowing the management to see consequences of decisions before implementation. With the continuing growth of computer processing power and the decline of computer processing cost, simulations are sure to become more widely used in daily operations. Heuristics are simplifying rules that are used to develop a feasible schedule. These rules are based on intuition or experience instead of by mathematical optimization. These rules may be required because the simulation and optimization may not be able to provide a feasible solution without them. Another use for heuristic rules is to develop an initial solution from which improvements can be made. An example may be that production cannot be increased or decreased more than 10,000 units for each major schedule change. Another rule may be that major schedule changes can only be incorporated once a quarter. Reasons exist for these business rules, but they cannot be quantified sufficiently for incorporation into the simulator. Remember that the computer tools cannot take over for good management of the company. Materials, capacity, and other resources have been well managed in the past. The computer tools should not replace effective management but rather should augment it.
Optimization attempts to calculate the best solution given the bottleneck to achieve the desired results. The optimization focus could include the most profits, shortest total lead-time, best customer service for a preferred customer, or smallest total changeover time; it should also include making whatever measure that is selected the best that it can be. For optimization to be effective there must be a system defined for which the demand exceeds the possible supply. Optimization then provides the best possible solution to the problem in terms of this specified objective function. In the process industry, with its dependent setups and variant production batch size, optimization modelers enable the scheduler to consider a variety of inputs when developing the schedule.
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Erp Tools Tutorial
History Of Enterprise Resource Planning
The Theory Of Constraints And Erp
Sales And Operaions Planning
Buffer Resource Strategy
Enterprise Resource Management
Integrating The Supply Chain To Reap The Rewards
Strategic Sourcing And Procurement
Operations Planning (material And Capacity
Product Life Cycle Management
Manufacturing Execution System
Erp System Requirements
Selecting The Right Erp System
Data Record Accuracy
Implementation—generalized Industry Application
Repetitive Manufacturing Application
Process Industry Application
Customer 360-degree View
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