The Formulation of a comprehensive spreadsheet-based production plan for James Hardie Philippines Inc. - Aquino, Diana Sylvia Z. (College of Engineering, 2004)
James Hardie International is a leading international building materials company and a global leader in fiber cement, one of the world?s fastest growing building products. In the Philippines, it is the only manufacturer of fiber cement. Fiber cement is used in the exterior and interior of a building, from exterior cladding and internal lining, to fencing, bracing and decorative finishes. James Hardie Philippines, Inc. in Cabuyao Laguna is one of the largest and most complex plants of the James Hardie Company worldwide, running various products with different specifications and resource requirements at different volume quantities to satisfy varying local and international customer demands. Given the rising complexity of the plant?s production system, a production plan is needed in achieving high efficiency for the company. With that, the group intends to systematize the generation of the plant?s master production plan, focusing on retaining and/or improving the plant system?s capability in meeting the demand of its various products. The main objective of the study is to make use of dynamic programming and spreadsheet applications to develop a computerized production plan determination procedure. A production plan considers the plant?s various manufacturing capability parameters and constraints, demand levels of the different products, and customer-driven deadlines. However, the demand levels of products are not given prior to production planning. The company use forecasts to estimate the demand for the products. The desired production plan will not be optimum if forecasts are inaccurate. Because of this, the group verified if the company?s forecasts are adequate to use in the production planning. After the verification, the conceptualization of the creation of the production plan was done. The production plan makes use of several similar concepts from various Operations Management models. These include Material Requirements Planning, Aggregate Planning, Scheduling and the Transportation Model. Description of each model, their assumptions, and their similarities with this project were also discussed. After analyzing the different models, the group decided that the use of linear and dynamic programming will result to the optimum production plan. The model created by the group is a simple linear program incorporating only the quarterly budget, monthly demands, and plant capacities. Several assumptions were integrated to the model. This includes assumptions about amount and unit of product, allowances and changeover times. After stating assumptions, the model was then formulated. The objective function, variables and constraints were agreed upon by the group. The formulations for the first three quarters of FY2004 were encoded and run using LINDO solver to ensure that the model formulation was correct. After which, the model and solution code was encoded to an excel worksheet and a C++ program. The inputs should be the monthly budgets and product demand. The output is the fraction of the time each month that should be devoted to producing a particular product. Validation of the model against actual production quantities showed that the model outputs were appropriate and suitable for the production planning phase. This was validated by Mr. Vince Gaspar, who said that the only important requirement is for production quantities to be satisfied. Given those results, it can be said that the use of the production planner is beneficial in several ways. First, the planner is easy to use, even if in MS_DOS. Next, the output is able to satisfy all demand and safety stock requirements, as well as production capacities. Also, manual computations are now done automatically by the program. Machine utilization can also be improved because of the maximization constraint. Lastly, even if tedious data inputs are required by the program, the interface was improved to minimize input errors through systems pauses and paragraph formatting. Several improvements can still be done on the model to minimize deviations from actual results. A database linked to the program can be added to remove data input. Additional constraints such as sequencing.