Maintaining a stable master production schedule ( M P S is difficult for many firms, especially when material requirements planning is used to manage production operations This paper is concerned with the problem of measuring MPS stability, and the impact on stability of three important decision variables in managing the MF5 within a rolling-horizon Framework in a make-twstock environment: the method used to freeze the MPS, the proportion of the MPS frozen, and the length of the planning horizon for the MPS. Simulation aperiments conducted to determine the impact of these decision variables, as well as other important p d u d denand and cost chamcteristics, on MPS stability arc rrported. The results indicate MPS stability can be influenced by managerial action dimted toward management of the M P S as well as changes in important product cost and requirements characteristics. Subject Arrcrr: Material Rquimnenrr Phnning, Pkrnnhg and Conhol, Production/ Opcmtions Managauent, and ScheduUng.

The stability of the Master Production Schedule (MPS) is a critical issue in managing production operations with a Material Requirements Planning System. One method of achieving stability is to freeze some portion or all of the MPS. While freezing the MPS can limit the number of schedule changes, it can also produce an increase in production and inventory costs. This paper examines three decision variables in freezing the MPS: the freezing method, the freeze interval length, and the planning horizon length. Simulation experiment results are reported which suggest that freezing up to 50% of the planning horizon has a marginal effect on production and inventory cost under a wide range of operating conditions. These results also suggest that an order based freezing method produces superior results in comparison with a period based method.production/scheduling: material requirements planning, simulation: applications, inventory/production: planning horizons

Crude oil is the mixture of petroleum liquids and gases that is extracted from the ground by oil wells. It is an important source of fuel and is used in the production of several products. Given the important role price of the crude oil plays, it becomes extremely important for managers to predict future oil price while making operational decisions such as: when to purchase material, how much to produce and what modes of transportation to use. The goal of this paper is to develop a forecasting model to predict the oil prices that aid management to reduce operational costs, increase profit and enhance competitive advantage. We first analyze the primary theories related to the forecast of oil price followed by the reviews of two main streams of forecast theory, which are Target Capacity Utilization Rule (TCU) and Exhaustible Resources Theory. We implement a Target Capacity Utilization Rule recursive simulation model and test it on the historical data from 1987 through 2017 to predict crude oil prices for 1991 through 2017. We tried several variations of the base model and the best method produced MAD, MSE, MAPE and MPE of 12.676, 280.92, 0.2597, 0.028, respectively. We further estimated the forecasts of the oil prices at a monthly level based on our yearly forecast of oil prices from our best method. The calculated MAD, MSE, MAPE and MPE values are 5.66, 82.1163, 0.1246 and 0.038, respectively, which shows our model is promising again at a monthly level.

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