An information technology approach to production and operations decision making in dairy processing

The possibility of integrating capacity information into computeraided information systems of a processing plant can lead to a much improved system of production planning for the food industry. By applying Matrix Data Structures and the Gozinto Procedure the knowledge of capacity utilization, distribution and limitation is collected into a firm's database systems. With this information on hand, operational managers can check the feasibility of a new production plan before implementation. Operations researchers are capable of determining constraint coefficients corresponding to the capacity limitation. Using this information top management may search out superior production plans to improve profitability. Such actions and analyses serve to support decision making for better judgments. These methods promise a better operational performance and thereby promise to enhance the firm's business success.; There is no significant evidence to show that there is a linear relationship between the quality of fresh cheese and the rate of acid production during cooking. The complexity of acid production during cheesemaking seems to be more due to the biological dynamic system with the starter and all variables that affect the activity of bacteria culture. An artificial intelligence system is recommended to model and simulate this biological, dynamic system.; An artificial neural network application can be useful for estimating acid production characteristics during cooking stages in cheese making. This neural network model may be used for online predicting of cheese quality. A forward feed multi-layer structure with back-propagation learning can be used to train the model. The network seems to be capable to predict the quality of a four day old mild cheddar cheese with a reasonable (90%) degree of accuracy. It is concluded that ANNs are well suited to predict this production process. The developed methodology can be used in the cheese manufacturing to evaluate cheese quality in a multi-step-ahead prediction. Artificial neural networks can be used to provide additional information to increase reasoning and monitor power in the control, systems. It may be possible for process automation and adaptive control systems in the dairy with particular reference to the cheese manufacture.