In today's intensely competitive and highly volatile business environment, consistent development of low cost and high quality products meeting the functionality requirements is a key to a company's survival. Companies continuously strive to reduce the costs while still producing quality products to stay ahead in the competition. Many companies have turned to target costing to achieve this objective. Target costing is a structured approach to determine the cost at which a proposed product, meeting the quality and functionality requirements, must be produced in order to generate the desired profits. It subtracts the desired profit margin from the company's selling price to establish the manufacturing cost of the product. Extensive literature review revealed that companies in automotive, electronic and process industries have reaped the benefits of target costing. However target costing approach has not been applied in the machining industry, but other techniques based on Geometric Programming, Goal Programming, and Lagrange Multiplier have been proposed for application in this industry. These models follow a forward approach, by first selecting a set of machining parameters, and then determining the machining cost. Hence in this study we have developed an algorithm to apply the concepts of target costing, which is a backward approach that selects the machining parameters based on the required machining costs, and is therefore more suitable for practical applications in process improvement and cost reduction. A target costing model was developed for turning operation and was successfully validated using practical data.
Database mining, widely known as knowledge discovery and data mining (KDD), has attracted lot of attention in recent years. With the rapid growth of databases in commercial, industrial, administrative and other applications, it is necessary and interesting to extract knowledge automatically from huge amount of data. Almost all the organizations are generating data and information at an unprecedented rate and they need to get some useful information from this data. Data mining is the extraction of non-trivial, previously unknown and potentially useful patterns, trends, dependence and correlation known as association rules among data values in large databases.
In last ten to fifteen years, data mining spread out from one company to the other to help them understand more about customers' aspect of quality and response and also distinguish the customers they want from those they do not. A credit-card company found that customers who complete their applications in pencil rather than pen are more likely to default. There is a program that identifies callers by purchase history. The bigger the spender, the quicker the call will be answered. If you feel your call is being answered in the order in which it was received, think again.
Many algorithms assume that data is static in nature and mine the rules and relations in that data. But for a dynamic database e.g. in most of the manufacturing industries, the rules and relations thus developed among the variables/items no longer hold true. A simple approach may be to mine the associations among the variables after every fixed period of time. But again, how much the length of this period should be, is a question to be answered. The next problem with the static data mining is that some of the relationships that might be of interest from one period to the other may be lost after a new set of data is used. To reflect the effect of new data set and current status of the association rules where some of the strong rules might become weak and vice versa, there is a need to develop an efficient algorithm to adapt to the current patterns and associations.
Some work has been done in developing the association rules for incremental database but to the best of the author’s knowledge no work has been done to do the same for periodic cause and effect analysis for online association rules in manufacturing industries. The present research attempts to answer these questions and develop an algorithm that can display the association rules online, find the periodic patterns in the data and detect the root cause of the problem.