The identification of part families and machine groups that form the cells is a major step in the development of a cellular manufacturing system. The primary input to cell formation algorithms is the machine-part incidence matrix, which is a binary matrix representing machining requirements of parts in various part families. One common assumption of these cell formation algorithms is that the product mix remains stable over a period of time. In today’s world, the market demand is being shaped by consumers, resulting in a highly volatile market. This has given rise to a class of products characterized by low volume and high variety, which presents engineers with lots of problems and decisions in the early stages of product development. This can have an adverse effect on manufacturing like high investment in new machinery and material handling equipment, long setup times, high tooling costs, increased intercellular movement and excessive scrap which increases the cost without adding any value to the parts. Any change to the product mix results in a change in the machine-part incidence matrix, which may change the part families and machine groups, which form the cells. The manufacturing system needs to be flexible in order to handle large product mix changes. This paper discusses the impact of product mix variations on cellular manufacturing and presents a methodology to incorporate these variations into an existing cellular manufacturing setup.
Formation of machine groups and part families in cellular manufacturing systems using a correlation analysis approach
