Study On Supplier Selection And Order Allocation In Professional Hairdressing Products Wholesale Enterprise

With the sustainable development of social economy, the hairdressing industry is becoming a big consumption factor in the national economy. More people began to request their external image, thus prompting the demand for professional hairdressing products and services surged. The professional hairdressing products are mainly supplied from the wholesale enterprises to the hair salon to satisfy the customer demand in the six aspects of wash, cut, blow, perm, dye, care. The hairdressing products are usually with various types, large quantities and quick changed demand. In order to stand out from the increasingly fierce market competition, many enterprises have attached more emphasis on sourcing in the supply chain management as the qualification of the suppliers is uneven as well as the relevant product standards are not perfect currently. In particularly showing a greater concern on reducing the procurement costs, uplifting the management quality and improving the work efficiency through optimize the supplier selection and order allocation. Therefore, it is significant to build a hairdressing wholesale entreprise suited supplier selection, evaluation system and order allocation modeling.This paper takes the hairdressing product supplier selection and order allocation as object, adopts analytic hierarchy process and genetic algorithm as methods to carry out the research. Firstly, analyzes the current sourcing situation and lack faced. Secondly, designs an appropriate audit and evaluation system for the supplier qualification and performance. Thirdly, establishes an order allocation model with the purchasing cost minimizing objective function which considered the multiple vendors, multiple product types and the uncertain demand during different periods from the wholesale entreprise view. Finally makes the professional hairdressing products purchasing order allocation simulation as the practical example to output the satisfactory solution to the problem through genetic algorithm run by MATLAB. The results improve the work quality as well gain the competitiveness for the enterprise.