The selection of fresh product suppliers is a multi-criteria decision making (MCDM) problem with great significant and application value. This requires trade-offs between multiple criteria to prove its ambiguity and uncertainty. Therefore, a novel two-stage fuzzy integrated MCDM method to select suitable suppliers is employed. In the first stage, two collective relationship matrixes are constructed by quality function development (QFD), and relationships among customer requirements (CRs), company strategies (CSs) as well as selection criteria are considered separately in the two matrixes. Subjective criteria weights are obtained by fuzzy best-worst method (BWM) appropriately. In the second stage, the objective criteria weights are obtained using Shannon’s entropy method, and the fuzzy multi-objective optimization by ratio analysis plus the full multiplicative form (MULTIMOORA) is applied to rank suppliers. Finally, an application case is applied to prove the feasibility of the proposed method. These conclusions can help companies improve their CSs and increase their market competitiveness.

Green production has become an important issue in PCD (Product Collaboration Design) for almost every enterprise, and will determine the sustainability of enterprises in the long term. The choice of design partner is a necessary condition in order to achieve green production. For the uncertain, fuzzy, and dynamic information such as unknown indices and weights, fuzzy semantics, and dynamic time factors in GPCD (Green Product Collaboration Design), a two-stage dynamic hybrid MADM (Multi-Attribute Decision Making) approach based on fuzzy DEMATEL (Decision-Making and Trial Evaluation Laboratory), fuzzy KMA (Karnik–Mendel Algorithm), and fuzzy VIKOR (VlseKriterjumska Optimizacija I Kompromisno Resenje) was proposed. In the first stage, fuzzy DEMATEL was used to determine the evaluation indices. Then, in the second stage, to accurately depict the dynamic information generated by the different phases of a product design, the dynamic evaluation method based on fuzzy theories was employed, and the weights of the indices were calculated by fuzzy KMA, then sorted by fuzzy VIKOR. Finally, a case study and a comparative analysis wre provided to illustrate the effectiveness of the proposed approach.