Production Planning and Control (PP&C) has been deeply analyzed in the literature, both in general terms and focusing on specific industries, such as the fashion one. The paper aims to add a contribution in this field presenting an optimization model for the Fashion Supply Chain (FSC), developed considering an interdependent environment composed by a group of focal companies that work with both exclusive and not-exclusive suppliers. The proposed framework will combine simulation and optimization models based on parameters, decision variables, constraints and Objective Functions (OFs) collected through a literature review. The framework has been developed in a parametrical way, in order to fit the peculiarities of the different actors operating along the FSC. The empirical implementation of the framework has been conducted using data coming from fashion companies belonged to the same network, considering rush orders as stochastics events for the scenario analysis and Key Performance Indicators (KPIs) assessment.
The fashion industry is one of the sectors where many contributions can be found along the whole value chain, from the New Product Development (NPD) to the logistics and retail.
Nevertheless, most of them are focused on the definition of the Critical Success Factors (CSFs), such as high-quality products, compliance with delivery dates, cost reduction, and sustainability issues (May et al. 2015), the role of the IT, the alignment of the physical with the information flows (Caniato et al. 2015), the importance of quality control (Brun et al. 2014) and, even when they deal with workflow allocation, most of them are related to the brand owners’ perspective. What is evident is that focal companies have faced with an increasing attention to KPIs. As a consequence, all of the SC actors have been required to increase their performances by the brand owners, in line with the increased market pressure. At the same time, it is noticeable that these results cannot be obtained operating at a singlecompany level, but considering the entire FSC, because the outstanding quality of a final product is strictly linked to that of its components and, in the same way, the delay of the final product depends on components delays.
Moreover, this evidence is more explicit in the fashion industry, where “time” represents the key word for being competitive on the market in a complex environment characterized by short product lifecycles, high product variety and fragmented supply bases.
According to this, the work aims to define a structured framework to optimize the production planning and scheduling of the production within a FSC, with the use of a solver and a simulator.
The paper is organized as follows. In Section 2, we have presented a brief literature review on production planning and scheduling models, with a focus on the fashion industry. The proposed model has been detailed in Section 3, and its application in a case study has been shown in Section 4. Finally, in the last section we discuss the main conclusions of this work.
The case study is developed using a simplified set of data coming from two different brands, working with both exclusive and common suppliers. In detail, the set of data considered for each brand owner refers to one exclusive supplier and another that works for both the brands.
Figure 1: Simulation model production plan schema description.
The brand owners operate in the leather accessories industry and produce bags with different dimension and complexity, clustered into three different product categories (i.e. easy, medium and difficult). All three of them are realized by a not-exclusive supplier (i.e. S2 in Figure 1), that works for both the brand owners (i.e. B1 and B2). Once the optimization model has been applied to the three of them (i.e. B1, B2, S2), the simulation model is used to conduct the scenario analysis for evaluating the effect of rush orders on the system performances. The importance of including rush orders is due to the uncertainty and high variability of the brand owners’ production orders. Unexpected orders can represent a high proportion of the value of the production, up to the 20% of the total capacity.