# Data Mining and Augmented Reality: An Application to the Fashion Industry
## 4 Results  
### Association Rule Mining  
#### 4.1.1 Sampling Rules  
The first objective pursued through the ARM is the redefinition of the sampling rules, following a data-driven approach. As defined according to the company's requirement, the ARM for sampling rules is carried out every season, that is, in the fashion industry, the most relevant time partition during the year. However, due to the short amount of time needed to carry out the process, the time window can be shortened in specific cases, e.g., change in supplier production processes or sensible deterioration of performance.  
An example of this analysis is proposed with reference to supplier 7: specifically, different sample sizes are applied to evaluate their product: the sample size of 100%, according to the confidence (Conf = 1.000), the supplier always presents reliable outcomes (i.e., the referred association rule is _Supplier code = 7_, _Sample size = 100% - Outcome = Compliant_). According to the statement made at point 1), the ARM is not further detailed due to the excellent performance, and the reduction of the sample size could be taken into consideration. When products are evaluated through a sample size of 15%, the final outcome is \"Compliant\" in 94.1% of cases (Conf = 0.941)--i.e., the association rule can be expressed as _Supplier code = 7_, _Sample size = 15% - Outcome = Compliant_. Hence, the analysis can be detailed by product category so that specific criticalities related to some product categories can be assessed. Small leather goods and bags product categories are controlled with a sample size of 15% and are compliant in 96.0% and 93.2% of cases, respectively (i.e., the association rules can be reported as _Supplier code = 7_, _Sample size = 15%_, _Product category = Bags -> Outcome = Compliant - conf = 0.932_; _Supplier code = 7_, _Sample size = 15%_,  
Figure 3: Quality control process for the analyzed company (* QC = Quality Control, ** WO = Work Order).  
_Product category = Small Leather Goods -> Outcome = Compliant - conf = 0.960_). There is no substantial imbalance to suggest focusing on one category over another: both rank with good acceptance rates, but neither of them ranks perfectly. Hence, in both cases, more details could be provided.  
As an example, the results obtained by adding the material for the bags are provided (Table 3). In the rule in which material 661 appears, the Conf = 0.454 indicates that the lots will have to be rechecked in 45.4% of cases, meaning that the selected sample does not achieve the required quality threshold; also, the batch is urgent. Hence, it needs to be rechecked immediately in order to be delivered at least partially (i.e., only compliant products will be sent to the customer). Products made of materials 118 and 475, instead, present non-compliancy in 14.3% and 25.0% of the samples. They are not urgent; thus, the whole lot can be sent back to the supplier for complete reconditioning. In any case, understanding the root causes of products' non-compliance can support the adoption of corrective actions aimed at avoiding batches' rejection. Due to the different distribution of compliant and non-compliant outcomes per article material, different stages of quality controls could be adopted: indeed, when the 15% sample does not achieve the adequate quality level, company policy requires an inspection of the whole lot. If the supplier has historically achieved satisfying performances in the second stage, the 100% percentage could be reduced to 50% or 30% in order to be able not to maintain an adequate control level and a fair duration of the control process.