# Sales Forecasting for Fashion Products Considering Lost Sales
## 5 Conclusions, Limitations and Future Research  
In this work, a TLs model is proposed for sales forecasting in the fashion industry. To the best of our knowledge, although the sales forecasting model has been explored by a great number of researchers, the TLs model proposed is brand new. The TLs model delves into the relationship between sales demand and inventory and collaborates two parameters to describe the conversion rate of inventory to sales. To solve the proposed model, feature selection and clustering are conducted to process the historical data, and a GA algorithm is used to operate the parameter estimation process. Furthermore, the proposed model is compared with the LR, GBDT, SVR and ANN models on historical data provided by a multinational fashion retailer in Singapore. The experimental result shows that the TLs model outperforms other models in most cases in terms of forecast accuracy. The four traditional methods do not show significant differences when forecasting, and the forecasting performance of ANN is unstable. Moreover, the results give a conclusion that in the shoe dataset, cluster 1 is a targeted commodity and cluster 2 is a common commodity, while cluster 3 is a major commodity. For belts, clusters 1 to 3 are common commodities, major commodities and targeted commodities, respectively.  
Moreover, two indicators are built based on our TLs model: the average conversion rate and the marginal conversion rate. By defining the average conversion rate, a measure of product competitiveness is put forward for fashion managers. As for the marginal conversion rate, it is an auxiliary index for deciding on a suitable inventory decision. By setting the acceptable rate based on factors such as inventory cost, sales profit and production scale, the TLs-decision formula provides managers with the recommended inventory level.  
Nevertheless, the proposed TLs model shows limitations. The TLs model forecasts the total sales of all stores, which does not consider the forecasting problem of sales in each brick-and-mortar store and periodical sales. Here, a top-down approach could be applied for further research. Moreover, the TLs model could consider alternatives for techniques in each step of the proposed algorithm to achieve better performance. A possible improvement for further work is to find an alternative method to replace the linear regression used in the first layer. Moreover, more effort could be put into extending the TLs model with more realistic constraints and mining deep informationaboutf products, such as the impact of feature crosses on sales.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Item**} & \multirow{2}{*}{**Inventory**} & \multicolumn{3}{c}{**TLs Inventory Decision**} \\ \cline{3-5}  & & \(\mathbf{\zeta=0.65}\) & \(\mathbf{\zeta=0.70}\) & \(\mathbf{\zeta=0.75}\) \\ \hline SKU1 & 32 & 67 & 61 & 54 \\ SKU2 & 167 & 91 & 83 & 73 \\ SKU3 & 43 & 70 & 64 & 56 \\ SKU4 & 49 & 100 & 79 & 69 \\ SKU5 & 17 & 37 & 33 & 29 \\ SKU6 & 52 & 69 & 63 & 55 \\ SKU7 & 31 & 88 & 81 & 71 \\ SKU8 & 106 & 104 & 95 & 83 \\ SKU9 & 48 & 78 & 72 & 62 \\ SKU10 & 25 & 93 & 85 & 74 \\ \hline \hline \end{tabular}
\end{table}
Table 6: TLs inventory decision for the SKUs in shoes cluster 3.  
**Author Contributions:** Investigation, W.L.; Methodology, D.C.; Supervision, F.L. and K.Z.; Writing original draft, K.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by National Natural Science Foundation of China of grant number 71801124.  
**Conflicts of Interest:** The authors declare no conflict of interest.