# Using Product Network Analysis to Optimize Product-to-Shelf Assignment Problems
## 5 Discussion and Conclusions  
To attract customers and survive in a competitive environment, retailers need to implement appropriate retail-mix strategies including store location, product assortment, pricing, advertising and promotion, store design and shelf display, services, and personal selling. Among these, shelf-space allocation is one of the most important factors in determining customer purchasing decisions. Recently, advances in information technology have made it easier for retailers to collect various types of customer data. Mining this data for insight into customer behavior can help retailers solidify ephemeral relationships with customers into long-term loyalty. As part of this effort, retailers have modified their shelf-space management practices using product association analysis. Previous studies have demonstrated that product association analysis can improve the efficiency of shelf space usage and increase cross-selling. This study solves the persistent product-to-shelf allocation problem by integrating data mining and network analysis, and makes three major contributions. First, the study compares the effectiveness of transaction-based networks (TBN) and customer-based networks (CBN). Experimental results show that the two network types produce very different association values among products. If store managers want to reduce side effects caused by customers repeatedly purchasing the same products, CBN is a better choice than TBN. Conversely, TBN is more useful if store managers treat all transactions as equally important. Second, this research uses network analysis to evaluate product centrality, and uses the resulting centrality value to classify products as attraction items, opportunity items, or trivial items. Attraction items are popular products which attract customer visits, and should be kept in consistent locations making them easy to find. On the other hand, opportunity items should be relocated to increase cross-selling. Third, this study considers product association along with physical proximity and category constraints when solving the product-to-shelf assignment problem. Some potential extensions for this research are as follows. First, the proposed method uses closeness and betweenness to measure product centrality in the product relationship network. Different centrality measures give different meanings and future studies should apply a wider range of centrality measures. Second, this study assumes product volumes are identical and can be smoothly exchanged. However, in practice, different products might be displayed in different volumes, and future work should consider this issue. Third, some additional limitations that could be addressed are those concerning restrictions on storage conditions of certain products (e.g. frozen or refrigerated products) or consideration of product relocation costs. Meanwhile, it would be interesting to take the height of positioning within a shelf into consideration. Fourth, this study solves the product-to-shelf assignment using the Hungarian method, which would run too slowly given large quantities of data. Finally, \begin{table}
\begin{tabular}{c c c c} \hline \hline \(\alpha\) & \(\beta\) & **Number of Attraction Items** & **Number of Opportunity Items** \\ \hline
150 & 135 & 34 & 29 \\
150 & 140 & 34 & 9 \\
150 & 145 & 34 & 5 \\
160 & 135 & 24 & 39 \\
160 & 140 & 24 & 25 \\
160 & 145 & 24 & 15 \\
170 & 135 & 20 & 43 \\
170 & 140 & 20 & 29 \\
170 & 145 & 20 & 19 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Number of attraction and opportunity items using closeness centrality. the proposed method is now validated and evaluated using simulated behavior data produced by a transaction sequence generator, and future work should apply authentic store transaction data. Conceptualization, H.H., C.-Y.T. and R.J.K. ; Formal analysis, J.-S.C.; Funding acquisition, H.H. ; Methodology, C.-Y.T. ; Supervision, C.-Y.T. ; Writing--original draft, J.-S.C.; Writing--review & editing, L.Y.