Warehouse Layout Space Optimization Solution

(Warehouse SOS)

Lieu Hoang Long
Ho Minh Duc
Nguyen Chu Son
Duong Tuan Dat

Today, businesses evolve rapidly leading to the demand of companies to compete in various aspects of globalization. According to Alexander et al. [1], approximately 20% to 50% of the overall operating expenses in manufacturing firms are coming from products storage and handling. Nevertheless, efficient tool planning is expected to reduce approximately 10% to 30% of those costs. Besides, facilities and storage space design are an essential process for which firms improve sophisticated systems to ensure to meet the demand in quality as well as quantity of products and permanent development of internal processes [1]. Therefore, Intel’s honeycombing is a crucial problem that needs to be executed in order to enhance the operation within the warehouse. Their FEW warehouse is being used to store their tool crates, its risk and cost can be up to hundred million per tool crate relocation. For that reason, our team Era X Solution is going to find a solution through computing to visualize and improve the warehouse’s status.

Most warehouses are facing the problem of inadequate storage space in which around 85% to 90% of overall storage is in the continuous use condition [2]. Although the ultimate target from the point of the storage’s cost is to make the most of the space in the warehouse through high-density storage and block stacking which can also decrease the performance of the warehouse’s operation. Thereby, with such an enormous level, the warehouse will not be able to have enough space for storage even with a slight growth in inventory Interlake [3]. 

For Intel’s problem, our core scope is to deal with the honeycombing rate of the FEW warehouse, this problem is needed for the warehouse operator within the warehouse to do planning more automated instead of manually packing through datasheets. After that, we can move on to our next target, which is optimization of new items on the usable space. This usable space is not like the honeycombing, they are usable because that area can fit the smallest item from the tool list.  In conclusion, after we have these core functions, it is possible for people within the warehouse to estimate the risk and cost by using our optimized packing plan. Since last semester, our main objective has not changed, as we still try to enhance our app to run through different samples that were provided by Intel and had the best output in both performance and satisfied results.