Investigating the Effect of Product Reallocation on Warehouse Order Picking Efficiency - Design and Development of a Reallocation Model

Abstract

The optimal functioning of supply chains is imperative for companies to stay competitive. A significant part of the cost reduction in supply chains can be achieved through efficient warehousing. As market dynamics have changed, coupled with the additional strain on warehouse activities, the importance of streamlined warehouse operations has increased. Order picking, the biggest cost driver in a warehouse, has been proven to be of particular importance.

The main purpose of the thesis was to investigate storage assignment strategy and the incremental reallocation of products to improve the picking efficiency. The first phase of the thesis entailed summarising previous related literature on the topic of picking efficiency, namely storage assignment strategies, routing, order picking methods and product reallocation strategies to identify literature gaps. The second phase included developing a reallocation model suitable for a future integration into a warehouse management system.

To conduct the thesis, a deductive approach was used with a combination of applied science and design science research as the research strategy. The literature review followed a systematic approach, with defined exclusion, inclusion criteria and key words. To evaluate the proposed model, quantitative warehouse data was collected from a discount retailer and used for the simulation experiments.
The outcome of the thesis can be characterised into three parts. The findings of the literature review highlighted that warehouse design factors are multi-layered, thus entailing a large complexity in optimisation problems. Additionally, the literature review found that traditional research is focused on inefficient procedures and calculations, limited to pair-wise SKU correlations and matrix constructions. Moreover, under what circumstances correlation-based slotting strategies works have not been properly mapped. Lastly, the concept of incrementally reallocating products, when a storage assignment strategy is already applied, has received limited attention in literature.

The development resulted in a model using a rule-based clustering technique to periodically reallocate products. The model is partitioned into a pattern mining, clustering, and storage location assignment phase. To evaluate the model, a computerised representation of the case company warehouse was built together with the picking simulation. The simulations highlight that the proposed model shows improvements on the current storage assignment of the case company, albeit being small. The findings obtained and presented in this thesis should not be seen as final, rather as a proof of concept. Further testing and evaluations are recommended before a wider implementation effort is undertaken.

Popular Abstract

The ordinary consumer might not reflect on how a t-shirt, phone or book has been transported across the country, continent or the entire world. Even more so, a buyer won’t think about in which way his or her product has been stored in a warehouse and physically been picked. For warehouse workers and management, working behind the scenes, designing efficient warehouse operations is a well-planned and thoughtful process.

At some point in our lives, most of us have attempted assembling a puzzle. Albeit seeming simple at first, the process of finishing a puzzle requires planning, thinking and patience. Many of us know the frustration of searching for similar-looking pieces that are supposed to fit perfectly, and the satisfaction of finishing a puzzle.

Now, imagine a warehouse and the orders it receives as a giant puzzle, where each piece of the puzzle is a product that must be picked. Depending on what type of market the warehouse is serving, the specific strategies used to design efficient order picking operations differs. That is to say, no puzzle and warehouse are alike. Just as assembling a large puzzle is a cumbersome task, the process of designing efficient order picking processes requires meticulous planning and thought. For warehouses, optimising the picking efficiency rests on four key concepts. In analogy with a puzzle, routing considers finding the shortest path between all the pieces. The warehouse layout design concerns arranging a suitable workspace to assemble the puzzle. Order picking method regards different strategies to assemble the pieces while storage assignment concerns sorting, placing and categorising the pieces to help the assembly process.

The pieces of a puzzle, as the products of orders, are associated with each other. Some pieces are more frequently used than others, increasing the ‘importance’ of those pieces to finish the puzzle. This is very straightforward. However, finding and understanding which pieces that fit best together is progressively harder, especially as the size of the puzzle increases. Adding to this, imaging having a puzzle that is dynamic, where a piece that fits perfectly into its current spot may become misplaced in the future.

How do we combat the problem of a changing puzzle environment and keep an efficient assembly process? One common way to do it would be to reassess larger sets of pieces less frequently, consequently reshuffling larger parts of the puzzle. But what if the pieces where rearranged more frequently and gradually as new information of the puzzle becomes available? In the master thesis ‘Investigating the Effect of Product Reallocation on Warehouse Order Picking Efficiency’ by Daniel Tomic (2024) the incremental reallocation of products was studied to see if it could improve the picking efficiency of an e-commerce and retail warehouse. A novel product reallocation model, based associations between products was developed, displaying small improvements to the picking efficiency in the simulations. The warehousing context likely affected the magnitude of the efficiency gains, highlighting the multi-faceted complexity of picking efficiency optimisation. Preferably it should incorporate a systems approach, targeting multiple factors. However, this has been proven to be particularly challenging.

Finishing a large puzzle remains difficult and complex, similarly to the continuous optimisation process of picking activities. If we consider product reallocation as a small component of the broader picture aiming at improving the picking efficiency, the transition towards more comprehensive warehouse models takes a step in the right direction.

• author: Tomic, Daniel
• supervisor: Sandeep Jagtap
• organization: Production Management; Engineering Logistics
• course: MTTM05 20232
• year: 2024
• type: H2 - Master's Degree (Two Years)
• subject: Technology and Engineering
• keywords: Storage assignment strategies, product reallocation, order picking, routing, clustering, association rule mining.
• other publication id: 6026
• language: English
• id: 9167207
• date added to LUP: 2025-08-13 11:30:12
• date last changed: 2025-08-13 11:30:12