Hidden Costs of Warehousing — Travel Distance and Travel Time

Siddharth Desai
Clean Slate Technologies
4 min readOct 5, 2019


Warehousing is the intermediate process in the logistics value chain which involves storage of goods during a product’s journey from the factory to the end-consumer. There is a trove of information and answers to the question: what makes one warehouse more efficient than the other? Some of the obvious ones involve people, process, storage, material handling equipment and technology systems. A warehouse management system aids in controlling and managing the movement and storage of materials within a warehouse with data around process level transactions such as receiving, put-away, picking, shipping and replenishment. However, even with a state of the art WMS, there are still many “hidden costs” that can have a major impact on the bottom line, customer satisfaction and overall business success. What makes these costs hidden is lack of means to measure, analyse and optimize factors leading up to these costs. In this series, we’ll try to unearth a few of the hidden costs. This article covers a couple of significant factors contributing to hidden costs in a warehouse: travel time (not to be confused with time travel which we’ve come to believe has no cost, hidden or otherwise).

Around 75% of warehouses retrieve products manually. In these warehouses, forklifts or mechanized MHE such as EOT Cranes or tow-carts are the most expensive machines due to equipment, labor and maintenance costs.

Travel time is the second most expensive operating cost in a warehouse accounting for 60–70% of the labor costs which is the most expensive operating cost. Because travel is an activity, it’s sometimes difficult to see the hidden waste within that activity.

Excessive travel might be attributed to multiple reasons in a warehouse which results in lower throughput rate (units received/put-away/picked/replenished/fulfilled per standard man-hour).

  • Sub-optimal location storage or slotting of products: Improper storage of material in the warehouse increases the number of pallet positions required to store the item and the operational efforts to put-away and pick the material. For example, products are grouped only based on SKU types, sizes or storage requirements without accounting for product velocity or vice-versa, the distance traveled will be higher.
  • Order picking: Order picking is often the most preferred method of picking, but may not be the most effective. An order picker picks one order at a time, following a route up and down each aisle until the entire order is picked. This method may work well in an operation with a low number of orders, and a high number of picks per order; however, using this method in a warehouse with a large number of smaller orders would lead to excessive travel time. Even a typical WMS picking application might simplistically batch orders by grouping the first X orders in the work queue, without taking travel distance or other factors into account resulting in a higher travel distance and lower productivity.
  • Paper based put-away and picking: If operator works according paper-based task list, at first forklift or MHE moves to computer station where all tasks lists are placed, then moves to certain storage location to take pallet according to the task list, at the end, delivers pallet to a pick location and then moves to the computer station to confirm the completed task. The trip to computer station takes some minutes.
  • Single task sequencing: In order to perform put-away operations forklift has to return from the aisle to receiving area with empty forks. The same is the case with pallet pick operations. When each of these tasks are performed independently, the travel distance increases. In the above 2 scenarios, the distance when forks are empty is equal to 66% of total travel distance.
  • Inefficient routing: In a large warehouse or DC, inefficient routing based on operator’s memory of the shortest routes to material’s location during put-away, replenishment and pick leads to a higher travel time and distance resulting in capacity under-utilization and lower throughput

While most organizations face these challenges, an oft-repeated practice during peak seasons to just get through the work as a short term measure, managers just add additional labor and equipment which adds costs.Do share your feedback, perspectives and ideas about any other trends worth discussing.

Drop in a word or just say hi at siddharth@cleanslatetechnologies.com. Our next article will cover some additional hidden costs and how to measure, analyse and reduce these hidden costs.

Siddharth, Ceo

Clean Slate Technologies

References: Logistics Bureau, Burinskiene, A — Faculty of Business Management, Vilnius Gediminas Technical University, SupplyChain 247, Cyzerg, Zeninventory, NEC