This article shares findings from a warehouse operation improvement project. A simulation model, purchased from the SCDA shop, was customized and applied in a simulation study that aimed at improving operational efficiency in a dristribution warehouse linked to large batch production of plywood and other wooden construction material. The simulation model was used to evaluate and confirm zoning based warehouse improvement initiatives. Based on historical product- and customer-based order patterns outbound shipments were categorized and stored in dedicated warehouse zones. As confirmed by the simulation study, using a customized version of a SimPy based discrete-event simulation model from the SCDA shop, this warehouse zoning strategy reduced internal transport stress through shorter average movement cycles in the warehouse. Financially, this resulted in reduced internal transporter fleet size.

Based on expected storage dwelltimes shipments were stored in one out of eight storage zones

Based on historical order data, available per material number, customer id, and shipping destination, upcoming shipments were grouped into eight different categories of “slow vs. fast movers”. Below image shows an animated version of the shipping area.

The concept is simple. Fast movers occupy storage area for shorter time. They should thus be stored near the dispatch gate, as forklifts circulate between storage area and dispatch gate. Storing fast movers near the dispatch area ensures that this area, associated with the shortest internal transport time, is used as much as possible. Slow movers, on the other hand, should not be stored near the dispatch area. Instead, they should be stored in some distance away, ensuring that the storage area next to the dispatch process has capacity for frequently stored and shipped fast movers.

Below image gives another overview of the dispatch area with a total of 2 x 3 = 6 truck gates.

Plywood stacks, or other wooden construction materials, arrive in the dispatch warehouse from production through the gate on the right. They are stored in the dispatch warehouse and assigned to an upcoming shipment. Once the associated truck arrives at one of the truck gates plywood stacks are picked from the storage slot and moved directly into the truck.

A simulation model from the SCDA shop was cutomized and used for evaluating the zoning concept

Using a discrete-event simulation model, purchased from the SCDA shop, a simulation study was conducted. The simulation considered the following aspects:

• Production schedule
• Shipping schedule
• Work shift plan for dispatch area
• Forklift configuration
• Warehouse layout
• Proposed zone dimensions and arrangement

The simulation model was furthermore used to improve the warehouse zone dimensions, the number of requried forklifts and forklift drivers, and to fine tune the shipment categorization system developed with statistical methods from historical customer order and outbound shipping data.

Zoning concept significantly reduced average forklift move times, regardless of warehouse fill degree

Various sensitivity tests were conducted. For example, the impact of storage fill degree (i.e. the average number of occupied vs. available storage slots) onto the difference in average move times was evaluated. Below chart shows the results. While the the zoning concept seems to be even more effective at lower fill degrees it does also improve forklift productivity at very high fill degrees.

Note: Move time is the average time for picking or placing a plywood pack in the warehouse, and excludes load handling times, labelling times, and other times that are not directly related to forklift movement distance.

A Python-based discrete-event simulation model, running on SimPy, was used

The simulation model was originally purchased from the SCDA shop. A simple taining model was obtained and then customized. Customizations were necessary to e.g. reflect

• the categorization system,
• the production schedule,
• the shipping schedule logic.

The simulation model was developed in Python. It runs on a module called “SimPy”. You can find many SimPy training models and use case examples in the SCDA shop. Models can furthermore also be made available as web-apps or desktop executables, allowing users to execute models without ever having to download code, read code, or use powershell or other terminals for model execution. Instead, a simple double click can be enough to run and execute a pre-developed custom simulation model (based on input data provided in e.g. Excel files or similar).

• Link: Warehouse zoning SimPy model

Other content related to warehousing and warehouse simulation

If you want to learn more about how simulation and analytics can help you in achieving operational excellence in warehouses and factories please contact us, or check some of the following examples and articles:

• Link: Single warehouse allocator
• Link: Warehouse assignment (CBC)
• Link: Job shop SimPy Python simulation
• Link: Pallet receival simulation (SimPy)
• Link: Perishable SCM SimPy library
• Link: Poultry supply chain simulator
• Link: U-shaped warehouse AGV simulator (SimPy)

Linnart Felkl

Data scientist focusing on simulation, optimization and modeling in R, SQL, VBA and Python