Warehouse design challenges

Almost all quality improvement comes via simplification of design, manufacturing… layout, processes, and procedures. Tom Peters

🧠 Learning objectives

Recognize the complexity of warehouse design decisions.
Identify key warehouse design activities.

Warehouse design challenges

Figure 1: Warehouse design problems (Gu et al. 2010)

Design decisions are tightly coupled:

Decisions cannot be analyzed or determined in isolation from the others.
- Addressing one decision requires a research infrastructure integrating all the other decisions.
- Commonly, available models are not unified or interoperable.

Evaluating the impact of changing a design decision requires estimating changes in warehouse operation (e.g., via simulation or queueing theory).

We conjecture two primary inhibiting factors:

The warehouse design decisions identified in Fig. 1 are tightly coupled, and one cannot be analyzed or determined in isolation from the others. Yet, the models available are not unified in any way and are not “interoperable.” A researcher addressing one decision would require a research infrastructure integrating all the other decisions. The scope and scale of this infrastructure appears too great a challenge for individual researchers.

To properly evaluate the impact of changing one of the design decisions requires estimating changes in the operation of the warehouse. Not only are future operating scenarios not specified in detail, even if they were, the total warehouse performance assessment models, such as high-fidelity simulations, are themselves a considerable development challenge.

— Research on Warehouse Design and Performance Evaluation: A Comprehensive Review (Gu et al. 2010, 547).

Block layout (macro) vs. detailed layout (micro)

Figure 2: It is critical that reserve storage be adjacent to receiving staging for efficient put-away (Frazelle 2016)

Figure 3: Detailed layout for a Honda parts distribution center (Frazelle 2016)

We use warehouse activity relationship charting to document adjacency requirements. It is used to suggest the location of processes and functions relative to one another in a block layout. In the example in Figure 9.6, we simply record under each interprocess relationship the importance of locating the processes adjacent to one another. For example, it is critical that reserve storage be adjacent to receiving staging for efficient put-away (Figure 9.7). Computer-aided facility layout tools take these adjacency requirements, the floor-space requirements of each process, and the location of fixed objects as inputs and compute an optimal block layout for a facility. Figure 9.8 illustrates conversion of our adjacency optimization into a block layout for a Honda parts distribution center. Computer-aided design tools allow us to convert the block layout into a detailed facility design. Figure 9.9 illustrates our conversion of the block layout for the Honda parts distribution center into a detailed distribution center layout.

— World-Class Warehousing and Material Handling (Frazelle 2016, 299–300).

Design and Layout

Main activities:

Overall structure
Sizing and dimensioning
Department layout
Equipment selection
Operation strategy (storage & order-picking)

Warehouse design involves five major decisions as illustrated in Fig. 1: determining the overall warehouse structure; sizing and dimensioning the warehouse and its departments; determining the detailed layout within each department; selecting warehouse equipment; and selecting operational strategies. The overall structure (or conceptual design) determines the material flow pattern within the warehouse, the specification of functional departments, and the flow relationships between departments. The sizing and dimensioning decisions determine the size and dimension of the warehouse as well as the space allocation among various warehouse departments. Department layout is the detailed configuration within a warehouse department, for example, aisle configuration in the retrieval area, pallet block-stacking pattern in the reserve storage area, and configuration of an Automated Storage/Retrieval System (AS/RS). The equipment selection decisions determine an appropriate automation level for the warehouse, and identify equipment types for storage, transportation, order picking, and sorting. The selection of the operation strategy determines how the warehouse will be operated, for example, with regards to storage and order picking. Operation strategies refer to those decisions about operations that have global effects on other design decisions, and therefore need to be considered in the design phase. Examples of such operation strategies include the choice between randomized storage or dedicated storage, whether or not to do zone picking, and the choice between sort-while-pick or sort-after-pick. Detailed operational policies, such as how to batch and route the order picking tour, are not considered design problems and therefore are discussed in Gu et al. (2007).

— Research on Warehouse Design and Performance Evaluation: A Comprehensive Review (Gu et al. 2010, 539–40).

Overall structure (conceptual design)

Determines the functional departments, for example:

how many storage departments,
employing what technologies, and
how orders will be assembled.

Goal: Meet storage and throughput requirements and to minimize costs.

Example 1 (Considering only storage and throughput)

Build a warehouse to store 10,000 pallets and ship 1,000 pallets per day.

Overall structure: service and product requirements

Example 2 (Considering service level)

Build a warehouse to store 10,000 pallets and ship 1,000 pallets per day, with 95% of orders shipped within 2 hours of receipt.

Example 3 (Considering product characteristics)

Build a warehouse to store 10,000 pallets of Cocoa and ship 1,000 pallets per day, with 95% of orders shipped within 2 hours of receipt.

The overall structure (or conceptual design) of a warehouse determines the functional departments, e.g., how many storage departments, employing what technologies, and how orders will be assembled. At this stage of design, the issues are to meet storage and throughput requirements, and to minimize costs, which may be the discounted value of investment and future operating costs. We can identify only three published papers addressing overall structural design.

— Research on Warehouse Design and Performance Evaluation: A Comprehensive Review (Gu et al. 2010, 540).

A combined optimization and simulation approach is proposed, where the optimization model generates an initial design, and a simulation evaluates performance, e.g., service level. If the constraints evaluated by simulation are satisfied, then the procedure stops. Otherwise, the optimization model is altered by adding new constraints that have been constructed by approximating the simulation results.

— Research on Warehouse Design and Performance Evaluation: A Comprehensive Review (Gu et al. 2010, 542).

The values 10,000 pallets, 1,000 pallets/day, 95% within two hours, and the cocoa product are course-scenario inputs. The source establishes storage, throughput, cost, and service level as design dimensions; it does not state a cocoa-specific two-hour target.

Sizing and dimensioning

Warehouse sizing and dimensioning have important implications on:

construction costs
inventory holding and replenishment, and
material handling.

Most research focuses on single-department warehouses, treating sizing and dimensioning separately.

Sizing

Determines the storage capacity of a warehouse. Scenarios:
1. Inventory levels are determined externally (e.g., public warehouse)
- Stochastic demand for storage space has to be satisfied.
1. Warehouse can directly control the inventory policy (e.g., independent wholesale distributor)
- Inventory policy and inventory costs should be considered in solving the sizing problem (e.g., reorder point and order quantity).

Common assumptions:
- Additional space can be leased.
- Costs: facility construction, inventory holding, replenishment, storage demand not satisfied (e.g., lost sales).

Capacity requirements should consider seasonality, storage policy, and order characteristics.

Warehouse sizing determines the storage capacity of a warehouse. There are two scenarios in modeling the sizing problem: (1) Inventory levels are determined externally so the warehouse has no direct control over when incoming shipments will arrive and their quantities (e.g., in a third-party warehouse) and all the exogenous requirements for storage space have to be satisfied by the warehouse; and (2) The warehouse can directly control the inventory policy (e.g., an independent wholesale distributor). A major difference is that in the latter case, inventory policy and inventory costs should be considered in solving the sizing problem. Assuming the warehouse has no control over inventory, warehouse sizing determines an appropriate storage capacity to satisfy the stochastic demand for storage space. White and Francis (1971) study this problem for a single product over a finite planning horizon. Costs considered include those due to warehouse construction, storage of products within the warehouse, and storage demand not satisfied by storage in the warehouse. Problems with either fixed or changeable storage size are modeled. The second model allows changes in the storage size over the planning horizon (e.g. by leasing additional storage space), so the decision variables are the storage sizes for each time period. A linear programming formulation is presented for the second model, and the optimal solution is found by solving a network flow problem (see also Lowe et al. (1979)). Similar problems of determining fixed and changeable warehouse size are also discussed by Hung and Fisk (1984) and Rao and Rao (1998) with different cost formulations.

Levy (1974), Cormier and Gunn (1996) and Goh et al. (2001) consider warehouse sizing problems in the case where the warehouse is responsible for controlling the inventory. Therefore, the costs in their models include not only warehouse construction cost, but also inventory holding and replenishment cost. Levy (1974) presents analytic models to determine the optimal storage size for a single product with either deterministic or stochastic demand. Assuming additional space can be leased to supplement the warehouse, Cormier and Gunn (1996) propose closed-form solution that yields the optimal warehouse size, the optimal amount of space to lease in each period, and the optimal replenishment quantity for a single product case with deterministic demand. The multi-product case is modeled as a nonlinear optimization problem assuming that the timing of replenishments is not managed. Cormier and Gunn (1999) developed a nonlinear programming formulation for the optimal warehouse expansion over consecutive time periods. Goh et al. (2001) find the optimal storage size for both single-product and multi-product cases with deterministic demand. They consider a more realistic piecewise linear model for the warehouse construction cost instead of the traditional linear cost model. Furthermore, they consider the possibility of joint inventory replenishment for the multi-product case, and propose a heuristic to find the warehouse size. The effects of inventory control policies (e.g., the reorder point and ordering quantity) on the total required storage capacity are shown by Rosenblatt and Roll (1988) using simulation. Our ability to answer warehouse sizing questions would be significantly enhanced by two types of research. First, assessing capacity requirements should consider seasonality, storage policy, and order characteristics, because these three factors interact to impact the achievable storage efficiency, i.e. that fraction of warehouse capacity that can actually be used effectively. Second, sizing models all employ cost models, and validation studies of these models would be a significant contribution.

— Research on Warehouse Design and Performance Evaluation: A Comprehensive Review (Gu et al. 2010, 540–41).

Dimensioning

Translates capacity into floor space in order to assess construction and operating costs.
Example (block stacking):
- 1 pallet = \(1.2 \text{ m} \times 1.0 \text{ m}\)
- 10,000 pallets = 12,000 m² + aisles + staging
- If stack height = 3, then 4,000 m² + aisles + staging

Assumptions:
- Single storage department
- Multiple storage departments (e.g., forward-reserve configuration)

A key issue with all research on the dimensioning problem is that it requires performance models of material handling; these models are often independent of the size or layout of the warehouse. Gu et al. (2010)

The warehouse dimensioning problem translates capacity into floor space in order to assess construction and operating costs, and was first modeled by Francis (1967), who used a continuous approximation of the storage area without considering aisle structure. Bassan et al. (1980) extends Francis (1967) by considering aisle configurations. Rosenblatt and Roll (1984) integrate the optimization model in Bassan et al. (1980) with a simulation model which evaluates the storage shortage cost, a function of storage capacity and number of zones.

They assume single-command tours in order to evaluate the effect of warehouse dimension on the operational cost, and therefore their approach is not applicable to warehouses that perform multi-command operations (e.g., interleaving put-away and retrieval, or retrieving multiple items per trip). The work discussed so far has approached the sizing and dimensioning problem assuming the warehouse has a single storage department. In reality, a warehouse might have multiple departments, e.g., a forward-reserve configuration, or different storage departments for different classes of Stock Keeping Units (SKUs). These different departments must be arranged in a single warehouse and compete with each other for space. Therefore, there are tradeoffs in determining the total warehouse size, allocating the warehouse space among departments, and determining the dimension of the warehouse and its departments. Research studying these tradeoffs in the warehouse area is scarce. Pliskin and Dori (1982) propose a method to compare alternative space allocations among different warehouse departments based on multi-attribute value functions, which explicitly capture the tradeoffs among different criteria. Azadivar (1989) proposes an approach to optimally allocate space between two departments: one is efficient in terms of storage but inefficient in terms of operation, while the other is the opposite. The objective is to achieve the best system performance by appropriately allocating space between these two departments to balance the storage capacity and operational efficiency tradeoffs. Heragu et al. (2005) consider a warehouse with five functional areas, i.e., receiving, shipping, cross-docking, reserve, and forward. They propose an optimization model and a heuristic algorithm to determine the assignment of SKUs to the different storage areas as well as the size of each functional area to minimize the total material handling and storage costs.

A key issue with all research on the dimensioning problem is that it requires performance models of material handling; these models are often independent of the size or layout of the warehouse. Research is needed to either validate these models, or develop design methods that explicitly consider the impact of sizing and dimensioning on material handling.

— Research on Warehouse Design and Performance Evaluation: A Comprehensive Review (Gu et al. 2010, 541).

(Storage) Department layout

Storage department layout problems:
1. Pallet block-stacking pattern
  - storage lane depth
  - stack height
  - pallet placement angle with regards to the aisle
  - storage clearance between pallets
  - Number of lanes for each depth
2. Storage department layout
  - door location
  - aisle orientation
  - number, length, and width of aisles
3. AS/RS configuration
  - dimension of storage racks
  - Number of cranes

KPIs affected by layout problems:
- construction and maintenance cost;
- material handling cost;
- storage capacity (e.g., the ability to accommodate incoming shipments);
- space utilization; and
- equipment utilization.

In this section we discuss layout problems within a warehouse department, primarily a storage department. The storage problems are classified as:

(P1) pallet block-stacking pattern, i.e., storage lane depth, number of lanes for each depth, stack height, pallet placement angle with regards to the aisle, storage clearance between pallets, and length and width of aisles; (P2) storage department layout, i.e., door location, aisle orientation, length and width of aisles, and number of aisles; and (P3) AS/RS configuration, i.e., dimension of storage racks, number of cranes. These layout problems affect warehouse performances with respect to: (O1) construction and maintenance cost; (O2) material handling cost; (O3) storage capacity, e.g., the ability to accommodate incoming shipments; (O4) space utilization; and (O5) equipment utilization. Each problem is treated in the literature by different authors considering a subset of the performance measures, as summarized in Table 1.

— Research on Warehouse Design and Performance Evaluation: A Comprehensive Review (Gu et al. 2010, 541).

Equipment selection

Addresses two strategic decisions:
1. Level of automation (commonly determined by designers and managers based on experience)
2. Storage and material handling systems

For example, what is best considering operational, floor space, and equipment costs:
- Block stacking, single-/double-deep pallet rack, deep lane storage, or unit load AS/RS?
- Shelving systems, modular drawers, gravity flow racks, carousel systems, or mini-load storage/retrieval systems?

Fundamental issues:
1. How to identify the equipment alternatives that are reasonable for a given storage/retrieval requirement?
2. How to select among the reasonable alternatives?

The equipment selection problem addresses the level of automation in a warehouse and what type of storage and material handling systems should be employed. These decisions obviously are strategic in nature in that they affect almost all the other decisions as well as the overall warehouse investment and performance. Determining the best level of automation is far from obvious in most cases, and in practice it is usually determined based on the personal experience of designers and managers. Academic research in this category is extremely rare. Cox (1986) provides a methodology to evaluate different levels of automation based on a cost-productivity analysis technique called the hierarchy of productivity ratios. White et al. (1981) develop analytical models to compare block stacking, single-deep and double-deep pallet rack, deep-lane storage, and unit-load AS/RS in order to determine the minimum-space design. Matson and White (1981) extend White et al. (1981) to develop a total-cost model incorporating both space and material-handling costs, and demonstrate the effect of handling requirements on the optimum storage design. Sharp et al. (1994) compare several competing small-part storage equipment types assuming different product sizes and dimensions. They considered shelving systems, modular drawers, gravity-flow racks, carousel systems, and mini-load storage/retrieval systems. The costs they considered include operational costs, floor-space costs, and equipment costs. In summary, research on equipment selection is quite limited and preliminary, although it is very important in the sense that it will affect the whole warehouse design and the overall lifetime costs. There are two fundamental issues for equipment selection: (1) how to identify the equipment alternatives that are reasonable for a given storage/retrieval requirement; and (2) how to select among the reasonable alternatives. A very significant contribution would be to develop a method for characterizing requirements and characterizing equipment in such a way that these two issues could be addressed in a unified manner.

— Research on Warehouse Design and Performance Evaluation: A Comprehensive Review (Gu et al. 2010, 543).

Operation strategy

Once selected, operation strategies have important effects on the overall system and are not likely to be changed frequently.

Two major operation strategies are discussed:

Storage strategy (e.g., randomized, dedicated)
Order picking strategy (e.g., zone picking)

Storage strategy

May have significant impact on capacity or travel time. Basic strategies include:

Random storage
Dedicated storage
Class-based storage
Duration-of-Stay (DOS) based storage.

The basic storage strategies include random storage, dedicated storage, class-based storage, and Duration-of-Stay (DOS) based storage, as explained in Gu et al. (2007). Hausman et al. (1976), Graves et al. (1977), and Schwarz et al. (1978) compare random storage, dedicated storage, and class-based storage in single-command and dual-command AS/RS using both analytical models and simulations. They show that significant reductions in travel time are obtainable from dedicated storage compared with random storage, and also that class-based storage with relatively few classes yields travel-time reductions that are close to those obtained by dedicated storage. Goetschalckx and Ratliff (1990) and Thonemann and Brandeau (1998) show theoretically that DOS-based storage policies are the most promising in terms of minimizing traveling costs. Historically, DOS-based policies were difficult to implement since they require the tracking and management of each stored unit in the warehouse, but modern WMSs have this capability. Also the performance of DOS-based policies depends greatly on factors such as the skewness of demands, balance of input and output flows, inventory-control policies, and the specifics of implementation. In a study by Kulturel et al. (1999), class-based storage and DOS-based storage are compared using simulations, and the former is found to consistently outperform the latter. This conclusion may have been reached because the assumptions of the DOS model rarely hold true in practice. All the results on operational strategies are for unit-load AS/RS. Studies on other storage systems are rarely reported. Malmborg and Al-Tassan (1998) develop analytic models to evaluate the performance of dedicated storage and randomized storage in less-than-unit-load warehouses, but no general conclusions comparable to the unit-load case are given. A strong case can be made that additional research is needed, especially to clarify the conditions under which the storage policy does or does not have a significant impact on capacity or travel time.

— Research on Warehouse Design and Performance Evaluation: A Comprehensive Review (Gu et al. 2010, 543).

Order-picking strategy

Order-picking organization and planning must answer the following questions:

Part-to-picker or picker-to-part?
Wave-picking? How many waves of what duration?
Will the warehouse be divided into zones? If so, will zones be picked sequentially or concurrently?
Single or batch order picking? If batched, sorting while picking or after picking?

Depending on the operating principles selected, the order picking methods will be:

Single order picking
Batching with sort-while-pick
Batching with sort-after-pick
Sequential zoning with single order picking

Sequential zoning with batching
Concurrent zoning without batching
Concurrent zoning with batching

In a given day or shift, a warehouse may have many orders to pick. These orders may be similar in a number of respects; for example, some orders are shipped using the same carrier, or transportation mode, or have the same pick due date and time. If there are similarities among subsets of orders that require them to be shipped together, then they also should be picked roughly during the same time period to avoid intermediate storage and staging. Thus, it is common practice to use wave picking, i.e., to release a fraction of the day’s (shift’s) orders, and to expect their picking to be completed within a corresponding fraction of the day (shift).

In addition to wave picking, two other commonly used order-picking strategies are batch picking and zone picking. Batch picking involves the assignment of a group of orders to a picker to be picked simultaneously in one trip. In zone picking, the storage space is divided into picking zones and each zone has one or more assigned pickers who only pick in their assigned zone. Zone picking can be divided into sequential and parallel zone picking. Sequential zone picking is similar to a flow line, in which containers that can hold one or more orders are passed sequentially through the zones; the pickers in each zone pick the products within their zone, put them into the container, and then pass the container to the next zone. (Bartholdi et al. (2000) propose a Bucket Brigades order-picking method that is similar to sequential zone picking, but does not require pickers to be restricted to zones). In parallel zone picking, an order is picked in each zone simultaneously. The picked items are sent to a downstream sorting system to be combined into orders.

The organization and planning of the order-picking process has to answer the following questions:

Will product be transported to the picker (part-to-picker) or will the picker travel to the storage location (picker-to-part)?
Will orders be picked in waves? If so, how many waves of what duration?
Will the warehouse be divided into zones? If so, will zones be picked sequentially or concurrently?
Will orders be picked in batches or separately? If they are batched, will they be sorted while picking or after picking?

Depending on the operating principles selected, the order-picking methods will be:

Single-order picking.

Batching with sort-while-pick.

Batching with sort-after-pick.

Sequential zoning with single-order picking.

Sequential zoning with batching.

Concurrent zoning without batching.

Concurrent zoning with batching.

Research on the selection of an order-picking strategy is very scarce, which might be a result of the complexity of the problem itself. Lin and Lu (1999) compare single-order picking and batch-zone picking for different types of orders, which are classified based on the order quantity and the number of ordered items. Petersen (2000) simulates five different order-picking policies: single-order picking, batch picking, sequential zone picking, concurrent zone picking, and wave picking. Two control variables in the simulation study are the numbers of daily orders and the demand skewness, while the other factors such as warehouse layout, storage assignment, and zone configuration (when zone and wave picking are used) are fixed.

The performance measures used to compare the different policies include: the mean daily labor, the mean length of day, and the mean percentage of late orders. For each order-picking policy, the simplest rules regarding batching, routing, and wave length are used. It also should be noted that the performance measures are mainly related to order-picking efficiencies and service quality; additional costs caused by downstream sorting with batch, zone, and wave picking are not considered. Furthermore, comparison of these policies are made mainly with regards to the order structures, while other important factors such as storage assignment and detailed implementations of the order-picking policies are assumed to be fixed. Therefore, the results should not be considered generic and more research in this direction is required to provide more guidance for warehouse designers. Order-picking strategy selection remains a largely unresolved design problem. Additional research would be valuable, especially if it could begin to characterize order-picking alternatives in ways that were easy to apply in design decision making. As an example, could researchers develop performance curves for different order-picking strategies?

— Research on Warehouse Design and Performance Evaluation: A Comprehensive Review (Gu et al. 2010, 544).

Order-picking: Common research pitfalls

For each order picking policy:

The simplest rules regarding batching, routing, and wave duration are used.
- Performance measures are mainly related to order picking efficiencies and service quality; additional costs caused by downstream sorting with batch, zone, and wave picking are not considered.
Policy comparison considers only order structures, while other important factors such as storage assignment and detailed implementation are assumed to be fixed.

The performance measures used to compare the different policies include: the mean daily labor, the mean length of day, and the mean percentage of late orders. For each order-picking policy, the simplest rules regarding batching, routing, and wave length are used. It also should be noted that the performance measures are mainly related to order-picking efficiencies and service quality; additional costs caused by downstream sorting with batch, zone, and wave picking are not considered. Furthermore, comparison of these policies are made mainly with regards to the order structures, while other important factors such as storage assignment and detailed implementations of the order-picking policies are assumed to be fixed. Therefore, the results should not be considered generic and more research in this direction is required to provide more guidance for warehouse designers. Order-picking strategy selection remains a largely unresolved design problem. Additional research would be valuable, especially if it could begin to characterize order-picking alternatives in ways that were easy to apply in design decision making. As an example, could researchers develop performance curves for different order-picking strategies?

— Research on Warehouse Design and Performance Evaluation: A Comprehensive Review (Gu et al. 2010, 544).

References

Frazelle, Edward. 2016. World-Class Warehousing and Material Handling. Second Edition. McGraw-Hill Education.

Gu, Jinxiang, Marc Goetschalckx, and Leon F. McGinnis. 2010. “Research on Warehouse Design and Performance Evaluation: A Comprehensive Review.” European Journal of Operational Research 203 (3): 539–49. https://doi.org/10.1016/j.ejor.2009.07.031.