Data-driven inventory planning and warehouse layout design
The variable nature of supply and demand requires ongoing analysis of warehouse capacity and warehouse layout design. Poor inventory planning can introduce many unnecessary wastes and costs to the warehouse. Traditional tools used to assess warehouse logistics (e.g., spreadsheets or materials requirement planning (MRP) software) estimate the sizing of warehouses using set assumptions like delivery time, inventory needs, and safety stock levels. But, at best, these estimates are a static snapshot of the ideal warehouse situation and don’t take into account uncertainty, opportunities for manufacturing growth, or ways to optimize warehouse logistics. Additionally, finding the balance between efficiency and cost has become more complex with globalization.
All of this combined complexity requires advanced tools to empower warehouse and logistics managers to make data-driven inventory planning decisions. Modeling tools, such as discrete event simulation (DES), assist in developing operational strategies for warehouse layout designs that optimize the storage and location of materials while minimizing traffic and picking time; enhancing safety; and reducing the cost of storage and handling. DES is a flexible, comprehensive tool that provides the potential to improve efficiency and space utilization of warehouses in new construction as well as existing facilities. This article focuses on how to use discrete event simulations to optimize warehouse logistics for planning and management purposes, including:
- What is a DES?
- How does a warehouse DES work?
- Five ways to optimize a warehouse using DES
- A case study featuring warehouse “what-if” scenarios
- When to use DES for warehouse optimization and planning
What is a discrete event simulation?
As a warehouse optimization tool, DES software models the inventory, storage, and movement of materials and personnel within a given warehouse. Simulations allow manufacturers—from food and beverage to pharmaceuticals—to optimize their warehouse floor plans by examining storage needs and quantifying the impact of various replenishment strategies.
The power of DES lies in being able to run scenarios—simulations that take variability and uncertainty into account—to assess the impacts of supply chain disruptions, changes to product demand, or the move from labor-intensive processes to automated solutions. The resulting insights can help you develop mitigation strategies or influence capital and operational expenditures.
DES has been proven to help companies reduce on-hand inventory, lower operating costs, maximize warehouse space, and provide justification for shifting from labor-intensive workflows to automating certain warehousing tasks.
How does a warehouse discrete event simulation work?
Discrete event simulation inputs
The inputs used for warehouse optimization simulations will be the same across various facility types and industries even though the actual materials used in a manufacturing process may vary. Warehouse modeling inputs include:
- Item master: Includes storage temperature conditions, segregation requirements, and physical dimensions
- Bill of materials: Includes the quantity of material consumed and product produced
- Lead times: The time between ordering and receiving material into the warehouse
- Safety stocks or inventory on hand: The amount of inventory required on hand to avoid disruptions to the manufacturing process
- Quality control: Metrics related to regulatory and safety considerations
- Rate of depletion: The speed at which material is moved out of the warehouse and consumed, determines the pace of new orders, factoring in lead time and safety stock
We can assign a probability to each of these DES inputs and vary them in simulations to assess different scenarios. This provides an enhanced level of confidence that a warehouse will be appropriately sized in terms of square footage, number and types of items stored, and traffic flow—even taking into account potential supply disruptions or increased production demands.
Discrete event simulation outputs
The outputs of a warehouse optimization simulation are defined at the outset:
- Pallet spaces: The number of pallet spaces and the way they are placed help determine the ideal layout design.
- Inventory profiles: The number of pallets of each item needed informs warehouse layout, the arrangement of storage, and type of racking. For example, do you have ten, 50-lb. items that require one pallet or 1,000 items needing a half pallet each?
- Dock utilization: This is determined by the number of docks needed.
- Automated storage and retrieval system (ASRS) and automated guided vehicles (AGVs): This is a count of vehicles needed to handle throughput. If AGVs aren’t used and workers are doing the picks, this will usually be in combination with a forklift system.
- Staff required: This is a calculation of the headcount necessary to do picking, management, and shipping.
- Storage space per environmental condition: The simulations differentiate between types of storage, including controlled room temperature, hazardous materials, coolers (2–8o C), and freezers. These need to be tracked separately rather than lumped together to give an appraisal of total storage space needed. Differentiating by storage type allows a more accurate estimate of the expense.
These enhanced insights enable warehouse and logistics teams to build a stronger, more resilient data-driven strategies for their facilities and operations.
Five ways to optimize a warehouse with discrete event simulations
1. Identify the ideal warehouse layout design
The outputs from simulations can right-size the warehouse for layout and design planning and provide operational understanding. The models incorporate all the necessary equipment, quantify the storage and staging areas, and calculate the number of docks required based on throughput.
2. Run warehouse ‘what-if’ scenarios
Simulation software is customizable and designed to facilitate the analysis of operations under varying warehouse conditions. This makes it simple to update a model with revised space metrics, identify inventory risks, and develop resource plans based on observed trends and supplier behaviors. Start with a few assumptions about lead times and inventory needs, and then assign probabilities to characterize uncertainty for each of these parameters to see what results from that uncertainty. A more accurate picture of reality arises when you consider multiple scenarios.
Once the base case model is built and validated, the tool can be revised using fresh inputs. Updating those parameters is straightforward and can be efficiently compared to the initial configuration of the model. Consider training some staff to use DES software so they can update an existing model in the future and continue to experiment with different warehouse optimization scenarios.
3. Prevent over-ordering in your warehouse
Recently, we’ve seen the fragility of supply chains in multiple industries, including in pharmaceutical manufacturing and food production. The global nature of supply chains means that political instability, climate catastrophes, or an epidemic in a distant part of the world can have an impact on imports domestically. Such variability in lead times can leave warehouse managers guessing how to implement effective inventory controls, and it has driven a conservative planning strategy of increasing safety stocks to minimize the risk of shortages.
Excess inventory, however, might require additional storage space that otherwise could be repurposed in a more valuable way. And, if inventory levels increase beyond the warehouse capacity, you many need to expand a current site or secure offsite storage. The cost of additional space, the increased risk of material expiration, and the need for additional headcount to manage warehousing operations can all negatively impact profit margins.
We can leverage DES models to identify the probability of reaching peak inventory levels to mitigate the risks of over-ordering. Confidence levels are developed to provide insights into maximum modeled pallet occupancy, staging spaces, refrigerated/frozen storage, etc. For example, DES models can help a logistics manager predict a 98% chance that total pallet occupancy will not exceed ### pallets. These probabilistic forecasts, along with tracking of expired inventory, enable warehouse managers to develop inventory strategies that fit their company’s risk appetite.
Simulations provide data-driven inventory and risk profiles. These empower warehouse managers to maintain adequate safety stocks without across-the-board over-ordering, even in uncertain environments. It allows them to strike a balance between a conservative approach with the lean operations of a just-in-time warehouse.
4. Provide flexibility
Supply chain needs can change suddenly. The flexibility of DES modeling helps you visualize the impact of this uncertainty. Run multiple scenarios in which inputs, such as lead times or production throughput, are tweaked to model the potential impact of anticipated changes. Warehouse models can assess inputs for hundreds of different items and look at the variability within and between all of them. This is a distinct advantage over a spreadsheet, which does not allow complex relationships to be easily assessed. This flexibility allows warehouse managers to develop responsive stocking strategies.
5. Optimize warehouse traffic and inventory flow
DES helps you understand the quantity and flow of materials, equipment, and personnel in a warehouse. It can model traffic, especially at intersections, to minimize safety risks in a warehouse layout design. As well, warehouse simulations can optimize where to store material to minimize travel and pick time.
Modeling can be used to justify the automation of some or all warehouse activities. For one client, we considered pairing ASRS with AGVs to optimize the proposed layout design before the facility was constructed. After the facility was in operation, the client used the same model to improve the system by optimizing the number of AGVs and uncovering throughput bottlenecks within the system.
Case study: $1.2 million saved with DES “what-if” scenarios
A biotechnology company was conducting a feasibility study of an existing facility and hired to determine the effect that increased demand, new technologies, and insourcing buffer manufacturing would have on the size of its warehouse.
Our team created a discrete event simulation (DES) model using current manufacturing and supply chain inputs. Once validated, we revised the model to experiment with many alternative warehouse scenarios—all while adjusting the inputs to reflect anticipated changes—and quantify the impact on the warehouse size, as well as designing appropriate floor plans for pallet locations and racking solutions.
The client was able to estimate the warehouse space needed for raw materials, quarantine, intermediates, and finished goods based on projected demands, and right-size its cold storage requirements. The resulting cost avoidance was $1.2 million thanks to our recommendations for replenishment strategies and operational improvements to reduce the size of the warehouse expansion.
When to use discrete event scenarios for warehouse optimization and planning
Although DES is most commonly used at the beginning of the design phase to size the warehouse footprint for new construction, simulation models can be introduced later or revised every couple of years to evaluate warehouse layout design in anticipation of projected changes to an operation, such as increased production throughput or changes to delivery lead times. This powerful and dynamic warehouse optimization and planning tool can inform operational decisions about the need to expand, consider a new building, or use offsite warehousing.
DES modeling is part of CRB’s warehouse design and logistics consulting services, which begin with an assessment of your current and forecasted materials needs, including:
- Receiving & shipping
- Inventory levels
- Process flows
- Material handling equipment
- Racking type(s)
- Special handling needs
Our consulting team is here to help optimize your warehouse logistics for data-driven planning. Curious about your facility’s potential? Let’s talk.