Fulfillment managers remain concerned following the 2020 holiday peak season, which was riddled with shipping delays and operational gridlock. They are aggressively searching for opportunities to reduce order cycle times to support demand. Finding operational improvements involves breaking down the detailed requirements of the business (e.g., order volumes, lines/order, units/order, item dims, service levels, etc.). And, while small parcel carrier limitations were a major issue during the prior holiday season, we will focus on “inside the four walls” opportunities.
Most fulfillment issues can be categorized into data, process, equipment, labor, and technology. Managers must dive into these areas of their existing facilities to find ways to drive fulfillment to its maximum potential.
An underlying issue limiting fulfillment speed is the lack of quality data informing the design and supporting operational management. The top issue is missing and inaccurate item master data, including vendor case dimensions and weights, units per case, and cases per pallet. This information is valuable in determining if an item is conveyable and selecting the appropriate pick location size. Order history data is also required to generate profiles including each and full case pick volume activity, lines per order, units per line, and other profiles. But the application of the item master details with the order volumes/profiles is the key to designing and managing an operation. There are multiple dimensioning systems available to collect this information, and it should be a top priority. While it may seem too late to integrate this data into an existing operation, it can be used for daily planning and adjustments to the design.
The fulfillment process begins with receiving and stocking inventory into the building. This foundation enables replenishment to a forward pick area where orders are processed into totes or shipping boxes. The last 100 feet typically includes the packaging and manifesting functions and is often a limiting factor to higher throughput. However, the most common area of improvement is order picking, which often uncovers replenishment as a big issue. These two processes must be synchronized in order to have the highest possible throughput. The additional challenge is most “process” improvements require modifications to equipment and/or systems to realize the greatest benefits. For example, converting to a batch-picking process, using a min/max replenishment strategy, integrating pick & pass, using cartonization logic, applying automatic label print/apply technology, and executing velocity-based slotting require changes to equipment and/or systems.
The selection of pick equipment (e.g., shelving, case flow, pallet flow, shuttles, AMRs, etc.) and the facility layout are critical to achieving fast cycle times. Design flexibility is also important for handling unexpected demands, such as acquisitions. The pick location sizes should be based on supporting a target “days of supply” that minimizes replenishments, while not oversizing the pick area. This balancing act is a major challenge faced by managers. Not having inventory in the pick location at the time of pick is the greatest risk to lengthening order cycle times. Yet, putting too much (or all) of the inventory in a pick location increases the pick area footprint, which lengthens order cycle times (and expands capital budgets).
The sizing effort starts by applying the items’ dimensions with the expected daily volumes. With this analysis, you can evaluate an effective days-of-supply varied by the item’s velocity. Ideally, the fastest movers (A-items) are adequately stocked in the pick locations to hold about two weeks of inventory. The dilemma is how much space and what equipment is required to provide this amount of inventory. Generally, for A-items, it is not feasible to stock more than a month of inventory in the pick location or you risk oversizing the pick area. Ironically, the slower items (assuming similar item sizes), may have months of inventory in the smallest location. These slower items may also be picked directly from reserve storage to provide more space in the pick area for faster movers. Dynamic slotting may also be an option, which requires a pick location size that supports changing volumes based on multiple factors. However, dynamic slotting may increase replenishment labor versus a fixed location strategy, so there is often a hybrid approach. This location sizing analysis is a balancing act involving the cost of pick/ replenishment labor and the size/capital cost of the forward pick area.
There is a wide range of material handling equipment integrated within the pick area including carts, conveyor, lift trucks, and fully automated product-to-person technologies. To speed up order fulfillment most utilize a multi-level pick platform with conveyor to transport totes/shipping cartons through zones, put-walls, dunnage machines, auto-labeling, and pack stations. In more sophisticated designs, automation, such as AMRs (automated mobile robots) is used to automatically move products to pick/pack stations.
The use of automation is rapidly growing to speed up fulfillment, but it requires additional capital. Building a solid return on investment is required to justify the additional investment with savings mostly within the pick and replenishment labor functions. While automation is the solution for many, misaligned processes, equipment, and limited staffing can quickly lower the expected returns.
Productive workers are essential to faster order fulfillment and are becoming harder to find and keep. This has caused some to over-staff, resulting in higher costs per unit and an unproductive work environment. Others have limited staffing options and are relying more on a temporary workforce. Whatever the situation, providing quality training and a comfortable work environment is important to attracting/retaining workers and achieving higher productivity. Additionally, using the right number of supervisors (~20% ratio to employee count) can keep fulfillment flowing efficiently. Many deploy labor management software as a solution, but these systems can backfire if the proper amount of time is not allowed to build accurate standards and for change management. Workers should be held accountable for performance, but need an efficient design, proper training/supervision and supportive systems.
An effective system should enable a well-designed solution and not add constraints. The ability to batch orders (e.g., single-line orders) and wave management are valuable capabilities to speed-up fulfillment. Systemically driving replenishment to ensure pick locations have the required demand is another must-have functionality. Establishing an on-going slotting strategy is critical to managing congestion and increasing labor productivity. Many companies are dealing with dated systems and are aggressively looking for system modifications and bolt-on solutions to speed -up fulfillment.
To achieve faster order fulfillment it is important to have efficient processes and size the forward pick area to balance pick and replenishment labor. This balance requires the right mix of equipment and integration of material handling technology. For an operation to remain productive, the workforce must be properly managed and trained. Maintaining consistent fulfillment speeds requires the support of systems to empower the design and enable high productivity levels. Identifying the specific reasons for sluggish order fulfillment and unleashing the potential can be discovered by breaking down processes, equipment, layout, labor management, and software.
Norm Saenz is a recognized leader in supply chain engineering, with 29 years of experience in facility planning, design, and implementation management. As a Managing Director and Partner with St. Onge Company, he develops client accounts and manages projects involving new and existing facility designs, space and layout planning, labor productivity estimating, equipment and technology evaluations, capital cost and ROI development, design specifications, supplier selections, and implementation management. In addition, he supports St. Onge projects involving Supply Chain Logistics Optimization and Execution Software (WMS, LMS, WCS, WES, TMS,). Visit www.stonge.com for more information.
This article originally appeared in the May/June, 2021 issue of PARCEL.