Posts filed under 'Logistics Strategy'
The focus in the backroom is to do more with less, and retailers are looking to logical layouts and space-saving strategies and equipment to help accomplish this.
According to industry figures the amount of space devoted to backrooms has steadily declined over the past several decades. In the 1970s backroom storage accounted for about 30% of a store’s square footage, but that amount has dwindled to 15% or less today.
One of the key drivers to this downsizing is just-in-time inventory management. The idea is to minimize the amount of inventory that is held as back-up stock at all points in the supply chain and to increase the frequency of store deliveries. More accurate POS data, allows operators to identify more precisely how many of each product sell each day, and then to order just enough to hold a store over a one- to three-day cycle.
Store friendly deliveries
Another goal of current supply chain thinking is to transfer as much inventory from the delivery truck directly to the sales floor rather than keep stock in the backroom. This approach is aimed at reducing handling costs.
Fifty to 60% of supply chain costs are in the last 100 meters. Traditionally, the backroom was where most of this cost occurs as received orders are broken down, sorted by aisle and then loaded on carts to be taken to the sales floor.
Today, many retailers are pushing backroom activities back into the distribution center. As store orders are picked, pallets are built so that merchandise is arranged by store aisle and merchandise adjacency - known as “store-friendly deliveries.” This strategy involves merchandise pallets that can go directly from the truck to the store’s sales floor, where they are broken down and put on the shelves. The goal is to minimize handling.
Keeping track of the hundreds of variations in store layouts that can exist even within a single chain can be mind-boggling. However, it is possible to prepare store orders at the warehouse so that most pallets are sorted by store aisle.
The trade-off, of course, is extra warehouse labor costs, but retailers still come out ahead. In fact, a “store-friendly delivery” strategy typically increases warehouse labor by 25%, but can also decrease store labor by 33%.
In terms of time this trade-off amounts to a savings of 16 seconds per case handled.
February 9th, 2010
As the supply chain moves to a leaner, demand driven model, the trend is towards less inventory in the system, in general. This is a change from the traditional model which was essentially an inventory driven supply chain. With fewer inventories in the system, we see that product assortment and the need to handle individual products in the most efficient manner possible is driving DC design. That is, the slotting requirement is now as important as inventory storage and in many cases drives the DC sizing requirement.When designing a warehouse facility for efficiency, several factors need to be considered. The goal should always be to minimize costs, both capital costs used to construct a new facility or expand an existing location, as well as on-going operating costs associated with handling product and maintaining the physical structure.
Thus “Efficient DC Design” implies the design of a warehouse to minimize annual operating costs while maintaining desired service levels. Service levels are often affected by efficiency within a warehouse operation, and thereby impacted by the design of the layout.
Assuming a conventional, case pick operation there are 3 Main factors driving efficient design: Pick Slots, Net Working Capacity (Cubic Storage), and Dock Operations. Each is examined below in more detail.
Pick slots / Rack Bay requirements: How many rack bays are needed to satisfy the types of slots required to efficiently select product for shipping?
Determining the pick slot requirements is an analytical process involving detailed data mining and evaluation. The correct application of slotting logic to the data results in an efficient DC design. The basic data needs include volume or sales history, physical product characteristics (including packaging types and case & pallet dimensions), and inventory requirements.
Assigning an efficient slot type to each unique item in the distribution center should be based on weekly shipping volumes and desired replenishment activity. The trade off in productivity is pick line length versus replenishment or restocking activity. In many distribution centers, picking productivity accounts for up to 60% of all direct labour and thus commands the greatest attention.
A pick slot can vary in size from a single carton location to a multiple pallet location, all accessible from floor level.
Once the number and type of pick slots is determined, this number is translated into the equivalent number of rack bays required. The height of the rack bays will depend greatly on the inventory levels to be held in the distribution center.
Cubic Inventory Storage: How many rack bays are needed to satisfy the cubic (ft3) inventory storage requirements, on average and at a peak? What height of building is required to efficiently store the required inventory?
The necessary storage volume is often expressed in terms of cube (ft3). The ability of a distribution center to efficiently store cube is defined as Net Working Capacity (NWC). Once pick slot requirements have been determined and converted into rack bays, the cubic inventory on hand will determine the required height of the bays, and thus the entire building size. The NWC is then calculated at varying building heights to ensure that inventory will fit overhead of the pick slots. In some designs, where inventory levels are very high, special dense storage sections may be added to the DC layout in order to minimize stacking height requirements.
It is always vital to hold inventory for a given item as close as possible to its designated pick location(s). This minimizes the amount of putaway and replenishment labour required to stock the pick slot.
The travel aisle spacing between rack bays is dictated by the mobile equipment meant to operate within a given aisle. Generally, fork lift equipment outrigger dimensions will vary with the required lift height at which product is placed in overhead reserve locations. The allowance for operators to pass easily in an aisle will determine the final aisle width. Passing is a requirement for efficiency as it prevents an operator being impeded by another from performing their function. A typical, conventional facility with a clear height range from 28′ to 35′ will have a minimum 10′6″ aisle width for single-deep pallet racking.
Dock & Dock Door Requirements: What size dock should I have? What is my optimal receiving dock depth and width? Of my shipping dock? Should the facility have separate receiving and shipping docks? How many dock doors?
Not to be underestimated is the amount of dock space required for efficient receiving, flow and shipping of product. The dock is the heart of any operation and ultimately creates needed efficiencies or, if inadequate, hazardous bottlenecks.
Again, the trade off is in building size vs. operating efficiency. The dock and dock door requirements are driven primarily by shipping or service levels, the hours of operation, and the number of days per week of operation. The more balanced the workload, the more efficient the design will be. Dock sizes can range from 50′ to 120′ in depth, depending on the amount of crossdock or product flow-through on a given operating shift, or for any required equipment such as pallet wrapping machines.
Other factors to consider in Efficient DC Design:
Location of auxillary functions such as location of building columns, battery charging, returns handling, clerical offices, etc. These items do not drive the design, but should be considered such that they integrate well and don’t interfere with the main functions of the warehouse.
Last but not least, one must consider flexibility in DC design. Given the changing landscape of supply chain management, a flexible operation is a must. Therefore, thinking ahead to consider expansion planning and ‘what if’ scenarios will enhance your DC plan. Flexibility in the equipment chosen, sizing of dock and storage areas, will allow easier transition to new operating realities as required.
The factors outlined above address a conventional warehouse operation where orders are selected onto pallet jacks and putaway and replenishment functions are performed by fork lift trucks. The principles however, are similar in non-conventional solutions. Obtaining and evaluating the right data will allow one to follow the basic steps above, and gain an understanding of the footprint required for an efficient DC design.
September 29th, 2009
In a need to shed operating costs, particularly in an environment of high volatility, companies are turning attention to their distribution networks. In the knowledge that distribution networks can offer competitive advantage in a marketplace demanding ever higher levels of customer service they’re asking how to optimize current infrastructures.
No pre-established solutions exist for what constitutes an optimal distribution network. Each operator has a unique context influential on the outcome of any investigation into exactly what their optimal network should look like.
However, the methodology employed to arrive at the answer is universal: collect the necessary information; model current network performance under a series of growth projections; model potential alternatives under the same series of projections; compare the alternatives to the current infrastructure in terms of capital and operating costs, customer service, financial sensitivity, risk, and ease of implementation. Once complete, this method provides an effective platform for building the optimal distribution network.
The first step, collecting the necessary information, is the most important. Everyone wants to avoid the “garbage in, garbage out” maxim that leads to bad decision making. Instead, all information for use in this exercise must be properly reviewed, cleansed and validated making it mission- critical to thoroughly review information needs prior to undertaking a distribution network study.
Each point in the distribution network must be mapped and characterized in terms of its logistics function. This includes the supplier base from which the operator draws its product, the company’s distribution centers and stocking points, as well as, the customers and/or company’s stores.
- 1) The suppliers each need to be described in terms of volumes shipped into the network expressed in:
- o cost of goods, pallets, pounds and cubic feet; the frequency and mode of shipment (truck load, LTL, rail cars, etc.); the lead times from supplier to distribution center site; the information exchange between your company and your suppliers.
- 2) Each facility within the distribution network should be described:
- o product in terms of current capacity; geographic position, pallets, pounds and cubic feet; SKUs variety; on-site storage capacity; the frequency and mode of shipment; the destination of shipments (i.e., direct to customer, into distribution centers or stocking points)
- o distribution centers and stocking points in terms of storage and throughput capacity (current and site maximum); number of SKUs; is it owned or leased (if leased, term of lease); geographic position; 3rd party or self-operated; service region and customer base; the frequency and mode of outbound shipments (to customer locations and inter-facility transfers).
- 3) The customers served should be described in terms of shipping volumes, geographic location, service level, particular requirements.
Compiling the above allows an operator to create a static distribution network map. The next level of analysis converts the static map into a dynamic network model making use of transactions between suppliers, distribution centers and customers. To do this:
- A sample period is chosen and all transactions occurring within that period are brought into a database to create a network model. Often, data management constraints lead companies to use an abbreviated sample period, for example 12 weeks.
- 12 months is the preferred sample period as it allows for a rigorous analysis that models seasonal distribution network peaks and valleys and eliminates errors arising from “annualizing” a model. That is, converting a short sample period and the related cost model based on annualized figures rather than actual P&L performance and operating budgets.
- All transactions for the sample period are incorporated at line level detail including:
- Each purchase order line shipped into the network;
- Each inter-facility transfer at the SKU-line level;
- Each order line shipped to a customer.
- Using the physical properties of the items and freight history, the transaction can be expressed in terms of pallets, cubic feet, pounds and shipments.
To conclude, the final aim is to project the dynamic distribution network model to a future state or design year. Too often, this step simply models using a volume growth rate assumption or set of assumptions and measures the effect on the network. However, while absolute volume growth is an important component of projecting future network requirements, other critical elements to consider include:
- New item variety - new products originate for a variety of reasons, from packaging changes to entirely new product lines. Changes in SKU variety have an important impact on network capacity.
- Alternate suppliers or supply channels - optimal network design should incorporate future planned changes to supply. This may impact geographic origin of the supply such that new infrastructure or increased capacities are required. For example, sourcing product from overseas affects not only the frequency and mode of inbound shipments, but related extended lead times and variances alter inventory positions a company takes on those products.
- New customers - the absolute volume growth must be characterized in terms of growth from both existing accounts and new accounts or customers. This latter growth may be regionally specific or dispersed along the lines of the current customer base and therefore plays a determinant role in the optimal network solution.
- New customer demands - as customers evolve and seek cost reductions and service level improvements from their own networks, demands change. It’s important to ascertain forthcoming changes in customers’ demands. For example, a supplier may deliver product directly to customer retail locations while down-the-road that customer requires product shipments to distribution centers instead, leaving the store deliveries to the customer’s own network.
Having amassed, cleansed and validated the appropriate information, the operator is ready to model the current distribution network and explore alternatives that yield a better cost-service outcome. In this way, when it comes time to recommend changes toward an optimal distribution network as a result of the study, management can be confident of a sure footing.
September 8th, 2009
Whether striving to finance business expansions or day-to-day operations, these have been difficult times. For this reason, many companies are increasingly acknowledging the strategic need to unlock large amounts of cash tied up in inventories, sales and purchases.
The working capital of a company, that is, the difference between its current assets and short-term liabilities is an indicator of two things: first, the financial health of the company and its ability to meet its immediate obligations; and second, the company’s operational efficiency.
The importance of optimizing and managing working capital resides in the fact that companies need cash to finance growth and ongoing operations alike - as it’s just not possible to pay suppliers and employees with inventory.
A common shortcoming many companies encounter when implementing cash-driven initiatives is: failing to understand the relationship between supply chain activities and financial performance. For example, the way orders are selected in the warehouse can affect Days-Sales-Outstanding (DSO) in addition vendor delivery performance can be leveraged when negotiating payment terms.
The following are a few examples of factors sometimes considered “soft” issues that can, in fact, provide a quantifiable return when improved:
Perfect Order Rate
The collection of invoices with erroneous deliveries (i.e. undetected mispicks, scratches) will cause not only deductions and charge-backs but also delays due to customers’ payment cycles.
Safety Stock Levels
There are several factors affecting inventory, however, when launching an inventory optimization initiative, the first step is to determine what the level of safety stock should be, and although inventory levels will fluctuate due to legitimate business reasons, there should be a set target. For example, the total inventory of a product shouldn’t be greater than its safety stock if the product is received and shipped every day in full truck loads.
Fast moving material handling systems
Introducing cross-docking, reverse line picking and flow-through programs for those items and vendors that meet the volume, quality, service level and cost requirements will impact inventory reduction capability.
Order fulfillment cycle time
Once the company has a firm order from a customer, the time it takes to fulfill that order is incorporated into the cash conversion cycle. Given this, the ability to respond to demand faster and while maintaining or lowering cost should drive transportation and warehouse initiatives.
Vendor Collaboration
Working with Vendors can make a big difference. For example, moving inventory off the balance sheet through the pursuit of Vendor Managed Inventory initiatives can reduce a company’s investment in inventory.
There are many other factors rooted in supply chain activities that impact working capital performance and should be incorporated into the supply chain performance metrics. These include:
- Vendor lead times
- Supply chain visibility
- Real-time proof of deliveries
- Forecasting accuracy
- Vendor performance linked to payment terms
- Minimum order quantity
The extent to which each of these factors influence the financial results vary from company-to-company, however, in order to implement an effective working capital optimization initiative the following steps should not be overlooked:
Analyse end-to-end supply chain processes - to identify opportunities based on depth of research and data analysis from vendors to customers.
Define performance metrics - to measure not only results but also leading indicators (e.g. input variables that impact results).
Set goals - at tactical and operational levels to ensure execution effectiveness.
Align cross-functional accountability - create cross-functional teams to drive improvement initiatives across the supply chain.
By increasing overall awareness and the attention given to the relationship between the supply chain and financial performance, companies can strive to link their activities in this area to their Working Capital Optimization initiatives. The above most specifically suggests how to do so in regard to warehouse operations and material handling systems, and stresses the importance of the related Supply Chain metrics used to support this effort.
August 11th, 2009
One of the first things anyone in the distribution business learns is the 80/20 rule, or more formally, Pareto’s Law:
· 80% of shipping volumes are generated by the top 20% of SKUs
· 20% of shipping volumes are generated by the bottom 80% of SKUs
Traditionally, this is what we call the “fast-slow” split and every distribution center grapples with how to manage these very two different sets of items. In fact, at KOM, we like to remind people that there is a third set of items, the “hyper-fast”, that deserve special attention since these top 5% of SKUs can generate anywhere from 30 – 50% of total shipping volumes.
So who was Pareto and where did his law come from?
Vilfredo Pareto was an economist and avid gardener in Italy during the late 1800s. He made two observations in his professional work and hobby that led him to articulate what would later become “Pareto’s Law”.
· As an economist studying land ownership in Italy, he observed that 80% of the land was owned by 20% of the land owners.
· As a gardener, he observed that 80% of his peas came from 20% of the peapods he grew.
This curious coincidence led him to look for that distribution pattern elsewhere and he noticed a great many things had this logarithmic distribution. Today, Pareto’s Law describes patterns across a whole range of disciplines, not just the supply chain.
· Traffic engineers tell us that 80% of the traffic is generated by 20% of the vehicles.
· Marketers tell us that 80% of a marketing campaign’s results come from 20% of the advertising spend.
· Cooks tell us that a chicken is 80% done in the first 20% of its cooking time.
So here’s a water cooler game for distribution folks – where else in life have you seen Pareto’s law in action?
March 11th, 2008