Eighty percent of a company’s supply chain costs are locked in during the design and planning of the supply chain strategy. So when it comes to your network of distribution centers, no matter how well your distribution centers operate, if you have the wrong number of them or if they are in the wrong locations, or serving the wrong purpose, your supply chain will be a drain on finances. Therefore, before you invest capital in a new distribution center or if the SCOR process recommends an evaluation of your distribution center network, you should ask yourself the following questions: How many distribution centers should I have? Are there opportunities to consolidate them? What is the role of each one in my overall supply chain strategy? Are their locations consistent with my supply chain strategy? Have I properly sized my distribution centers to accommodate my supply chain strategy? Which products should I store in which distribution center? Which customers should each of them service? Are they flexible to accommodate future industry and customer demands?

A well-designed Logistics Network Model will help you answer all of these questions and define what distribution centers are required, where they should be located and how they operate to support your overall supply chain strategy.

Put simply, network analysis is the process of determining the appropriate facility infrastructure to support a given supply chain strategy. This process depends greatly on a variety of cost variables and operational constraints. Cost drivers will vary depending on the scope and nature of the analysis (i.e. single vs. multitier distribution) but generally can be categorized as one of the following:

 Inbound raw material sourcing costs.

 Fixed costs at distribution centers (and production facilities, if applicable).

 Variable costs at distribution centers (and production facilities, if applicable).

 Inventory carrying costs.

 Replenishment and transfer freight costs between facilities.

 Outbound freight costs from distribution centers to customers.

Operational constraints are business requirements to which you must adhere – regardless of the cost. You can violate these constraints only if you’re willing to pay a specified penalty.

Constraints can vary widely but generally include the following:

 Facility status (will definitely remain open or will definitely close).

 Facility eligibility (what is the capability of a given facility, and/or which SKUs can you place in a given facility?).

 Facility storage and throughput capacity.

 Customer service requirements.

 Sourcing requirements (sole source for customer shipments vs. split shipments with another facility).

 Minimum and maximum number of facilities.

Demand profile constrains the analysis (customer location and product mix) as well as the number and type of alternative scenarios under consideration.

In most cases, the need to consider myriad alternatives, to simultaneously evaluate multiple cost variables and to honor a host of constraints makes most problems too difficult to solve using traditional methods such as a calculator or spreadsheet. Determining an optimal solution, typically based on the least cost or maximum profit, requires the use of a network modeling tool. Most commercially available tools consist of three primary components: a user interface that accepts the various demand, cost and constraint data; a translator that converts this data into representative mathematical functions; and a solver, which is the analytical engine that determines the solution. Solvers utilized by more robust modeling tools employ specialized mixed integer linear programming theory (a complicated explanation too lengthy for the space allotted to this article) to calculate the true optimal solution and, therefore, are referred to as “optimizers.” Most commercial tools also provide significant statistical and graphical reporting functionality.

Many people think of network modeling as being synonymous with network analysis. In reality, modeling is only one component of the network analysis process. I make this distinction because there is often a misconception that an all-knowing model can be constructed to identify the one true optimal network. In reality, a model is simply a mathematical tool. The burden still lies with the user to develop alternative operating scenarios, provide input data for each scenario, correctly interpret model results and account for qualitative issues such as risk management, human resources and sales and marketing implications.

The first benefit at the top of everybody’s list is cost savings, and for good reason. However, there are several other legitimate benefits to undertaking a network study; not the least of which are the unexpected benefits resulting from the interdepartmental dialogue and interaction mandated by such an exercise.

Cost Savings: The general rule of thumb for savings opportunities associated with network analysis is 5 percent to 15 percent of those logistics costs that the study can influence. This will obviously vary from case to case and assumes that the current network is suboptimal. It also depends on your ability to institute change within the network. For example, if a particular distribution center must remain open to honor a contractual agreement, or the head of a family-owned business chooses to safeguard the plant where he founded the company, it might be possible to reduce cost-savings opportunities. They often come in the form of cost avoidance rather than cost reduction.  Many studies begin in an effort to locate new facilities in order to support planned volume growth or new market penetration rather than consolidate facilities to reduce cost. In this situation, it is difficult to quantify savings because there is no clear benchmark against which to compare the recommended solution.

Customer Service: Customers are becoming more demanding in their order-to-delivery requirements. The number of orders is increasing, the size of the orders is decreasing, willingness to accept imperfect orders is decreasing, and the management of delivery windows continues to tighten. Analysis of an optimized distribution center network can be constrained by these demands and will generate a solution that will support customer service requirements through sensitivities to lead time and order fill rate requirements.

Budgeting Tool: A network model can also serve as a budgeting tool for projecting future capital and operating cost requirements. As part of this budgeting process, a network model can quickly test alternative operating scenarios as well as predict the impact of acquisitions, new product introductions and other business changes that could occur during the next budget cycle.

Communication: In some companies, the biggest value of network modeling is that it serves as a catalyst to encourage dialogue between individuals from across the organization. Network modeling requires participation from all functions that impact, or are impacted by change to the distribution center network. As these individuals focus on the organization at large and voice their various concerns, new insights and knowledge always result.

In order to conduct a quality network analysis, significant effort must be dedicated to the collection and validation the various data elements. Also, give thought to the aggregation and representation of data within the model. Modeling generally occurs at a product group rather than an SKU level, with customers typically being aggregated by type and geography. In the end, there are three fundamental drivers to any network analysis: demand, costs and constraints.

Demand: Demand data describe the customer base and associated order volume and profile. You can typically glean this data from historic customer shipment data, ideally across a 12-month time period in order to capture seasonal demand patterns. You can aggregate the information as necessary to determine the allocation of volume by product, customer class, geography, and transportation mode. To obtain future state volume, product mix and allocation changes across customer, geography and transportation modes, you must select a design year (typically three to five years).

Costs: The number and types of cost data will vary depending on the scope of the analysis. In general, costs are either fixed (independent of demand) or variable (a function of demand). Fixed costs include capital investments and overhead expenses such as facility leases and administrative labor. Variable costs are generally synonymous with operating costs such as direct labor and transportation. Some costs, such as inventory carrying costs, are arguably comprised of both a fixed and variable component, and you can model them as such. One of the model’s tasks is to perform the trade-off analysis between fixed and variable costs.

A simple example is the decision to open an additional distribution center. Assuming that operational constraints do not force the opening of the facility, it is only justifiable if the variable cost savings that it provides offset the fixed costs required to open it. Fixed costs will increase as a result of the facility, equipment, administrative labor and inventory associated with the distribution center. However, the facility should reduce outbound transportation costs, while inbound transportation and variable facility costs may or may not decrease. If the operational cost savings offset the fixed expense, the facility receives a recommendation, otherwise it will not.

In some instances, cost data may be difficult to acquire, particularly when there is a need to allocate costs by product group and/or customer classification. Typically, you can derive manufacturing and distribution costs from site-specific operating statements. Aggregated transportation costs are obtainable from the same sources.

Constraints: Constraints are user-imposed requirements placed on the model that override cost considerations. Constraints can take on a variety of forms, but four are more prevalent than most. The first are capacity constraints placed on production lines, plants or distribution centers. The second are eligibility constraints. Eligibility precludes a dry distribution center from shipping refrigerated product or a can line from producing bottles. The third are customer service constraints. They mandate a maximum travel time or distance to customers. Service constraints often force the opening of facilities that would not otherwise be justified on the basis of cost. The fourth are open/closed constraints. These constraints establish a minimum or maximum number of facilities and identify specific facilities that require closure or must remain open.

The two biggest obstacles to a successful analysis are the availability of operating data and the ability to maintain focus on the objectives of the study. The latter is primarily a project management issue. Due to the number and variety of individuals participating in the study and their particular areas of interest, a network analysis can easily veer off course in attempts to address any number of issues for which it is unsuited.

Issues with operating data, on the other hand, are generally out of everyone’s control. In addressing data shortfalls, there are generally three methods of resolution. The first is to confirm that the data is truly necessary. In the context of a long-term strategic analysis, less-than-perfect data do not necessarily compromise the study and can still yield results which are directionally sound. The second is to develop work-arounds for missing information. These work-arounds take on many forms but are essentially best-guess assumptions in the absence of concrete information. Finally, for data that is critical to the analysis, you must make additional effort, often manually intensive, to derive the needed information.

A successful network analysis project boils down to data and personnel. The data required to perform the analysis must be accurate and accessible. It may be somewhat assumptive, but it must not compromise the integrity of the analysis. It must garner confidence from the management personnel because they will have to act upon the recommendations.

More important than data are people. First, an experienced, analytically minded person(s), from within or outside the organization is necessary to process the data, construct the model and guide the process. Second is a project team comprised of knowledgeable people from across the company who are capable of addressing the various business and logistics functions impacting the study. Third is upper management support.

A network modeling effort can easily be misconstrued as something it is not as well as lend itself to excessive amounts of what-if analysis. This is avoidable early in the project by explaining the strategic nature of the process, addressing the limitations of a network model and clearly defining the goals and objectives of the study.

In an ideal world, you will pursue the development of an optimal distribution center network so that the quantity, location and purpose of each facility are empirically correct and understood by your organization. The capability of modern-day analytical models is astonishing. Whether performed via internal staff or through the use of a consultant specializing in network modeling, models give you the power to evaluate millions of cost variables, hundreds of operating scenarios and virtually every network or business sensitivity and “what-if” scenario. Having this definitive empirical information is the foundation for distribution center design, real estate transaction, financing and all of the other components which are required to achieve a cost- effective distribution center network and satisfy or exceed customer demands.

I would like to thank Mike Jones, a principal with the St. Onge Co., for his invaluable assistance in the preparation of this article. BI