Space Optimization
Johnston McLamb has a well-defined methodology for developing a decision support system for space optimization that is customized to each customer's business. Space optimization is an automated process for identifying properties that can be vacated by moving the operations and assets within those properties to other properties that have suitable excess space. Johnston McLamb combines a visual business intelligence capability (showing properties and related data on a map) with a cost model that uses available data about the facilities and customer-specified business rules to determine an optimum outcome that meets the customer's business objectives.
Organizations that are already doing some sort of space optimization often perform it manually, which can be very time-consuming and may lead to sub-optimal results. For example, one large enterprise required about three weeks to optimize space manually for a small subset of its facilities. The space optimization system that Johnston McLamb developed for the company did the same work in seconds, produced better results, and helped the enterprise meet its goal of achieving lease expense savings and revenue increases exceeding an estimated $100 million per year.
For many organizations, just providing the ability to see all facilities on a map is a major breakthrough. Interactive user controls and filters instantly provide different ways to look at facilities and drill down to details, which is much more illuminating than working with tabular reports of data. Decision makers can run "what if" scenarios, make decisions more quickly than ever, and react rapidly to changing marketplace conditions.
The companies that can benefit most from optimizing the space in their facilities are ones with more than 500 facilities, annual revenue in excess of $1 billion, and who already own the necessary technology (in this case, an Oracle database and Oracle Application Server). U.S. federal government agencies are also candidates if they own or manage space in more than 500 buildings. However, organizations of any size can improve their business operations using Johnston McLamb's space optimization methodology.
How Space Optimization Works
Normally, the primary objective is to minimize lease and ownership expense by combining facilities. This is accomplished by moving the operations and contents out of some facilities and then vacating them. As a result, owned facilities can be sold or leased to another tenant, and leased facilities can be vacated as soon as possible.
Space optimization takes multiple factors into account. In order to move the contents or operations completely out of a facility, there must be another facility with enough space to accommodate them. When moving the contents or operations of one facility to another, it might be desirable for the two facilities to be within a certain distance of one another. The new facility must be suitable for the contents and operations of the facility being vacated. For example, it might not be a good idea to move the operations of a retail facility to a warehouse in an undesirable location, even though the warehouse might have enough space available. Finally, vacating a facility might result in lease or ownership savings, but it costs money to make the move. Organizations want to know if the move makes sense financially.
Ideally, the user of a space optimization solution can specify parameters for the factors described above (and perhaps others). For example, one parameter might be the minimum amount of available space that must exist in a facility in order for it to be eligible to receive the complete contents or operations of another facility. Another parameter might be the maximum distance between facilities to be combined. Users might specify that facilities can be combined only if they have certain factors in common (e.g., the purpose for which the facility is used). If facilities can be combined only if the net cost savings over a future period of time is positive, then a parameter would be the maximum amount of time allowed to achieve a positive rate of return.
A space optimization system uses a cost model that considers all the factors and determines the optimum combination of facilities to achieve the desired objective. For example, if the desired objective is to maximize cost savings over the next three years, then the system determines the solution that results in the greatest net cost savings while staying within the constraints of space, proximity and suitability. The system consists of a web based user interface (UI) and a back end analytics component. The UI displays the locations of facilities on a map. Facilities are represented by icons that convey information about each facility. For one such system, Johnston McLamb designed icons that used color and shape to convey four types of information: type of facility, amount of space inside the building, amount of parking space and total site s