Getting better performance out of supply chain and operations activities is not at the top of everyone’s priority list, but it should be. In today’s world, moving things smartly and efficiently – regardless of whether they are people, product or petabytes – is paramount to success.

This challenge is not a new one. But, the pressures of meeting customer demands, fulfilling their mission requirements, and maintaining high service levels is significantly complicated at a time of diminishing budgets. Even though government organizations recognize the importance of making their operations more efficient, leaders must prove the benefit of the change before investing time, people, and money in system and process improvements.

Another way of saying that is they must have an unwavering focus on outcomes. That’s something leading private businesses and government agencies are trying to accomplish through modeling, simulation and predictive analytics.

These tools allow smart organizations to fully comprehend and quantify the impact of changes to their existing supply chains before making modifications so they can develop and implement strategies for immediate – and long-term gains. From the U.S. Department of Defense to Major League Baseball, big data analytics is proving to be a cost-effective tool for improved efficiencies in supply chains, logistics and budgets.

Getting in Tune

Modeling is much more than a theoretical exercise, and when used in the real world can result in significant return on investment.

Sony Electronics learned this first hand when it implemented modeling to enhance its integrated marketing campaigns and reaped rewards to the tune of a 14% increase in incremental revenue. Sony did not stop there. It also put in place an agile marketing analytics platform our company developed for them – software which provided valuable insights during the budgeting cycle and helped the company channel marketing investment activities to deliver even higher returns.

Just like any substantial undertaking in business, following a roadmap that clearly outlines the objectives, the processes and the challenges is fundamental to achieving success. While easily said, this is not always easily developed.

To help ensure success for its clients, we have found that using a four-step process can help to rapidly simulate the effect of different configuration settings on a broad set of enterprise performance metrics over the passage of time for any given situation.

The first step involves gathering and cleansing the large amount of existing data that organizations collect.

Second, the data is put through a series of modeling scenarios which determine alternatives against the baseline status quo.

Third, simulations are executed to predict business outcomes – including cost, inventory and performance – across all scenarios.

Finally, results are rationalized and scored, and recommendations are made based on the organization’s objectives.

Batter Up

These four steps cover a lot of territory and a lot of data, which can be overwhelming and difficult to rationalize to the common observer – much like trying to make sense out of the statistics and data that rule the game of baseball. Modeling and predictive analysis are key regardless of the game being played.

To the casual observer, aside from the physical act of playing baseball, the game itself can seem like an overwhelming collection of statistics and data that does not make any sense. In fact, if you were to restrict the announcers from quoting statistics they wouldn’t have much to say. But, the business of baseball is a lot more than the statistics of the game.

Major League Baseball (MLB) relies on specific data to run its businesses – including completing financial processes, marketing its brands, vying for top players, managing staff and adapting to market conditions.

One would think because of its long, rich history, and MLB’s embarrassing riches of accumulated data that it would have the insight to achieve and sustain high performance across its businesses. This, however, this was not the case. A prime example of this was the inability of MLB executives to make informed personnel decisions despite having a system that stored all the players’ data in one location.

To help MLB more clearly see the patterns their data offered, Accenture for example developed a new analytics-based process enabled by a multi-tier technical architecture. The analytic model employed modern techniques of performance assessment and new processes to capture, store and retrieve game logs, play-by-play data and player performance statistics with drill down functionality.

The model also provides a means to conduct data-intensive comparative analysis across statistics and contractual data. MLB still has its vast amount of data that grows with every swing of the bat, but it now also has a holistic view of operations from a single, consolidated data repository that fuels fact-based, multi-criteria, comparative analysis.

Show Me the Way

Government organizations can learn a lot from MLB and commercial giants like Sony who have successfully navigated seemingly endless fields of data. In fact, with commercial innovation successes as a guidepost for government challenges, several federal agencies are already using modeling, simulation and predictive analytics to prove the efficiencies they are making in their complex supply chains.

As the United States continues to fight the global war against terrorism and provide critical support for humanitarian missions, the Defense Logistics Agency (DLA) supports an unparalleled mission to provide valuable logistics and contract management support to U.S. Armed Forces around the world. To deliver the right item, at the right time, at the right place for the right price, DLA has undertaken focused supply chain refinements that help every step of the way to better support the warfighter.

DLA has worked over the past several years to modernize its supply chain processes and systems such as supply and demand planning, material management, procurement, order fulfillment and financial management.

An essential component of this modernization was the development of an enterprise business system which provides integrated processes and shared capabilities across their eight supply chains. Focusing on improving customer service and reducing costs, DLA is better able to predict, procure, manage and route logistical transactions that are required to handle DLA’s massive demand.

As part of the Comprehensive Inventory Management Improvement Plan (CIMIP), the Office of the Deputy Assistant Secretary of Defense, Supply Chain Integration (OSD SCI) and the Government Accountability Office (GAO) outlined a need to identify improvements across the entire Department of Defense (DOD) forecasting process, particularly around the life-cycle of items and excessive inventories.

Having been awarded a two-year contract with OSD SCI, Accenture Federal Services is using advanced analytics and simulation tools to determine how enhanced forecasting processes may help improve forecast accuracy and lower inventory levels, while maintaining or improving supply chain performance across DOD.

While federal supply chains face unique challenges that are not always found in commercial businesses, there are many similarities. In both cases, there is a great advantage to incorporating the latest modeling, simulation and predictive analytics techniques prior to making change.

This focus on the outcome from the start should be factored into every decision. While the move from massive information to straightforward operations may be difficult, federal agencies that begin with a holistic approach will achieve greater rewards. Knowing is always better than guessing – especially for the citizens and businesses federal agencies serve.

John Goodman and Jeff Scott Miller are senior executives with Accenture Federal Services. Goodman is managing director, Defense and Intelligence portfolio, and interim Chief Executive. Miller is vice president for Defense Supply Chain Solutions in the Defense and Intelligence portfolio. Learn more about Accenture’s US defense business and its work with federal agencies