Getting it Right the First Time

Using an Advanced Analytics Solution to Speed Business Value in Your Supply Chain System

When organizations spend millions of dollars on a new supply chain system, decision makers expect—and demand—superior performance. A global apparel manufacturer got just the opposite when its supply and demand planning system implementation went horribly wrong. An excess of unwanted stock and shortages of high-demand items contributed to a $100 million earnings loss in just one quarter. The impact was extensive. How could the company have prevented it? With advanced analytics and simulation.

With an innovative advanced analytics and simulation approach, defense and government organizations can take supply chain system performance to the next level, identifying the right group of parameter settings long before go-live. With this, they can achieve specific and time-sensitive business outcomes that are imperative in this environment.

Learn about Accenture’s four-step process for speeding supply chain business value with innovative advanced analytics solutions, and discover how it can change the game for implementing new supply chain systems.

As vital as a retail supply chain is to business, government and defense supply chains are lifelines unlike any other. A supply chain disaster in these high-stakes environments would mean more than lost revenue or declining shareholder value—it could endanger missions, and even lives.

As such, government organizations know it is essential to realize the promised benefits of forecasting, advanced planning and supply chain management systems, whether deploying them for the first time or optimizing them for sustained business performance. But for many government agencies, this is easier said than done.

With large-scale, diverse missions and complex business requirements, identifying the right parameter settings and the right business rules to achieve desired business performance is challenging. Getting it wrong leads to degraded business performance, cost overruns, wasted time and, frequently, program termination.

Implementing new supply chain systems lets agencies steer the enterprise in virtually any direction. These systems provide organizations with countless parameter and configuration options. Yet as robust as these capabilities are, they muddy the waters by providing a multitude of directions, adding complexity and confusion to the process.

Frequently, organizations rely on legacy or “rule of thumb” approaches when configuring these new systems. Other organizations look to their software vendors to recommend forecasting models and settings, which are generic and never tuned to their specific business needs. Still others focus on whether the system works at the most elementary level, rather than getting a full picture of the business value it yields.

In contrast, advanced analytics can be used in early planning and analysis phases to identify the right system settings and project the values and time periods of expected business outcomes. What results are:

• Shorter design phases
• Accelerated system configuration and testing
• Quicker time to value

Accenture has developed a four-step process to rapidly simulate the impact of many different configuration settings on a broader set of enterprise performance metrics over time.

1. Collect data
   Historic system data is collected from the supply chain system in order to emulate business performance over a proscribed period of time. This data will also establish a baseline level of business performance on which to base projected changes in configuration settings and business rules.
   
   
2. Configure scenarios
   Scenarios are developed for the various configurations being considered. Depending on the sophistication of the analytical software used, the scenarios can also consider a variety of different forecasting and replenishment analytical solutions being considered for implementation. The most important aspect of this step is documenting the various demand and supply planning configuration settings being considered.
   
   
3. Generate business outcomes
   Formally consider the projected business outcome when deciding what configuration settings to implement. These outcomes are measured by considering a weighted combination of metrics that measure cost, service and capacity. This allows organizations to compare multiple business outcomes and lets management choose the “best” configuration to meet enterprise business objectives.
   
   
4. Score and recommend
   Once the business outcomes are generated, results are scored and final configuration settings recommended and approved. This allows the enterprise to influence relative results when comparing business outcomes from more than one scenario by applying “weight” to each business outcome characteristic. The analysis is beneficial because it shows relationships and impact among all characteristics, helping decision makers understand opportunities and trade-offs.