Federal agencies are striving to become more innovative and iterative, leading to growing adoption of open source within the government. The issuance earlier this year of the Federal Source Code Policy illustrates how this technology, once anathema to government agencies, has become the de facto standard for the creation and deployment of many applications.
With the explosive adoption of open-source components being used to assemble applications, agency personnel are now tasked with ensuring the quality of the components that are being used. Developers must have confidence in components’ security, licensing and quality attributes and know for certain that they are using the latest versions.
Unfortunately, many agencies that are adopting the RMF are also relying on outdated and inefficient practices and tools that are not designed for today’s open and agile world. In addition to relying on potentially vulnerable components to build applications, some agencies have continued to depend too heavily on common application security tools, such as static application security testing and dynamic application security testing.
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Supply Chain Analytics: How Manufacturers Can Get The Most Value Out Of Their Automated Data Capture Technology
There are a prolific amount of data sets and data sources available that can help overcome the aforementioned challenges. The problem is that “big data” is coming in from so many varied sources, and manufacturers simply do not know what to do with it or how to use it. The answer lies in supply chain analytics. With the right analytical tools, manufacturers can obtain actionable, meaningful, and supported insight from available data in order to make better business decisions. When it comes to AIDC, supply chain analytics are most useful in two chief areas: device optimization (the technology itself) and labor management (those who are using the technology).
AIDC Device Optimization
With supply chain analytics, manufacturers can receive timely and relevant feedback about their AIDC platform to determine how the technology is performing – feedback beyond what is provided by a typical Mobile Device Management solution. Through this insight, users can better understand the underlying causes of inefficiencies, identify areas for continuous improvement, perform predictive analysis, and more. For example, through dashboard and reporting tools, manufacturers can easily see device utilization data to determine user adoption rates. They can monitor battery performance of their devices in the field to prevent downtime. Or, they can even make sure that the right tools are available at the right time. As a result, manufacturers can optimize their mobile deployments to attain additional ROI.
Labor Management
The second component to this equation involves labor management. Using supply chain analytics, it is possible to match the right tools with the right people, and the right people with the work. Analytics platforms accomplish this by gauging and managing the labor resources that use the technology in terms of measurement of activity benchmarking, engineered labor standards, and dashboard reporting. These tools take into consideration production data (volume), integrated with labor, cost, customers, and time data.
Achieving Analytics Success
Supply chain analytics tools can provide practical and fully actionable (fact-based decision making) information to help optimize the supply chain from an AIDC and human capital standpoint. Along with the right AIDC tools and support organization behind those tools, supply chain analytics can assist in driving more revenue, reducing your cost structure and improving the experience of your customers and your workforce. Yet, this is only a piece of the supply chain analytics puzzle. Looking forward, manufacturers will continue to extend the capabilities of analytics tools to gain insight into the overall performance of the manufacturing facility. With the influx of the Internet of Things (IoT), more data points are available than ever before, which allows manufacturers to gauge the efficiency of a particular production line or overall equipment effectiveness (OEE).
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