Simafore provides tools and expertise to:
The Analytics Compass Blog is aimed at two types of readers:
individuals who want to build analytics expertise and
small businesses who want to understand how analytics can help them improve their business performance.
If you fall into one of these categories, join hundreds of others and subscribe now!
Critical manufacturing operations require very high service levels from the factory equipment and facilities. Service level is be defined as the ratio of equipment delivered (or available to utilize) when needed, to the total number of equipment requests. Service levels can be kept high by two means: high levels of redundancy (i.e. stocking a very high inventory of excess equipment and/or replacing with short delivery times) or maintaining high availability in combination with short delivery times. However the first option is impractical, particularly with limited budgets or in cases where demand is not smooth, but “lumpy”. This shifts the focus on developing a robust equipment preventative maintenance strategy that can avoid unplanned and extended equipment downtimes, and thus increase availability and reduce operating costs. Since availability has a substantial impact on the efficiency of a system, maintenance strategy optimizations should be based on analytic models that can accurately capture, measure and predict availability.
When the data.gov website was launched in 2009, it had a measly 47 datasets. Four years later it has exploded to nearly 100,000 data sets in more than 50 formats. This is merely the public facing data which the government makes available to the tax paying citizenry. The "other" government data (still funded by taxes) which are not openly available to all, due to security and other reasons is clearly significantly larger. EMC Corporation recently released a report where they indicated that only about a quarter of this data is tagged and analyzed by the government currently. Officials have been quoted as saying that in the next 5 years, the feds will spend about $13 billion (16% of the total IT budget) to improve big data infrastructure and develop data mining best practices for this data. The report also summarized the top three areas where large government agencies can best leverage big data and analytics: improving process and efficiency, enhancing security and predicting trends.
While Key Performance Indicators (KPI) offer a rational basis for judging performance, there are two main challenges. The first challenge is selecting the right key performance indicators for your business. How do you know which are the best KPIs for your needs? The standard solution is to monitor several KPIs simulataneously. This can lead to data overload and become a barrier for effective communication of business performance to executives.
The term KPI, for Key performance indicator is a widely used catch-all phrase in many industries today. This is of course, a very healthy trend because it indicates that businesses are rightly placing emphasis on measuring performance objectively. There are many important facts to consider while selecting or forming a KPI and one KPI does not fit all functions within a company, and in the same way one KPI does not fit all industries.