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Cost and commodity price forecasting constitute very important analytics for a wide range of application areas. One of SimaFore's first applications of this process - which has since then been built into ForeCost, a webapp - was for an automotive supplier company. The app was generalized to enable small and medium manufacturers who do not have access to expensive statistical expertise to rapidly develop competitive bids for their products by generating accurate price forecasts based on historical costs (of raw materials, labor, transportation etc).
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.
Manufacturers spend an estimated $25B a year in supporting warranty claims on their products. The indirect costs in terms of loyalty and branding are significantly higher. As consumers, we are happy when a product that we buy comes with a solid warranty, but to an original equipment manufacturer (OEM), warranty can be a double edged sword. If an OEM provides good warranty support on its products, it wins if the product turns out to be reliable, but can lose significantly if it is not: both in the actual costs and on brand perception (and future sales). So it is clear that manufacturers should not only collect good information on warranty claims (which they all do), but also develop solid insights from the claims data about their products and their supply chain (which many manufacturers don't).
Tableau, the prom queen of data is finally going out with R, the alpha-geek of analytics. This is a moment a lot of us have been waiting for. Tableau will soon release their version 8.1 which allows super easy integration with R. I had the opportunity to test drive the beta version of 8.1 with really cool results. Below are a few initial impressions along with a simple workbook you can download and play with (if you have the beta version).
Maintenance is a significant cost factor in many organizations, and is under constant pressure of cost reduction. Unfortunately, there is also a tendency to only highlight the costs and disregard the benefits of a good maintenance policy. This could be due to the difficulties in rating and estimating the contribution of maintenance to the organization’s efficiency. This is exactly where the "internet of things" can clarify matters significantly. How is that? Let us examine it in more detail.
Time series forecasting has several applications within manufacturing businesses ranging from helping plant managers to set up and streamline their production schedules to helping executives arrive at meaningful budget estimates.
When you are managing a large organization that requires the use of a wide range of equipment, maintenance costs can be a significant problem. Maintenance costs are influenced by a variety of factors most of which are subject to large amount of fluctuations or variability in the operational frequency, failure rates, age of equipment, on field damage, scheduled maintenance, cannibalization, use of technology enhancers, and some human performance variables that affect maintenance performance (e.g., skill level, experience, training). The challenge organizations, ranging from manufacturing to healthcare, have is to identify when to perform maintenance under these conditions of variability so that their net cost is low. This is particularly difficult when you have a daisy chained sequence or a network of machines, so that a breakdown in any one machine will affect the throughput of the entire system.
We frequently get requests to train and place qualified consultants into right positions from different sources. Some of these job requirements make it very difficult to impossible find the right person. For example here is one such requirement:
For a lot of contract manufacturing companies today, the cost of labor is a big factor that influences profitability. The days of off-shoring to China may be coming to a slow natural decline. There are many reasons for this, primarily among them quality (I am sure many of us have experienced a product that was "Made in China" which broke down within a short period of time), cost of transportation, and also political reasons. Additionally, the Chinese themselves are apparently experiencing labor shortage issues. The giant Chinese manufacturer Foxconn (which supplies a lot of Apple hardware) had announced that they would install a million robots to improve efficiency and fight labor costs.
The National Association of Manufacturers and the National Center for the Middle Market (NCMM) recently completed a survey of what is considered "middle market manufacturing". These are companies in the revenue range anywhere from $10MM to $1B. The survey was aimed to understand how these companies view what are considered "Advanced Manufacturing Technologies".