Understanding the Analytics Supply Chain

In the article The Analytics Supply Chain, the author explains an interesting concept that appears to be a bottleneck for big data analytics projects at firms.  This issue lies within the analytics supply chain itself.  Deploying big data projects has become more and more popular, but the results are not always satisfactory.  Often projects take too long to source the data, build the models, and deliver the analytics-based solutions to decision makers in an organization.  In a twist of fate, the analytics supply chain becomes the issue for supplying the analytics which was meant to help the supply chain.  The author suggests looking at the analytics supply chain in terms of customers being decision makers and the products being consumed are analytical models.  Just as a normal supply chain, bad inputs usually equates to bad outputs.

When thinking of data as raw material and output as models, bad or incomplete data usually results in poor or incomplete models.  Furthermore, sometimes sourcing enough data for complete models takes too long, and thus substitutes are used such as spreadsheets.  The article states that between 20% and 80% of spreadsheets have been found to have errors, and as one might imagine, errors lead to the proliferation of different versions of the truth.  So a complete model using complete, accurate data is necessary.  And such models can take time, and one can think of unfinished models as inventory.  Inventory does not contribute to the bottom line, so the requirement of complete models outputted in a reasonable amount of time becomes important.  Perhaps this requires a more precise approach or hiring more individuals, but it’s important to recognize that data driven models that are too complex or incomplete will normally not deliver the analytics based decisions as anticipated.

Finally, the author makes some suggestions about identifying if an analytics supply chain is in need of repair.  If analytics projects are hindered by lack of IT, data, and/or other scarce technical resources, there might be an issue in the analytics supply chain.  If a firm’s ability to proceed with new analytical models is hindered by the constant maintenance on older systems, there may be an analytics supply chain issue.  If big data systems have been employed, but the results don’t seem to justify the investment, perhaps it’s time to evaluate the analytics supply chain as opposed to scrapping big data projects because they appear to be a waste of money.  Evaluations like this could save firms money in multiple ways not to mention the time invested in preexisting projects.

 

Has anyone ever heard of evaluating the analytics supply chain?

Have you ever run across models that were too complex to implement?

Have you seen any instances where big data projects were scrapped because results were not produced fast enough?

http://data-informed.com/the-analytics-supply-chain/

Process Mining – Big Data Working for Manufacturers

In the article Why Manufacturers Need Process Mining – A New Type of Data Analytics, the author is extolling the benefits of what he calls a new type of data analytics – process mining.  Process mining can be used to reduce inventory costs, identify production bottlenecks, improve on-time delivery, and optimize logistics between production sites, distribution centers, and end clients.  It’s hard to justify  that process mining is actually a new type of data analytics that can be used for manufacturers, but process mining does follow the a few of the key rules for successful big data usage.  First, as the name implies, it concentrates on one process.  Implementation of successful big data projects normally requires concentration on one area for improvement.  The specificity of big data projects usually allows them to be successful, and the article looks at why process mining, and big data analytics on the whole, can be successful in saving manufacturers money.

Process mining specifically looks at a very important factor in manufacturer’s processes, KPIs or key performance indicators.  KPIs are exactly what they sound like, the main factors that measure performance and the overall successfulness of process or project.  Process mining’s value lies in the fact that it initially makes one look at KPIs of a process.  It also challenges the manufacturer to validate if they are measuring the right KPIs and understand if the data they are gathering can be related back to KPIs.  Process mining, as all big data does, uses software do the difficult work of visualizing the processes and highlighting specific variances impacting KPIs.  One example used in the article is the examination of throughput times and the ability to identify that specific vendors are not meeting their lead-time commitments.  These types of analyses and results is exactly what big data projects are meant to do and achieve.

Another point that the article points out is that process mining encourages manufacturers to identify inefficiencies and problems within the process.  It encourages companies to embrace their issues.  This ideology is absolutely necessary for continuous improvement, and it is a key to any big data project.  Embracing issues can be difficult, especially for older, engrained processes, but it is the only way to eliminate them.  It certainly is not the easiest thing to do, and requires a humble mindset entering an improvement project.  Process mining has all the key factors of a successful big data software, and it could be very useful for manufacturers that want to embrace the big data revolution.

 

Do you believe addressing KPIs first is the best way to approach a big data project?

Do you think process mining is actually a new or different type of big data analytics or just a rebranding of basic big data?

Do you think that some manufacturers are reluctant to implement big data projects because they do not want to know their inefficiencies?

 

http://www.mbtmag.com/article/2017/02/why-manufacturers-need-process-mining-new-type-big-data-analytics

McKinsey Study Shows Big Data is Affecting Numerous Industries Including Manufacturing

In the article How McKinsey’s 2016 Analytics Study Defines the Future of Machine Learning, the investigates a McKinsey study released in January of this year that shows how far big data and analytics have come in the previous 5 years as well as what to expect in the very near future.  The main insights gleaned show that the use of big data is growing in numerous industries, and that the key realizing the value of big data is integration and adopting from the top down.  For manufacturing specifically, machine learning leading to preventative maintenance and predictive analytics is one of the most powerful advantages that has emerged.  Unfortunately, just about every industry examined in the paper has still has not captured more than 50% of the potential value from data and analytics.

The original paper released by McKinsey released in 2011 speculated that manufacturing would find the greatest potential value in data and analytics from possible drops of 50% in product development cost, 25% lower operating cost, and 30% gross margin increases.  As of 2017, the new McKinsey paper shows that manufacturing has only captured 20-30% of this total value with the main issues being siloed data in legacy IT systems along with skeptical and sometimes unsupportive leadership.  With regard to one of big data’s most powerful uses, machine learning, manufacturing stands to realize significant value from machine learning.  In the areas of real-time optimization, predictive maintenance, and forecasting, there is definitely some value to be had in regard to machine learning.  In the areas of strategic optimization, predictive analytics, and understanding data trends, there is substantial value to be had.

Design-to-value, supply chain management and after-sales support are three areas where analytics are making a financial contribution in manufacturing.  Unfortunately, manufacturing as a whole has lagged in adoption with a few advanced players capturing a significant amount of the value.  Digging deeper to see exactly where the money has been saved, analytics has benefited the supply chain and production aspects of manufacturers, but only the small groups that have adopted the technology.  McKinsey estimates that only 5-10% of manufacturers have used analytics for financial savings in the supply chain and production aspects.  In the supply chain, the cost savings has come in the form of 10-15% reduction in product cost, and in production side, the cost savings has come in the form of 10-15% reduction in operating costs.  Overall, the main hindrances to more big data implementation is senior management involvement and designing and implementing the appropriate organization structure to support data and analytics.

Do you believe big data analytics adoption accelerate over the coming years?

Do you believe the results of the study are completely accurate?

How far away are we from a time where if manufacturers are not using analytics, they will not be able to compete in the market?

http://www.cloudcomputing-news.net/news/2017/jan/05/mckinseys-2016-analytics-study-defines-the-future-of-machine-learning/