How a pharmaceutical supply chain company is taking advantage of the Internet of Things

In 2014, during a routine check from the Ministry of Health in the U.S., it was found that only 55 percent of vaccines were stored and transported in the temperature conditions that ensured the medication maintained its quality. To put that into perspective, every baby born receives vaccines to prevent diseases such as small pox and measles. If only 55 percent of those vaccinations maintain safety requirements, that creates a situation where a majority of babies don’t get the quality dosage and medication they need to protect them from diseases.

To overcome this challenge, organizations are turning to technology. More specifically, the Internet of Things (IoT) is making it possible to ensure the safer transportation and delivery of medications. Dutch pharmaceutical services company, AntTail, is paving the way for building innovative IoT applications that more effectively track the conditions of medications while in transit.

The team at AntTail built an IoT application using the Mendix low-code application development platform. The application collects sensor data from medication shipments to provide information on temperature, as well as send push notifications to patients with reminders on when to take the medication.

One of the barriers for creating IoT apps is the requirement of many disparate technologies. AntTail uses a central router as a hub for all of the sensors, collecting the data when there is a connection and storing the data when there is no connection to ensure that no data is lost. The Router uses Vodafone’s Managed IoT Connectivity Platform as a way to connect to AWS, and has a Java service running that puts the data into Hadoop.

Read more at How a pharmaceutical supply chain company is taking advantage of the Internet of Things

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Transforming Integrated Planning & Supply Chain Processes with Augmented Intelligence Capabilities

In conjunction with the announcement, o9 released an eBook titled, “Who Gets the Cheese?”

Aptly named after one of the greatest business books of all time (Who Moved My Cheese?), this resource details one of o9’s systems for optimally allocating resources across initiatives and brands at consumer goods companies.

Founded by executives, practitioners and technologists that have led supply chain innovations for nearly three decades, the o9 team has been quietly developing a game-changing Augmented Intelligence (AI) platform for transforming Integrated Planning and Supply Chain processes.

The team has deployed the AI platform with select clients, including:

  1. Bridgestone Tires
  2. Asian Paints
  3. Restoration Hardware
  4. Party City
  5. Del Monte
  6. Aditya Birla Group
  7. Caterpillar
  8. Ainsworth Pet Foods

Speaking on behalf of o9 Solutions, Co-founder and CEO Chakri Gottemukkala said, “While executives we work with hear the buzz around technologies for data sensing, analytics, high performance computing, artificial intelligence and automation, they are also living the reality of slow and siloed planning and decision making because the enterprise operates primarily on spreadsheets, email and PowerPoint.”

Read more at Transforming Integrated Planning & Supply Chain Processes with Augmented Intelligence Capabilities

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How can Lean Six Sigma help Machine Learning?

Note that this article was submitted and accepted by KDnuggest, the most popular blog site about machine learning and knowledge discovery.

I have been using Lean Six Sigma (LSS) to improve business processes for the past 10+ year and am very satisfied with its benefits. Recently, I’ve been working with a consulting firm and a software vendor to implement a machine learning (ML) model to predict remaining useful life (RUL) of service parts. The result which I feel most frustrated is the low accuracy of the resulting model. As shown below, if people measure the deviation as the absolute difference between the actual part life and the predicted one, the resulting model has 127, 60, and 36 days of average deviation for the selected 3 parts. I could not understand why the deviations are so large with machine learning.

After working with the consultants and data scientists, it appears that they can improve the deviation only by 10%. This puzzles me a lot. I thought machine learning is a great new tool to make forecast simple and quick, but I did not expect it could have such large deviation. To me, such deviation, even after the 10% improvement, still renders the forecast useless to the business owners.

Read more at How can Lean Six Sigma help Machine Learning?

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6 in 10 businesses experienced at least one supply chain disruption in Asia Pacific in 2016

One in four businesses exceed ‎US$1 million in losses, but almost half of survey respondents in Asia Pacific did not insure their losses.

Zurich Insurance has revealed the key Asia Pacific findings of the Business Continuity Institute (BCI) “Supply Chain Resilience Report 2016”. Despite six out of ten organisations experiencing at least one supply chain disruption during the past year, with one in four exceeding ‎US$1 million in losses, the report found that almost half of survey respondents in Asia Pacific did not insure their losses.

Partnering with BCI for the eighth year, the annual report is regarded as one of the most authoritative benchmark reports in this business area. The key findings for Asia Pacific (APAC) this year are:

  1. IT/Telecom outages was named as the number one cause of supply chain disruption
  2. One in four organisations experienced cumulative losses of over ‎US$1 million
  3. 46% of organisations do not insure their losses, meaning they bore the full brunt of the cost
  4. Only 30% of disruptions occur with an immediate supplier
  5. 48% responded that top management have made commitments to supply chain resilience

Read more 6 in 10 businesses experienced at least one supply chain disruption in Asia Pacific in 2016

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The Analytics Supply Chain

Businesses across many industries spend millions of dollars employing advanced analytics to manage and improve their supply chains. Organizations look to analytics to help with sourcing raw materials more efficiently, improving manufacturing productivity, optimizing inventory, minimizing distribution cost, and other related objectives.

But the results can be less than satisfactory. It often takes too long to source the data, build the models, and deliver the analytics-based solutions to the multitude of decision makers in an organization. Sometimes key steps in the process are omitted completely. In other words, the solution for improving the supply chain, i.e. advanced analytics, suffers from the same problems that it aims to solve. Therefore, reducing inefficiencies in the analytics supply chain should be a critical component of any analytics initiative in order to generate better outcomes. Because one of us (Zahir) spent twenty years optimizing supply chains with analytics at transportation companies, the concept was a naturally appealing one for us to take a closer look at.

More broadly speaking, the concept of the analytics supply chain is applicable outside of its namesake business domain. It is agnostic to business and analytic domains. Advanced analytics for marketing offers, credit decisions, pricing decisions, or a multitude of other areas could benefit from the analytics supply chain metaphor.

Read more at The Analytics Supply Chain

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One-Page Data Warehouse Development Steps

Data warehouse is the basis of Business Intelligence (BI). It not only provides the data storage of your production data but also provides the basis of the business intelligence you need. Almost all of the books today have very elaborated and detailed steps to develop a data warehouse. However, none of them is able to address the steps in a single page. Here, based on my experience in data warehouse and BI, I summarize these steps in a page. These steps give you a clear road map and a very easy plan to follow to develop your data warehouse.

Step 1. De-Normalization. Extract an area of your production data into a “staging” table containing all data you need for future reporting and analytics. This step includes the standard ETL (extraction, transformation, and loading) process.

Step 2. Normalization. Normalize the staging table into “dimension” and “fact” tables. The data in the staging table can be disposed after this step. The resulting “dimension” and “fact” tables would form the basis of the “star” schema in your data warehouse. These data would support your basic reporting and analytics.

Step 3. Aggregation. Aggregate the fact tables into advanced fact tables with statistics and summarized data for advanced reporting and analytics. The data in the basic fact table can then be purged, if they are older than a year.

Read more at One-Page Data Warehouse Development Steps

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Six signs that your Big Data expert, isn’t

big-data-iceberg-napkin-21-608x608

This is so far the best article that I have been reading about the Big Data. It is what I have been advocating to people.

1. They talk about “bigness” and “data,” rather than “new questions”

… It seems most of the tech industry is completely drunk on “Big Data.”

… most companies are spending vast amounts of money on more hardware and software yet they are getting little, if any, positive business value.

… “Big Data” is a terrible name for the revolution going on all around us. It’s not about Bigness, and it’s not about the Data. Rather, it’s about “new questions,” being facilitated by ubiquitous access to massive amounts of data.

… If all you’re doing is asking the same old questions of bigger amounts of the same old data, you’re not doing “Big Data,” you’re doing “Big Business Intelligence,” which is itself becoming an oxymoron.

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A Tale of Two Disciplines: Data Scientist and Business Analyst

data scientist and BA

The ability to use data to achieve enterprise goals requires advanced skills that many organizations don’t yet have. But they are looking to add them – and fast. The question is, what type of big data expert is needed? Does an organization need a data scientist or does it need a business analyst? Maybe it even needs both. These two titles are often used interchangeably, and confusion abounds.

Business analysts typically have educational backgrounds in business and humanities. They find and extract valuable information from a variety of sources to evaluate past, present, and future business performance – and then determine which analytical models and approaches will help explain solutions to the end users who need them.

With educational backgrounds in computer science, mathematics, and technology, data scientists are digital builders. They use statistical programming to actually construct the framework for gathering and using the data by creating and implementing algorithms to do it. Such algorithms help businesses with decision making, data management, and the creation of data visualizations to help explain the data that they gather.

Read more at A Tale of Two Disciplines: Data Scientist and Business Analyst

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Supply Chain & Big Data ÷ Analytics = Innovation

Google the term “advanced analytics” and you get back nearly 23 million results in less than a second.

Clearly, the use of advanced analytics is one of the hottest topics in the business press these days and is certainly top of mind among supply chain managers.

Yet, not everyone is in agreement as to just what the term means or how to deploy advanced analytics to maximum advantage.

At HP, the Strategic Planning and Modeling team has been utilizing advanced operational analytics for some 30 years to solve business problems requiring innovative approaches.

Over that time, the team has developed significant supply chain innovations such as postponement and award winning approaches to product design and product portfolio management.

Based on conversations we have with colleagues, business partners and customers at HP, three questions come up regularly – all of which this article will seek to address.

  1. What is the difference between advanced and commodity analytics?
  2. How do I drive innovation with advanced analytics?
  3. How do I set up an advanced analytics team and get started using it in my supply chain?

Advanced analytics vs. commodity analytics

So, what exactly is the difference between advanced analytics and commodity analytics? According to Bill Franks, author of “Taming The Big Data Tidal Wave,” the aim of commodity analytics is “to improve over where you’d end up without any model at all, a commodity modeling process stops when something good enough is found.”

Another definition of commodity analytics is “that which can be done with commonly available tools without any specialized knowledge of data analytics.”

The vast majority of what is being done in Excel spreadsheets throughout the analytics realm is commodity analytics.

Read more at Supply Chain & Big Data ÷ Analytics = Innovation

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What’s the Difference Between Business Intelligence (BI) and EPM?

Business Intelligence Emerges From Decision Support

Although there were some earlier usages, business intelligence (BI) as it’s understood today evolved from the decision support systems (DSS) used in the 1960s through the mid-1980s. Then in 1989, Howard Dresner (a former Gartner analyst) proposed “business intelligence” as an umbrella term to describe “concepts and methods to improve business decision-making by using fact-based support systems.” In fact, Mr. Dresner is often referred to as the “father of BI.” (I’m still trying to identify and locate the “mother of BI” to get the full story.)

The more modern definition provided by Wikipedia describes BI as “a set of techniques and tools for the acquisition and transformation of raw data into meaningful and useful information for business analysis purposes.” To put it more plainly, BI is mainly a set of tools or a platform focused on information delivery and typically driven by the information technology (IT) department. The term “business intelligence” is still used today, although it’s often paired with the term “business analytics,” which I’ll talk about in a minute.

Along Came Enterprise Performance Management

In the early 1990s, the term “business performance management” started to emerge and was strongly associated with the balanced scorecard methodology. The IT industry more readily embraced the concept around 2003, and this eventually morphed into the term “enterprise performance management” (EPM), which according to Gartner “is the process of monitoring performance across the enterprise with the goal of improving business performance.” The term is often used synonymously with corporate performance management (CPM), business performance management (BPM), and financial performance management (FPM).

Read more at What’s the Difference Between Business Intelligence (BI) and EPM?

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