Top tips to build a robust supply chain

All businesses need to ensure its goods and services are procured at the lowest cost and meet the company’s needs in terms of timely delivery, quantity, quality, and location.

This is essential in not only providing the best customer experience, but also in ensuring you stay on top of your competitors, as consistency in the supply chain is key. However, the supply chain management world is constantly evolving and it is key to keep pace with both market expectations as well as opportunities.

Choosing and procuring the right technology is just the beginning of the variety of challenges that are present when managing and securing an IT supply chain. Organisations need to ensure effective asset management configuration and deployment are continuing to take shape, while maintaining technology standards and continuity of supply.

Here are some top tips in managing and creating a robust and effective supply chain, with experience and advice from the largest FTSE listed British IT service provider with over a 30-year heritage in IT and information enablement.

Be clear on expectation and deliverables

Many organisations will issue identical performance indicators and market assessment techniques on all engagements they have, irrespective of the technology being purchased or outcome desired by the business.

This is a detrimental approach as nuances and subject matter expertise are unable to be imparted by the partner that could potentially save money, time or actually mitigate risk.

Truly assess each engagement and accurately as well as realistically assess the desired outcomes/output that you wish to achieve, in comparison to work loads and true capabilities of workforces and systems.

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All in with online, can J.C. Penney get up to digital speed?

I had a few occasions chatting with the IT people of the company in the past few years. They were reluctant to adapt to the on-line trend of the retail market. One year, they wanted to expand their on-line catalog business; the next year, they closed the on-line catalog business and moves the majority of their IT people overseas in the following years. This time, it appears that the new SVP, Mike Amend, hired from Home Depot, is ready to face the on-line retail business challenges.

This article highlights a lot of positive actions for the company to transition itself from a traditional retail business to an on-line one.

  1. Recognizing its market strength: Research from comScore tells Penney that its customers have household incomes of $60,000 to $90,000, and they tend to be hardworking, two-income families living both in rural and urban settings. They don’t have the discretionary income to commit to membership fees.
  2. Last month, Penney added the ability to ship from all its stores, which immediately made about $1 billion of store inventory available to online customers and cut the distance between customer and delivery.
  3. About 80 percent of a store’s existing inventory is eligible for free same-day pickup.
    Last week, it offered free shipping to stores with no minimum purchase. Large items like refrigerators and trampolines are excluded.
  4. JCPenney.com now stocks four times the assortment found in its largest store by partnering with other brands and manufacturers.
  5. More than 50 percent of its online assortment is drop-shipped by suppliers and doesn’t go through Penney’s distribution. Categories added range from bathroom and kitchen hardware to sporting goods, pets and toys
  6. JCPenney.com now has one Web experience regardless of the screen: phone, tablet or desktop.
  7. Its new mobile app and wallet include Penney’s new upgraded Rewards program. Customers can book salon appointments on it. The in-store mode has a price-check scanner.
  8. Penney set out to “democratize access to the data,” so that not only the technical staff could understand it, now dashboards and heat maps allow the artful side of the business — the merchants — to measure such things as sales to in-stock levels or pricing to customer behavior.

Read more at All in with online, can J.C. Penney get up to digital speed?

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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

Thank you for reading. Should you need any further information, please do not hesitate to contact us.

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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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