Supply chain analytics: 5 tips for smoother logistics

Organizations are increasingly turning to data analytics to navigate supply chain disruptions and to enhance their SCM efforts. Here’s how to do it right.

Organizations are increasingly turning to data analytics to navigate supply chain disruptions and to enhance their SCM efforts. Here’s how to do it right.

The worldwide supply chain challenges that plagued companies in multiple industries throughout 2021 are continuing this year. One potentially effective solution for addressing supply and demand issues is to leverage data analytics.

Professional services and consulting firm KPMG in a recent report notes that several major disruptions are currently affecting supply chains. These include the ongoing global logistics disruptions stemming from the COVID-19 pandemic that continue to impact businesses and consumers — as the flow of goods into key markets is restricted by shutdowns of major global ports and airports.

The major logistics disruptions create a ripple effect across global supply chains that ultimately cause goods to pile up in storage, the firm says. Assuming that these disruptions decrease and access to sea and airfreight reverts back to pre-pandemic levels, it will likely take some time before things return to normal, it says.

Other factors contributing to supply chain problems include production delays, over reliance on a limited number of third parties, and labor market shortages. The report also points out that many companies are investing in technologies to automate key nodes within the supply chain.

This year will see an accelerated level of investment, KPMG says, as businesses look to enhance critical supply chain planning capabilities by adopting more advanced “digital enablers” such as cognitive planning and AI-driven predictive analytics.

“The onset of new technology has fundamentally changed the way supply chains operate globally,” the report says. “The consumers are becoming more demanding, and this is leading the supply chains to change and evolve at a faster rate. Modern operations are focused on technology and innovations, and as a result, supply chains are becoming more complex.”

How can organizations best use data analytics to enhance their supply chain management (SCM) efforts? Here are some best practices, according to experts.

Turn data into actionable, simple insights

Most companies are awash in large volumes of data, often stored in diverse systems and databases, says John Abel, CIO at networking technology company Extreme Networks. Supply chains have the added complexity of additional data sources being generated from extended partners such as outsourcing, logistics, and distribution operations, he adds.

“As a result, many struggle to use this data to generate meaningful insights beyond top-level metrics and descriptive statistics,” Abel says. “Data analytics tools can deliver deeper, actionable insights as well as improve accuracy of those insights.”

Focus analytics on difference-making areas

Supply chain organizations are being inundated with data such as customer orders, item information, equipment utilization, and ever-evolving transportation costs, says Erik Singleton, expert practitioner for global supply chain at consultancy North Highland Worldwide Consulting.

“The key to building a successful, customer-centric supply chain while maximizing operational efficiency is using the right analytics to make data-driven decisions,” Singleton says. He recommends that supply chain organizations focus their analytics on three main areas.

Leverage real-time data to deal with disruptions

As both the size and complexity of supply chains grow globally, it is becoming exponentially more difficult to manage and respond to fluctuations across the supply chain, Abel says.

“With data points changing rapidly, analysis and decision-making is often based on outdated information and further exacerbated by the time needed to effectively analyze the data,” Abel says. “To navigate this successfully, supply chain managers need to develop concurrent planning systems that optimize demand and supply by utilizing advanced analytics and real-time visibility across the supply chain.”

Emphasize data governance and quality

The old adage about information, “garbage in, garbage out,” certainly applies to supply chain data, says Mark Korba, vice president of supply chain and business intelligence at Optimas Solutions, a fastener manufacturer and distributor.

“It is important to validate data, especially since it is coming from a variety of sources,” including customer inventory management systems, demand planning applications, supplier software, and others, Korba says. “Often the data isn’t consistent or managed the same across systems, and therefore lacks integrity.”

Make supply chain analytics broadly available

SCM involves multiple facets of the organization, so analytics capabilities need to be shared liberally.

“Make it easy for everyone involved in the supply chain to get the data and tools that they need,” says Arthur Hu, senior vice president and CIO at computer hardware provider Lenovo. “This first requires breaking down any ‘information silos’ and establishing an integrated end-to-end information system.”

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Oracle releases new cloud analytics offering for Oracle Fusion SCM offering

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Oracle, a global provider of integrated cloud applications and platform services, announced it rolled out a new cloud analytics offering for its shipper customers using its Oracle Fusion Cloud Supply Chain & Manufacturing (SCM) platform, which connects shippers’ supply networks with an integrated suite of cloud business applications.

Earlier this month, Oracle, a global provider of integrated cloud applications and platform services, announced it rolled out a new cloud analytics offering for its shipper customers using its Oracle Fusion Cloud Supply Chain & Manufacturing (SCM) platform, which connects shippers’ supply networks with an integrated suite of cloud business applications.

Oracle said that the new cloud analytics provide shippers with the needed insights “to detect, understand, and resolve issues faster throughout the supply chain.” And they added that in leveraging Oracle Analytics Cloud and Oracle Autonomous Data Warehouse, the new Oracle Fusion SCM Analytics provides shippers with pre-built metrics and dashboards that utilize machine learning capabilities that help shippers on various fronts, including reducing costs, ensuring customer satisfaction, and driving revenue.

“Supply chains are under immense scrutiny as organizations face new and unexpected disruptions,” said T.K. Anand, senior vice president, Oracle Analytics, in a statement. “Now more than ever, organizations need real-time insights into every element of their supply chain to help them make the right decisions and get ahead of disruptive events and changing customer expectations. With Oracle Fusion SCM Analytics, customers can quickly uncover supply chain performance insights, identify issues, increase efficiency, and minimize supply chain disruption.”

Jon Chorley, GVP of SCM Product Strategy and Chief Sustainability Officer, Oracle, provided LM with a detailed overview this new offering in interview.

  1. LM: What drove the need for Oracle to roll out Oracle Fusion SCM Analytics?
  2. LM: What are the main benefits of the new analytics capabilities for shipper customers?
  3. LM: Can you please provide a basic example of how it functions?

This example highlights how Oracle Fusion SCM Analytics provides customers with new ways of working with data by using machine learning-powered predictions, which helps organizations gain actionable insights to improve supply chain performance – and ultimately deliver the best possible customer experience.

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Walmart Canada partners with FourKites for supply chain analytics

The Canadian branch of Walmart has agreed a new partnership with FourKites for the development and furthering of the company’s supply chain visibility and predictive analytics capabilities.

Walmart Canada will use FourKites supply chain platform to track the real time location and predictive shipment times across its Canadian operations that span over 400 stores a number of distributions centers within the region.

Walmart staff will be able to use FourKites’ mobile app to track these, leveraging the company’s GPS-connected assets.

“Walmart Canada’s partnership with FourKites reflects our deep commitment to delivering an outstanding customer experience,” said John Bayliss, senior vice president, logistics & supply chain.

“We will use FourKites’ predictive tracking technology to know precisely when shipments will arrive at our distribution centers and at our stores, so we can ensure that customers find the products they’re looking for so they can save money and live better.”

The implementation of this technology will allow Walmart Canada to better optimize its operations, including staffing levels, assignments and minimizing truck waiting times.

Read more at Walmart Canada partners with FourKites for supply chain analytics

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Asda and Co-op to work together to drive supply chain efficiencies

Asda and the Co-op are pioneering a new way of supply chain collaboration, by enabling mutual suppliers to submit aggregated data on waste, water and energy to both retailers at the same time.

The retailers are working with collaboration platform 2degrees to collect the sustainability data.

Under the agreement, suppliers who serve both retailers can submit the data once, indicating their combined data should be shared with both customers.

It is hoped that by eliminating the need for duplicated information, suppliers will be able to spend more time focussing on the delivery of quality products whilst saving on time, money and resources.

Both retailers have seen suppliers benefit from the platforms delivered by 2degrees. The Co-op, one of the founding partners of multi-client platform Manufacture 2030 has already seen a drinks supplier start addressing their carbon footprint through the platform.

Andy Horrocks of Kingsland Drinks, said: “We are looking closely at a case study shared by another drinks supplier on Manufacture 2030, and using it as a model of how this key environmental process could be done, helping us to sell the idea internally.”

Princes Limited is a key supplier to both Asda and Co-op, and has spoken of the benefits of aligned data.

David McDiarmid, Corporate Relations Director at Princes Ltd, commented: “It’s great that two retailers like Co-op and Asda have embraced this approach. With all our manufacturing locations sharing their data between these customers we have cut down duplicated effort, saving time and making the entire process a lot more efficient.

“I hope other retailers will see the benefits of such a collaborative approach and consider it for their suppliers environmental reporting.”

Read more at Asda and Co-op to work together to drive supply chain efficiencies

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Latest moves of Amazon and Walmart confirm the death of the middle class as we know it

Amazon, whose Prime service claims more than 70% of upper-income households in the US — those earning more than $112,000 a year — is suddenly going after customers on government assistance who earn less than $15,444 a year for a one-person household.

The retailer on Tuesday announced it would slash the cost of its monthly Prime membership nearly in half, to $5.99 a month, for customers who have an electronic benefit transfer card, which is used for government assistance like the Supplemental Nutrition Assistance Program, better known as food stamps.

“It’s a shot over the bow at Walmart,” said Doug Stephens, a retail-industry consultant. In other words, the strategy is a direct grab for Walmart’s core customers. Nearly $1 out of every $5 in SNAP benefits was spent at Walmart last year, according to Morningstar.

At the same time, Walmart is going after Amazon’s core customers with its $3 billion acquisition earlier this year of Jet.com, which attracts a younger and higher-income group of shoppers than Walmart. The retailer has also recently been snatching up trendy online retailers like ModCloth, Moosejaw, and Shoebuy, and it’s reportedly considering a bid for the high-end menswear brand Bonobos.

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Amazon’s and Walmart’s latest moves confirm the death of the middle class as we know it

Amazon and Walmart are battling for shoppers at the highest and lowest ends of the income spectrum, leaving the middle class in the dust.

Amazon, whose Prime service claims more than 70% of upper-income households in the US — those earning more than $112,000 a year — is suddenly going after customers on government assistance who earn less than $15,444 a year for a one-person household.

The retailer on Tuesday announced it would slash the cost of its monthly Prime membership nearly in half, to $5.99 a month, for customers who have an electronic benefit transfer card, which is used for government assistance like the Supplemental Nutrition Assistance Program, better known as food stamps.

“It’s a shot over the bow at Walmart,” said Doug Stephens, a retail-industry consultant. In other words, the strategy is a direct grab for Walmart’s core customers. Nearly $1 out of every $5 in SNAP benefits was spent at Walmart last year, according to Morningstar.

At the same time, Walmart is going after Amazon’s core customers with its $3 billion acquisition earlier this year of Jet.com, which attracts a younger and higher-income group of shoppers than Walmart. The retailer has also recently been snatching up trendy online retailers like ModCloth, Moosejaw, and Shoebuy, and it’s reportedly considering a bid for the high-end menswear brand Bonobos.

Read more at Amazon’s and Walmart’s latest moves confirm the death of the middle class as we know it

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Cloud-Based Analytics for Supply Chain and Workforce Performance

Plex Systems, a developer of cloud ERP for manufacturing, has introduced two new analytic applications designed to provide manufacturers insight into supply chain performance and their workforce.
The new Supply Chain and Human Capital analytic applications build on the library of applications in the IntelliPlex Analytic Application Suite, a broad suite of cloud analytics for manufacturing organizations.

The Plex Manufacturing Cloud is designed to connect people, processes, systems and products in manufacturing enterprises. The goal is not only to streamline and automates operations, but also enable greater access to companywide data. The IntelliPlex suite of analytic applications aims to turn that data into configurable, role-based decision support dashboards–with deep drill-down and drill-across capabilities. The IntelliPlex Analytic Application Suite includes analytics for sales, order management, procurement, production and finance professionals.

IntelliPlex Supply Chain Analytic Application
The new IntelliPlex Supply Chain Analytic application provides a dashboard for managing strategic programs, such as enterprise supplier performance, inventory and materials management and customer success. Metrics include:

  1. On-time delivery and return rates by supplier, part, material, etc.
  2. Production backlog by part group, product time, etc.
  3. Spend by supplier and type, including unapproved spend
  4. Inventory turns and aging based on type, location, etc.
  5. Materials management accuracy, adjustments and trends by type, location, etc.
  6. On-time fill rate, customer lead time, average days to ship, fulfillment by location

Read more at Cloud-Based Analytics for Supply Chain and Workforce Performance

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Former Microsoft CEO Launches New Tool For Finding Government Data

This Tax Day, former Microsoft CEO Steve Ballmer launched a new tool designed to make government spending and revenue more accessible to the average citizen.

The website — USAFacts.org — has been slow and buggy for users on Tuesday, apparently due to the level of traffic. It offers interactive graphics showing data on revenue, spending, demographics and program missions.

For example, the site prominently features an infographic created to break down revenue and spending in 2014. Revenue is broken down by origin; spending is broken down by what “mission” of government it serves, based on the functions laid out in the Constitution.

It’s a big-picture view of where U.S. tax dollars come from, and how they’re spent. But click on a subcategory and you’re taken to a more detailed, granular view of that spending.

Ballmer didn’t create the site because he was an expert on government data. Quite the opposite, according to The New York Times’ Dealbook.

The Times says that Ballmer’s wife was pushing her newly-retired husband to get more involved in philanthropy. Ballmer said — according to his own memory, as he described the conversation to the Times — “But come on, doesn’t the government take care of the poor, the sick, the old?”

Read more at Former Microsoft CEO Launches New Tool For Finding Government Data

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IBM Datapalooza Takes Aim At Data Scientist Shortage

IBM announced in June that it has embarked on a quest to create a million new data scientists. It will be adding about 230 of them during its Datapalooza educational event this week in San Francisco, where prospective data scientists are building their first analytics apps.

Next year, it will take its show on the road to a dozen cities around the world, including Berlin, Prague, and Tokyo.

The prospects who signed up for the three-day Datapalooza convened Nov. 11 at Galvanize, the high-tech collaboration space in the South of Market neighborhood, to attend instructional sessions, listen to data startup entrepreneurs, and use workspaces with access to IBM’s newly launched Data Science Workbench and Bluemix cloud services. Bluemix gives them access to Spark, Hadoop, IBM Analytics, and IBM Streams.

Rob Thomas, vice president of product development, IBM Analytics, said the San Francisco event is a test drive for IBM’s 2016 Datapalooza events. “We’re trying to see what works and what doesn’t before going out on the road.”

Thomas said Datapalooza attendees were building out DNA analysis systems, public sentiment analysis systems, and other big data apps.

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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.

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