Tag Archives: Data

How to Use Big Data to Enhance Employee Performance

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Big Data has been one of the most significant and influential aspects of the Information Age as it relates to the enterprise world. Essentially, Big Data is the massive collection, indexing, mining, and implementation of information that emanates from just about any activity that can be monitored and managed electronically. Some of the uses of Big Data include: marketing intelligence, sales automation, strategizing, productivity improvement, and efficient management.

Enhancement of the workforce is one of the exciting and meaningful benefits of Big Data for the business sphere. Recently, human resource managers and analysts have been researching the implementation of Big Data as it relates to employees, and the following trends have emerged:

Employee Intelligence

For many decades, companies and organizations have tried various methods to gain knowledge about what their employees are really like. The productivity that workers can contribute to their employers is based on personal needs as they are balanced against the performance of their duties. With Big Data solutions, both personal needs and performance can be diluted into metrics for efficient analysis.

Modern workplace analytics originates from tracking employee records as well as metrics on their performance, interactions and collaboration. The idea is to focus on the right metrics to create a climate of positive engagement.

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One step ahead: How data science and supply chain management are driving the predictive enterprise

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DHL, the world’s leading logistics company, today launched its latest white paper highlighting the untapped power of data-driven insight for the supply chain. The white paper has revealed that most companies are sitting upon a goldmine of untapped supply chain data that has the ability to give organizations a competitive edge. While this wealth of supply chain data already runs the day-to-day flow of goods around the world, the white paper has revealed a small group of trailblazing companies are utilizing this data as a predictive tool for accurate forecasting.

“The predictive enterprise: Where data science meets supply chain” is a white paper by Lisa Harrington, President of the lharrington group LLC that was commissioned by DHL to identify the opportunities available to companies to anticipate and even predict the future. It encourages companies to get ahead of their business and direct their global operations accordingly.

Data mining, pattern recognition, business analytics, business intelligence and other tools are coalescing into an emerging field of supply chain data science. These new intelligent analytic capabilities are changing supply chains – from reactive operations, to proactive and ultimately predictive operating models. The implications extend far beyond just reinventing the supply chain. They will help map the blueprint for the next-generation global company – the insight-driven enterprise.

Jesse Laver, Vice President, Global Sector Development, Technology, DHL Supply Chain, said, “At DHL, we’re helping our customers get ahead of the competition by working with them to harness the wealth of data information from across their businesses, allowing us to develop smarter supply chain solutions that factor in their wider business operations. For our technology customers, we use data analytics to predict what’s going on in the supply chain, such as what products are in high demand, so we can tailor our solutions accordingly.”

While supply chain analytics technologies and tools have come a long way in the last few years, integrating them into the enterprise is still far from easy. Companies typically progress through several stages of maturity as they adopt these technologies. The descriptive supply chain stage uses information and analytics systems to capture and present data in a way that helps managers understand what is happening.

Read more at One step ahead: How data science and supply chain management are driving the predictive enterprise

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How data science and supply chain management are driving the predictive enterprise

Posted on by

DHL, the world’s leading logistics company, today launched its latest white paper highlighting the untapped power of data-driven insight for the supply chain. The white paper has revealed that most companies are sitting upon a goldmine of untapped supply chain data that has the ability to give organizations a competitive edge. While this wealth of supply chain data already runs the day-to-day flow of goods around the world, the white paper has revealed a small group of trailblazing companies are utilizing this data as a predictive tool for accurate forecasting.

“The predictive enterprise: Where data science meets supply chain” is a white paper by Lisa Harrington, President of the lharrington group LLC that was commissioned by DHL to identify the opportunities available to companies to anticipate and even predict the future. It encourages companies to get ahead of their business and direct their global operations accordingly.

Data mining, pattern recognition, business analytics, business intelligence and other tools are coalescing into an emerging field of supply chain data science. These new intelligent analytic capabilities are changing supply chains – from reactive operations, to proactive and ultimately predictive operating models. The implications extend far beyond just reinventing the supply chain. They will help map the blueprint for the next-generation global company – the insight-driven enterprise.

Jesse Laver, Vice President, Global Sector Development, Technology, DHL Supply Chain, said, “At DHL, we’re helping our customers get ahead of the competition by working with them to harness the wealth of data information from across their businesses, allowing us to develop smarter supply chain solutions that factor in their wider business operations. For our technology customers, we use data analytics to predict what’s going on in the supply chain, such as what products are in high demand, so we can tailor our solutions accordingly.”

Read more at One step ahead: How data science and supply chain management are driving the predictive enterprise

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5 Data-Driven Supply Chain Challenges to Overcome in 2016

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Supply chain, sourcing and procurement executives are feeling immense pressure to cope with the expansion into global markets, waves of disruptive innovation, rising customer expectations and complex regulatory requirements. These are catalysts that require supply chain management strategies to become bimodal and to make a shift from tactical to strategic.

In addition to the sourcing of goods and services, cost management and internal stakeholder compliance, executives’ responsibilities will include the ability to promote and support the top line. They have to be a trusted advisor to internal business partners and will have a tremendous impact on the success of an organization engaging with suppliers, managing relationships with strategic vendors and solving business problems.

For 2016, I see leading supply chain organizations making these top-five data-driven supply chain management challenges a priority.

1. Meet Rising Customer Expectations on Supply Chain Management

2. Increase Costs Efficiency in Supply Chain Management

3. Monitor and Manage Supply Chain Compliance & Risk

4. Make Supply Chain Traceability and Sustainability a Priority

5. Remain Agile and Flexible in Volatile Times and Markets

Read more at 5 Data-Driven Supply Chain Challenges to Overcome in 2016

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Big Data: The Latest Rage in Supply Chain Management

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Early uses of big data were concentrated in two areas: customer segmentation/marketing effectiveness, and financial services, particularly in trading. Recently, supply chain has become the “next big thing.”

Why? A company’s supply chain is rich with data, and it’s also a large cost component. Combined, those facts mean that advanced analytics can become a strategic weapon for optimizing the supply chain.

However, many companies can’t see the forest for the trees. They are optimizing, but not strategically. When applying data to supply chain, it’s critical to step back and look at what truly drives business value.

“They’re Digging in the Wrong Place”

As every fan of “Raiders of the Lost Ark” knows, Indiana Jones found the Ark of the Covenant first. The Germans had far greater manpower and resources and they were more efficient, but they were competently digging a hole in the wrong place. The same goes for using big data in supply chain optimization. You could have the most efficient process in the world, but if you’re making the wrong amount of the wrong product, it will hurt your business.

Read more at Big Data: The Latest Rage in Supply Chain Management

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10 ways big data is revolutionising supply chain management

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Big data is providing supplier networks with greater data accuracy, clarity, and insights, leading to more contextual intelligence shared across supply chains.

Forward-thinking manufacturers are orchestrating 80% or more of their supplier network activity outside their four walls, using big data and cloud-based technologies to get beyond the constraints of legacy enterprise resource planning (ERP) and supply chain management (SCM) systems. For manufacturers whose business models are based on rapid product lifecycles and speed, legacy ERP systems are a bottleneck. Designed for delivering order, shipment and transactional data, these systems aren’t capable of scaling to meet the challenges supply chains face today.

Choosing to compete on accuracy, speed and quality forces supplier networks to get to a level of contextual intelligence not possible with legacy ERP and SCM systems. While many companies today haven’t yet adopted big data into their supply chain operations, these ten factors taken together will be the catalyst that get many moving on their journey.

The ten ways big data is revolutionising supply chain management include:

  1. The scale, scope and depth of data supply chains are generating today is accelerating, providing ample data sets to drive contextual intelligence.
  2. Enabling more complex supplier networks that focus on knowledge sharing and collaboration as the value-add over just completing transactions.
  3. Big data and advanced analytics are being integrated into optimisation tools, demand forecasting, integrated business planning and supplier collaboration & risk analytics at a quickening pace.
  4. Big data and advanced analytics are being integrated into optimisation tools, demand forecasting, integrated business planning and supplier collaboration & risk analytics at a quickening pace.
  5. Using geoanalytics based on big data to merge and optimise delivery networks.
  6. Big data is having an impact on organizations’ reaction time to supply chain issues (41%), increased supply chain efficiency of 10% or greater (36%), and greater integration across the supply chain (36%).
  7. Embedding big data analytics in operations leads to a 4.25x improvement in order-to-cycle delivery times, and a 2.6x improvement in supply chain efficiency of 10% or greater.
  8. Greater contextual intelligence of how supply chain tactics, strategies and operations are influencing financial objectives.
  9. Traceability and recalls are by nature data-intensive, making big data’s contribution potentially significant.
  10. Increasing supplier quality from supplier audit to inbound inspection and final assembly with big data.

Read more at 10 ways big data is revolutionising supply chain management

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10 Tips For Getting Started With Global Supply Chain Risk Management Programs

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In exploring AGCO’s success with implementing a global supply chain risk management (SCRM) program, we can summarize our key recommendations to other manufacturers and services oriented companies in 10 tips:

  1. Start to engage with solution providers – Try them out, start to inflict the pain of visibility on your internal stakeholders, teach your organization to act with many blinders removed and adopt a more strategic level of thinking.
  2. Solutions are in a state of flux – Early adopters will likely have to go through radical changes in their programs as this industry matures, but this is preferable to remaining on the sidelines, getting stuck deeper in the old ways.
  3. Heuristics will make a big difference over time – Both in helping to eliminate false positives and also in identifying real issues with greater precision. Aggregated metadata from your third parties, combined with other big data sets, all processed in real time, will drive a change toward solutions that not only show what your supply base looks like but also helps manage risk scenarios and develop mitigation plans of action.
  4. A picture is worth a 1,000 conference calls – Think of a map, showing all your major internal and external business relationships (manufacturing facilities, warehouses and distribution facilities, logistical paths, suppliers and their suppliers, etc.). This simple illustration can quickly rally stakeholders around a common cause.
  5. Good SCRM analysis requires good data – Don’t skimp on the prep work. You know that sooner or later you do need to get to a clean master data management understanding, as well as item level PO analysis. You also need to fully assess your key suppliers and their immediate supply base and product lifecycles. This is a good time to start on that journey.

Read more at 10 Tips For Getting Started With Global Supply Chain Risk Management Programs 

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Financing the Supply Chain with Big Data

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To many, supply chain finance still leans primarily on approved invoices and credit. And yet, over the past 15 years, there’s been a complete transformation in the way financial processes are handled within the supply chain. Fifteen years ago, letters of credit predominated the payment interactions between buyers, suppliers and financial institutions. Financing was arduous and expensive. Today, online, cloud-based platforms are revolutionizing both payment and financing.

Data is the driver. Today, we have unprecedented visibility into all the transactions and interactions that take place in the supply chain. The cloud, as a central information hub, not only can host these interactions and provide a real-time picture of them, but it can also keep long-term records.

This gives financial institutions what they always wanted—a better way to assess risk.

Big Data Financing

Credit rating was historically the key factor for financial providers to assess risk. In many cases, it’s the buyer’s credit rating that counts most, even when the supplier is the one receiving the financing. The problem with credit rating, though, is that it depends on a lot of factors, not just on how reliable a supplier is in delivering goods or how reliable a buyer is in paying on time.

But as far as risk assessment goes, proven transaction history is what lenders prefer to set their decisions and rates upon. But for the longest time, financial providers didn’t have a good way to assess risk independently of credit rating. Now, thanks to big data, they do.

Read more at Financing the Supply Chain with Big Data

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Supply Chain Analytics: How Manufacturers Can Get The Most Value Out Of Their Automated Data Capture Technology

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There are a prolific amount of data sets and data sources available that can help overcome the aforementioned challenges. The problem is that “big data” is coming in from so many varied sources, and manufacturers simply do not know what to do with it or how to use it. The answer lies in supply chain analytics. With the right analytical tools, manufacturers can obtain actionable, meaningful, and supported insight from available data in order to make better business decisions. When it comes to AIDC, supply chain analytics are most useful in two chief areas: device optimization (the technology itself) and labor management (those who are using the technology).

AIDC Device Optimization

With supply chain analytics, manufacturers can receive timely and relevant feedback about their AIDC platform to determine how the technology is performing – feedback beyond what is provided by a typical Mobile Device Management solution. Through this insight, users can better understand the underlying causes of inefficiencies, identify areas for continuous improvement, perform predictive analysis, and more. For example, through dashboard and reporting tools, manufacturers can easily see device utilization data to determine user adoption rates. They can monitor battery performance of their devices in the field to prevent downtime. Or, they can even make sure that the right tools are available at the right time. As a result, manufacturers can optimize their mobile deployments to attain additional ROI.

Labor Management

The second component to this equation involves labor management. Using supply chain analytics, it is possible to match the right tools with the right people, and the right people with the work. Analytics platforms accomplish this by gauging and managing the labor resources that use the technology in terms of measurement of activity benchmarking, engineered labor standards, and dashboard reporting. These tools take into consideration production data (volume), integrated with labor, cost, customers, and time data.

Achieving Analytics Success

Supply chain analytics tools can provide practical and fully actionable (fact-based decision making) information to help optimize the supply chain from an AIDC and human capital standpoint. Along with the right AIDC tools and support organization behind those tools, supply chain analytics can assist in driving more revenue, reducing your cost structure and improving the experience of your customers and your workforce. Yet, this is only a piece of the supply chain analytics puzzle. Looking forward, manufacturers will continue to extend the capabilities of analytics tools to gain insight into the overall performance of the manufacturing facility. With the influx of the Internet of Things (IoT), more data points are available than ever before, which allows manufacturers to gauge the efficiency of a particular production line or overall equipment effectiveness (OEE).

Read more at Supply Chain Analytics: How Manufacturers Can Get The Most Value Out Of Their Automated Data Capture Technology

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Could Your Supply Chain Be The Weakest Link In Risk Management?

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Supply chains are a vital component of every organization’s global business operations and the backbone of today’s global economy. However, security chiefs everywhere are concerned about how open they are to an abundance of risk factors. A range of valuable and sensitive information is often shared with suppliers and, when that information is shared, direct control is lost. This leads to an increased risk of its confidentiality, integrity or availability being compromised.

Data Protection

Security is only as strong as its weakest link. Despite organizations’ best efforts to secure intellectual property and other sensitive information, limited progress has been made in effectively managing information risk in the supply chain. Too often data breaches trace back to compromised vendor credentials to access the retailer’s internal networks and supply chain. Mapping the flow of information and keeping an eye on key access points will unquestionably remain crucial to building a more resilient information.

Take a moment and think about this: Do you know if your suppliers are protecting your company’s sensitive data as diligently as you would protect it yourself? This is one obligation you can’t outsource because, in the end, it’s your liability. By looking at the structure of your supply chains, determining what information is shared and accessing the probability and impact of potential breaches, you can balance information risk management efforts across your enterprise.

Organizations need to think about the consequences of a supplier providing accidental, but harmful, access to their corporate data. Information shared in the supply chain can include intellectual property, customer-to-employee data, commercial plans or negotiations and logistics. Caution should not be confined to manufacturing or distribution partners. It should also embrace professional services suppliers, all of whom share access, often to your most valuable assets.

Read more at Could Your Supply Chain Be The Weakest Link In Risk Management?

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