Tag Archives: big

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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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.

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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.

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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.

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