Trax Expands Leadership Team With CRO Hire

Trax Technologies, a global innovator specializing in harnessing logistics data and insights to improve supply chain performance, announced today the company has expanded its’ leadership team with the appointment of Christopher Rajiah as the Chief Revenue Officer. Rajiah is responsible for setting and executing the company’s go-to-market strategy in order to scale the organization and solidify its position as the market leader for freight audit & payment and supply chain data management.

The executive appointment follows the additions of Elizabeth Hart as CAO and Benjamin Morens as COO in 2016. The expansion of the leadership team comes as Trax Technologies experiences significant product adoption as it transforms the freight audit and payment process to improve supply chain performance. Trax provides freight audit and payment services as a cornerstone of its cloud-based logistics performance management solution combining leading controls, supply chain data management, financial classification, and business analytics to deliver accurate, meaningful and actionable intelligence to its global customers.

“Chris’s extensive experience in successfully driving and executing global sales initiatives and growing strategic partnerships will be incredibly valuable as we continue to innovate, develop new capabilities, and extend Trax’s industry leadership,” said Don Baptiste, Trax Technologies CEO. “I’m excited to have him on our team.”

Rajiah joins from Equinix, where he served as VP of Worldwide Channel Partners and Alliances. Prior to Equinix, Chris was SVP Sales & Marketing at ViaWest, as well as the Vice President of Worldwide Partner Sales at Rackspace Hosting. Chris also spent 9 years at Extreme Networks, where he started his career, and, eventually, led their North American channel and worldwide strategic alliance teams.

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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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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.

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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).

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SenseAware is FedEx’s Internet of Things Response to Supply Chain Optimization

Supply chain visibility is critical to a company’s operational performance improvement, according to 63% of 149 responding companies in a survey conducted by Aberdeen Group.

“Visibility is a prerequisite to supply chain agility and responsiveness,” the report states.

And it requires tracking the location of a shipment not only at the transportation level, but also at a unit and item level.

Location tracking is good protection against shipment theft or loss, but companies need a deeper level of visibility for their products, according to FedEx.

The company’s solution? The IoT-inspired SenseAware, a sensor-based logistics solution.

SBL uses sensors to detect the shipment’s environmental conditions while warehoused or in transit and sends the data – via wireless communication devices – to a management software system where the data is collected, displayed, analyzed and stored.

It is “the basis of a powerful new central nervous system for the global supply chain,” according to FedEx.

The device is meant to provide intelligence that can help enterprises coordinate and manage product, information and financial flows.

Read more at SenseAware is FedEx’s Internet of Things Response to Supply Chain Optimization

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The Future Of Performance Management Is Not One-Size-Fits-All

In 2013, CEB research found that 86% of organizations had recently made significant changes to their performance management system, or were planning to. In 2014, a Deloitte survey found that 58% percent of companies surveyed did not think performance management was an effective use of time, and many media outlets jumped on the opportunity to air their grievances.

Finally, the rising wave of discontent seemed to crash in 2015, as a slew of large organizations like GE, Accenture, Netflix, and Adobe all scrapped their age-old annual performance management processes in favor of more continuous feedback systems. And many others followed suit.

But, was it the right move for everyone?

Last summer, I wrote an article on this topic myself, urging business leaders to really consider the implications of following these organizations. The issue, in my opinion, is not that these organizations did something wrong. Rather, the risk is that many leaders misinterpreted these stories to mean that they should abandon performance management altogether.

One thing is clear: the future of performance management in the American workplace is still very much in question.

For more insight into this important topic, I recently sat down with a handful of thought leaders in the performance management space, including Rob Ollander-Krane, Senior Director of Organizational Performance Effectiveness at Gap, Inc., Nigel Adams, Global Chief Talent Officer at Razorfish Global, and Amy Herrbold, Senior Director of Organizational Development at Kellogg. Together, we discussed the future of performance management to understand, from their perspective, why changes to this process are long overdue.

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How to Use Big Data to Enhance Employee Performance

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.

Read more at How to Use Big Data to Enhance Employee Performance

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Big (and Smart) Data for Digital Globalization

Data is all around us whether we use it or we are part of it. More than another trend, data is the way to move with agility and make every step and achievement tangible for those who do not see or believe it. One of the most transformational and accelerating factors of digitization is precisely how data is considered, leveraged, valued, and distilled. As data mining is not new it has become more than just a back office type of activity. It is all about turning facts into more than facts, figures into more than figures, and content into more than content.

For digital globalization practitioners and leaders, data shines like a glittering prize. That is why they face similar challenges to all business leaders when it comes to making the most of data. With the world to conquer and a number of diverse audiences to engage, they have to transform big data into smart data to focus on what enables making–and avoids breaking–the digital experiences local customers require. Specifically they must pin down the right data at the right time in the content supply chain to convert it into reliable indicators and valuable assets in the long run. In addition, due diligence is required to cover the cost and efforts of funneling, acquiring, and maintaining data. While the amount, the nature, and the scope of data depend on digital globalization targets and priorities, several categories may help establish a good base line to identify smart data and agree on a starting point for global expansion.

  1. Customer understanding data-Ranging from general (e.g. census) to segmentation data these data enable you to bear in mind what customers do at all times as prospects, decisions, buyers, or users.
  2. Usage data-As typical performance data this remains crucial in any proper mix of smart data for digital globalization.
  3. Content effectiveness data-Capturing and measuring the real impact of content on experiences is tricky and must reflect the nature and ecosystem of the content.

Read more at Big (and Smart) Data for Digital Globalization

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How visibility can drive supply chain performance

How visibility can drive supply chain performance

At its heart, supply chain management requires a balancing of operational efficiency, customer satisfaction and quality. Managing the true cost to serve each and every order is the aspiration to allow better negotiation and value creation across the supply chain. Customer and consumer centricity helps anticipate product and service requirements. But supply chains are becoming more extended and complex with a consequent increase in risk and the need for resilience. There are multiple data sources making it difficult to manage and measure end-to-end processes and metrics. Aligning priorities through integrated planning remains pivotal but there is an explosion of data available that needs to be incorporated and the value extracted to understand how supply-demand issues impact profit and revenue targets.

Organisations are looking to enable better and more consistent decision-making across complex processes with diverse systems and data. Many are leveraging business intelligence (BI) platforms to give them the capability to make decisions across the organisation, including environments where mobility and access to decision-critical information on the go is crucial. Putting the information in the hands of the people on the front line – those managing supply chain processes – is key to enabling decision making at the point of decision. But this requires synchronising an enormous amount of data that comes from many systems and sources in a way that it can be easily consumed by people who need to act on the insights.

Read more at How visibility can drive supply chain performance

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Supply Chain Impact on Shareholder Value: A Performance Paradigm?

If you ask a supply chain leader how they impact their company’s performance, the response is almost muscle memory, ‘reduce cost and inventory while improving service.’ If you ask the same leader how they impact shareholder value, there is often a long pause.

To shed some light on the subject, the APICS Supply Chain Council conducted a live poll during its jointly hosted Best of the Best S&OP Conference in June. Two poll questions were developed to examine attendee perception regarding shareholder value. Almost two-thirds of the respondents reported that they had some form of supply chain scorecard. Conversely, only three-percent reported that they linked supply chain performance to shareholder metrics.

This dialogue with supply chain leaders has sparked a number of research questions, especially in light of the fact that supply chain executives share a seat in the C-suite, including:

1. What are the key shareholder metrics that matter?

For a publicly traded company the ultimate measure is earnings per share or stock price. For privately held companies, the focus tends to be on the attributes that relate to earnings per share: growth, profit, and return.

2. What are the supply chain performance levers that intentionally add to shareholder value?

The Growth attribute is the conundrum that keeps supply chain leaders up at night. Traditionally, the assumption was that great service level, including both lead-time and reliability, didn’t lose sales and potentially helped grow share of customer’s ‘shelf space’ by having predictable availability.

3. How does that affect your supply chain strategy?

The correlation between supply chain excellence and earnings per share certainly is intuitive, but there is data to suggest that even the best supply chain companies still are not maximizing potential shareholder value.

Read more at Supply Chain Impact on Shareholder Value: A Performance Paradigm?

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