With globalization, shifts in workforce demographics and the shortage of critical skills, it is no surprise that organizations are rapidly embracing a more flexible workforce. In fact, nearly 27% of an average organization’s workforce is expected to be comprised of contingent labor by 2015.
Although a flexible workforce can help organizations reduce costs, close talent gaps, and navigate change, developing a strategy around the use of contingent labor has become increasingly complex. In order to maximize the value of contingent labor and plan for future workforce needs, organizations must adopt a more systematic approach to the way they manage and engage this critical talent pool. Organizations have a lot to consider and one issue that remains a key focus is SOW-based projects and services.
It doesn’t matter how beautiful your theory is, it doesn’t matter how smart you are. If it doesn’t agree with experiment, it’s wrong. – Richard P. Feynman
We’re big fans of experimentation at IQN Labs, and not just because we consider ourselves scientists – data scientists, that is. Experiments are a great way to learn how the world works. We can use them to improve our contingent workforce management practices that, in turn, improve your business results!
Experimentation is not just for ivory tower academics. Web companies regularly undertake experiments (sometimes calling it A/B testing) to improve purchase rates or user engagement or process efficiency or customer satisfaction. They experiment because it generates good evidence about what works and what doesn’t. Experiments could also be a useful tool for figuring out what works to improve contractor hiring. They’ll allow us to move beyond best practices (which may just represent a common opinion) to evidence-based practices. Continue reading
In 2011, venture capitalist and co-developer of the Mosaic web browser Marc Andreessen proclaimed, “Software is eating the world.” He further explained, “We are in the middle of a dramatic and broad technological and economic shift in which software companies are poised to take over large swathes of the economy.” Andreessen offered multiple examples showing that in the twenty-first century every company must be a great software company, whether its industry is bookselling or oil and gas or financial services or automobiles.
No matter what industry or business function you’re in you need to use software effectively. But, in 2014, it’s not enough to just be great at software. Now data is eating the world, and you need to be great at making use of it. I’m not only talking about reporting and charting and dashboards – though those are all important and form a critical basis for more impactful uses of data. You should be using exploratory visualizations, sophisticated research designs, advanced statistical methods, and clever machine learning to really move the needle on outcomes you care about.
I’ll illustrate data’s importance by extending an example taken from Andreessen’s 2011 article: Amazon beat Borders with software. Since then, Amazon has expanded its sales to include almost anything you could think of buying, including shoes. But how can Amazon beat software-based competitors like Zappos when both are equally good at enabling shoe sales with web software? The win will go to the company that best collects and exploits data about shoe-buying to make good purchase recommendations, price footwear for optimal profits, and select the right mix of goods to sell.
Data science for contingent workforce management
Most successful companies have implemented both talent and vendor management systems for hiring and managing the best labor available. Now it’s time to use the data gathered in such systems to get even better at finding and hiring the best talent at the right time for the right price. The challenge we face in the VMS space is that advanced data analytics for talent management are in their infancy. We don’t know what will work. Continue reading
Conventional wisdom in the contingent labor industry has been to select the managed service provider first, then leverage the chosen MSP to select a vendor management system (VMS) to enable and scale the program. But does this contingent labor convention still hold when the industry is redefining itself as services procurement?
A strong case is emerging that may well turn the conventional wisdom upside down. The following is a historical look at how we got to MSP first, then a practical look at the current winds of change that suggest VMS first. Continue reading
Is management of spend the same as management of labor? No, spend is spend and labor is labor “…and never the twain shall meet.”* Right?
The truth is, it’s not that simple anymore…
Welcome to the new spend-labor split paradigm, where the traditional way of treating spend and labor the same is not wrong, but neither is it always right. As all good quandaries go, it depends.
Let’s start by explaining that the premise of this spend-labor relationship question is based in the scope of Services, as in SOW-based Services (those services acquired with and governed by a statement of work contract). Given this is Part 2 of our series on SOW-driven changes in the contingent labor** industry, we offer the new paradigm that SOW is forcing us to confront and the accompanying analysis that supports the split scenario.
Paradigm is just a fancy word for model
The Spend-Labor Split Paradigm models the relationship between Spend and Labor when procuring and consuming Services. The model depicts how category characteristics and management activities associated with Labor and Spend can vary along the Services Continuum. Awareness of this relationship, relative to the underlying purpose of the Service being procured, helps position category strategies to optimize management of both Labor and Spend.
>> View the Spend Labor Split Paradigm Infographic