“Whose job is it to innovate? To bring new thinking, new ideas, new methods and new tech to the team and thus, the larger organization? Who owns that for our team?”
This was the question posed to me during a visioncasting session I was leading this week. I was surprised a bit at the question because I had assumed that most people in our organization viewed part of their role as staying connected to the industry. To the trends. To the possibilities outside of our four organizational walls. To bring innovation concepts into our everyday thinking.
Back to this in a minute…
Before jumping back into the manufacturing industry, while I was working in global management consulting, I read, heard about and was told that artificial intelligence (AI) was “the future.” It would be the next iteration of data and analytics. And was often mixed with machine learning in a given sentence or industry speech. The value of AI was not being fully realized or even understood by most companies at the time. While the promise of “AI as the next phase of analytics” sounded good, the reality was much different.
Let’s get grounded in definitions:
Artiﬁcial Intelligence (AI): Any algorithms exhibiting any behavior considered “smart”
Machine Learning (ML): Algorithms that detect patterns and use them for prediction or decision making
Natural Language Processing (NLP): Algorithms that can interpret, transform and generate human language
Robotic Process Automation (RPA): Algorithms that mimic human actions to reduce repetitive, simple tasks and RPA is generally not considered a form of AI
All forms of artificial intelligence involve algorithms — sets of rules specifying how to solve a specific problem. Algorithms can be calculated by anyone with a strong match competency, but they also form the basis of most computer software. The work of algorithms in software is not visible to the human eye, but they can be programmed and re-programmed by experts to solve problems deemed important within software environments.
As technology capabilities continue to evolve, the AI value realization may finally be coming to fruition. There is no technology called AI. AI is a grouping of technologies that allow machines and systems to sense, comprehend, act and learn. Examples include virtual agents, cognitive robotics, identity analytics, recommendation systems, and text, video and speech analytics.
There are several reasons that AI has potential to grow exponentially — now more than any time in the last few years. First, computing power. The fastest computer in the world is 3x as powerful and 4x cheaper than the 2nd fastest computer built 3 year ago. High performance cloud compute processing is now available for anyone to use (~$0.50 per hour) (ref: Accenture).
Second, people and technical training programs. Skilled resources to develop AI solutions are available now more than ever — not only experienced resources across industries but also college graduates specializing in this space.
Third, the period of “big data” yielded plans and solutions that now enable AI. There are many companies that are developing products that can be quickly installed and provide near-term value in the AI space.
Fourth, the recent investments in IoT, digital and big data solutions have created new sets of information that can make AI more successful. AI relies on information to develop accurate and successful predictions.
There are many great benefits of AI. First, from an employee engagement perspective, as AI tackles lower complexity, mundane activities, employees can focus on higher value, rewarding and self-fulling tasks. This not only drives job fulfillment but long-term growth and retention as companies expand the scope of existing jobs into higher complexity and rewarding activities, make skills more transferable — less barrier to entry as AI minimizes machine, process or industry-specific activities, and match employees to more rewarding and tailored roles. Leaders in this space are demonstrating next-level analytics thinking beyond just “outsourcing to reduce costs ideas.”
Second, to craft and manage AI within an organization, new jobs, roles and skills must be developed. Required AI-specific skill sets include user experience, data analysis, computer science and statistics. In addition, new roles are needed to oversee, manage, protect and infuse ethics into AI solutions.
Third, computing power is more available to support AI. Based on growth and recent success there will be faster training, more real-time responsiveness, and ability to run/handle more complex machine learning solutions that can handle increasingly complex activities.
What about in Strategic Sourcing and Procurement?
What are the immediate and realistic opportunities that may be acted upon quickly with AI? AI is automating or improving many time-consuming tasks or giving Procurement experts additional insights based on extremely complex and large sets of data.
With the help of AI algorithms, it is possible to analyze larger amounts of data and offer the most relevant solutions for traditional problems. A few other SS&P possibilities include the following.
Data Mining, Analysis and Clustering
Data mining allows detecting unpredictable patterns hidden in the data generated during users’ routine operations for improving the level of their effectiveness. Also, Data Classification provides a source of information for determining ideal prices and make benchmarking and comparison a reality.
AI can provide companies with accurate business data and increase the transparency of costs, with a high level of automation.
Finally, Data Clustering methods allow you to combine and intelligently compare data for all benchmarking categories for the entire business or do it separately, for prices and suppliers.
Verification and Reconciliation
Verification and reconciliation algorithms can automatically determine critical errors of data and key performance indicators (KPIs), thus providing accurate information that is essential for determining the effectiveness of procurement processes, calculating valid benchmarking results, and maintaining a history of price changes, which greatly improves efficiency analysis, measuring the effectiveness of processes and can lead to significant savings.
Item Master Data and Catalogs
Some organizations have unique products that need to be available at a moment’s notice. This can put procurement professionals in tough situations as they are constantly having to negotiate different contracts with multiple suppliers for the same items to ensure availability of these products. Usually, this leaves the end user to compare multiple choices for the same product when all they care about is getting the product they need, and quickly.
A highly intelligent algorithm can automatically select or suggest the best option for the user based on things like availability, location and price of delivery. Machine learning like this promotes efficiency and makes ordering products simple.
Most organizations do not have a database containing all contract data, and they need a simple way to extract such information. Using AI, a company can view contracts faster and accumulate large amounts of data about them to significantly reduce the likelihood of disputes and increase the number of successful transactions.
The advantages of contract analytics is that it helps companies fulfill the conditions of contracts and more quickly identify cases of non-compliance.
Using machine learning algorithms to classify procurement spend into categories and sub-categories. For example, reviewing millions of invoices to automatically categorize spend in different categories of cardboard packaging. Many companies have varied methods for classifying spend – or no set method at all — AI is a perfect solution for creating a book of record in the Spend Class space.
Purchase Price Variance
Machine Learning 101 — Anomaly Detection. Using machine learning algorithms to automatically detect and surface insights relevant to Procurement is an invaluable use case for AI. For example, unexpected changes in purchase prices for a commodity or from a specific supplier.
Supplier Risk Management
Artificial intelligence can be used to monitor and identify potential risk positions across the supply chain. AI can screen many different sources and provide alerts in supply chain risk – an important factor that enriches the supplier risk assessment and scorecarding.
This is just a top-of-mind list. The possibilities extend much further in sourcing and procurement — and will be a game-changer for those that are early and aggressive adopters.
Back to the initial question: Whose job is it to innovate — to bring concepts like AI to our teams, organizations and businesses? It’s all of ours. It’s not the job of the “innovation” team. It’s not just the point leader’s job. It’s not the job of our consulting or technology partners.
It’s incumbent upon us as knowledge workers to stay up with what’s new, what’s evolving and what can be leveraged for competitive advantage — to challenge our current thinking in the pursuit of better, and the pursuit of moving the team and organization further, faster. AI is one of those concepts that is now and will be in the future a game-changer; the opportunities to leverage in sourcing and procurement are significant given the data-driven nature of the capability.
Justin Honaman is VP, strategic sourcing and procurement (data, analytics, business transformation) at Georgia-Pacific.