Business relationships only work when all parties benefit.
Predictive Procurement is crucial to solving a wide range of complex supply chain problems efficiently and effectively. Procurement teams that successfully align with their key suppliers have seen improved quality, reliability, cost savings, and other critical deliverables for evaluating supplier performance.
Arkestro is explicitly engineered to drive impact on all of these dimensions of procurement’s value. It operates as a recommendation engine that learns from your organization’s data (and best supplier selection decisions) to eliminate manual validation, approval, and data-entry steps across the entire procurement cycle.
Procurement’s Biggest Challenges Today
As organizations across the globe continue to adjust to the ever-changing post-COVID business landscape, more than ever, procurement’s function is expanding beyond its traditional cost-reduction and supplier-management roles.
CPOs are looking to solve age-old procurement problems, most of which center around the high cost of delaying a transaction in a volatile, short, or challenging set of market conditions.
Large enterprises are perpetually dealing with challenges from siloed data and inadequate staffing to touch as much spend as they’d like with the resources they have available. This problem has gotten much worse as the amount of data has increased over time.
To be the strategic partners the business needs, procurement teams today require more and are demanding more. They are looking to integrate tools that reduce the organization’s risk exposure and leverage always-on validated data for better and faster decision-making at scale, across every category of spend.
Procurement teams want to expand their value propositions to drive innovation, influence demand, and create continual operational improvements. They want to work closely with strategic partners and suppliers to drive continuous improvement, speed up M&A integration, accelerate speed to market, and drive commercial compliance.
To do this, procurement needs a lot of validated data. The data in question needs to be well-structured and useful for the internal organization, external markets, and suppliers. It also needs to work within its existing tech stack—whether that includes a monolithic ERP, Procure-to-Pay system, or what Gartner calls “composable” procurement: a hybrid of suites and best-of-breed apps. And if they hope to join the league of high performers, they need to do this while delivering measurable improvements to numbers that show up on financial statements, full stop.
With the ever-rising levels of supply chain complexity, procurement teams must adopt digital technologies to deal with it. They need to implement procurement tech that:
- Offers predictions on real-time price information
- Provides appropriate supplier recommendations
- Makes it easy for suppliers to offer and agree to price and commercial terms
- Integrates with other technologies to provide the right data at the right time
- Provides supplier risk, and ESG information, including supplier diversity
The good news is that Akrestro does all of this, and it’s embedded within systems you already use, so there’s no dependency on training, user adoption, or supplier acceptance. In fact, most users of Arkestro do not even realize they are using an AI/ML technology: they simply feel like they are getting a faster and easier procurement experience.
If you have any of these problems at your organization, you’re probably wondering: How does Arkestro work, and what can it do for me? Here’s everything you need to know.
Procurement Is More Than Cost Savings
Measuring the efficiency of your procurement processes plays a crucial role in the supply chain, particularly in the post-pandemic era. It applies across the board, whether the economic downturn hits your business or not.
If you can’t measure it, you can’t manage it, and you certainly can’t improve it. The single most effective way to improve procurement efficiency is by predicting the outcome before it happens. That’s where AI/ML models can come in handy.
It’s impossible to talk about procurement performance without talking about cost savings. However, we all know that procurement’s value is much more than that. Especially following COVID-era supply chain disruptions, CPOs and CFOs know that savings are no longer enough to keep the procurement team moving in the right direction.
The 2021 Deloitte CPO Survey findings reveal that high-performing procurement teams use a more comprehensive range of KPIs than cost savings. These performance indicators have enabled them to withstand supply chain disruptions, remove barriers, and speed up their processes.
Some of these KPIs include
- Value creation and cost avoidance
- Return on investment per procurement headcount
- Cycle time per Purchase Order and/or Sourcing Event
- Internal stakeholder satisfaction, often Net Promoter Score
- Supplier performance (quality, delivery, and innovation)
- Spend Under Model (% of annual spend associated with a prediction)
Leading enterprises move numbers on these KPIs and cost savings by leveraging Arkestro’s Predictive Procurement Orchestration platform. Under the hood, Arkestro is running highly performant machine learning models and embedding game theory and behavioral science into procurement cycles to help procurement influence more spend faster.
Arkestro is engineered to amplify the impact of high-performing procurement teams by providing a 2–5x lift on speed and impact associated with everyday sourcing and purchasing cycles. It can also help align more spend with preferred suppliers, including every definition of “preferred,” such as ESG and supplier diversity.
CPO of Box, Linda Chuan, discusses how Arkestro helps them find diverse suppliers:
“With Arkestro, we are able to evaluate and select suppliers based on fine-grained diversity data — giving us the best opportunity to not only expand our pool of suppliers, but to do so with inclusion and equity in mind.”
The right procurement platform makes it easy to align KPIs with your business goals and track performance as far as quality, delivery, and cost savings go.
Arkestro Enhances Procurement Processes
For the longest time, procurement teams were frustrated with the amount of time, effort, and resources they spent collecting quotes from different suppliers before comparing them side-by-side to evaluate them. The go-to tool at the time was Excel.
Regardless of how “sophisticated” the software they were using was, they still had to bring on a category expert. The role of these experts was to identify potential issues with supplier submissions before they could adequately evaluate an award scenario.
Arkestro was born out of these common frustrations. Initially, the software was developed to reduce the burden of sourcing events. It has since evolved to be so much more. The platform leverages machine learning and other procurement AI technologies for predictive pricing and the generation of real-time recommendations. The result: faster and more efficient procurement cycles.
Jean-Michel Dos Remedios, VP of Strategic Sourcing at Bel Brands USA, highlights how Arkestro saves them time:
“In the procurement world, time is of the essence. Arkestro’s innovations make us faster and the faster we get, the more we can accomplish, and the more successful we can be.”
Arkestro helps procurement teams reduce time spent on manual analysis and data entry tasks with no login, training, or dependency on user adoption to create tangible business impact. Organizations using it have seen improved data quality and dramatic cost savings impact within days of implementation.
How Does Arkestro Work?
Arkestro’s Predictive Procurement Orchestration (PPO) platform operates as an embedded platform within existing ERP and P2P infrastructure. PPO influences all addressable spend and is category agnostic, leveraging a novel approach to predictive pricing. Customers can see how it works by back-testing the model (e.g., running predictions on purchases that have already taken place).
With a combination of machine learning, human behavior science, and game theory, Arkestro pulls in your company’s internal data, including your purchase orders, supplier master, category data, and contracts data. It also pulls external data, including supplier information, ESG, and diversity datasets, and then cleanses and validates the data.
Arkestro does this using three broad motions: Simulate, Send, Select.
- Simulate: Arkestro simulates a sourcing or purchasing event to predict prices and terms.
- Send: Akrestro emails a recommended offer to the supplier(s) for review, revision, and acceptance.
- Select: Arkestro recommends holistic awards and approvals using many possible scenarios. Awards can be for a single Purchase Order or a complex Bill of Materials. Awards reflect an organization’s intent and are based on KPIs and the the unique purchasing and pricing history of the line items involved. Arkestro’s award recommendations can be tweaked or manually overridden, so the procurement team is always in the driver’s seat.
What customers end up with are tangible business results, including
- Quicker and more impactful supplier engagement during sourcing and spot buys
- Greater amount of spend actively influenced using predictive models
- Increased cost savings and/or cost avoidance across more spend
- Better and faster quality decision-making across the organization
- Incorporation of supplier ESG, diversity, and risk metrics into spend decisions
A Simple Solution for High Performing Teams
Procurement teams today have a wide range of complex and ever-changing requirements. They need technology that’s not only capable of removing this complexity but also offers more situations to win, all while providing strategic value.
Arkestro does just that. Predictive Procurement delivers tangible business impact without organizations having to re-do their entire tech stack to be more strategic or integrate the necessary tools. With Arkestro top enterprises influence more spend faster, and become their suppliers’ customer of choice.
Want to see Arkestro in action? Get in touch with us today.