Supply Chain Leaders Struggle with Unpredictability. Noodle.ai has the Cure.
Our recent Diginomica post on “Supply Chain Unpredictability” raised issues that come up repeatedly in conversations we have with supply chain leaders looking for capabilities their current planning systems aren’t providing. Their legacy ERP systems fail at analyzing system-wide volatility and recommending actions within the near-term execution window that get goods onto store shelves (e.g. increase/delay production; expedite shipments).
Now, concurrent planning systems have emerged to address some near-term planning issues, but they fail to offer a hard dollar business case. Decision-makers want help deciphering truth from hype. 85 percent of AI projects produce erroneous outcomes. Snake oil abounds. How does a buyer know who to trust? Here, we offer a list of questions (below) to ask to sort through the plethora of choices.
We may be biased, but we think Noodle.ai answers those questions in a way that those other systems don’t – problems occurring within a planner’s execution window. This is the window that SAP APO/IBP and concurrent planning systems like Kinaxis, OMP and O9 don’t address. Our products integrated with those solutions, provides a planning and execution system that maximizes fill rate, identifies sales risk and boosts planner effectiveness.
But first, how big a problem is unpredictability?
Polling Questions Reveal Unpredictability Looms Large for SAP Supply Chain Leaders
During ASUGForward, SAP America’s User Group’s recent virtual event, we took note of two poll questions for attendees and were given one (limited) snapshot of the current mindset of the SAP customer.
As noted in the Diginomica post, “…nearly 50 percent of respondents said that ‘supply chain shortages and demand unpredictability’ was their biggest concern.”
Of course, all businesses have been whipsawed to some degree by variability in their demand and supply in 2020. Our conversations with consumer products companies confirms that this is a top concern. No surprises here.
The surprise came from a second polling question, where attendees were asked about their intention to implement AI-enabled supply chain planning software:
We were surprised to see that despite the huge concerns about unpredictability, only 25 percent of respondents have or plan to implement AI-enabled supply chain planning software in the next 12 months.
The result of these responses indicates “… our industry has not done a good job of articulating and demonstrating the most effective antidote to unpredictability is implementing predictive, probabilistic planning tools that use AI.”
The Snake Oil Problem
In 2018, Gartner predicted “through 2022, 85 percent of AI projects will deliver erroneous outcomes due to bias in data, algorithms or the teams responsible for managing them…”
But why such a high failure rate? We think it’s because a lot of “AI Snake Oil” is being sold, leading to high-cost, low-impact projects. For example:
- The “Do-it-all” platform – just add… everything
- The “Toolkit.” Almost built. Just assemble at home
- The “We help you do it yourself.” @$500/hour.
- The “CPG is just like any other industry. It is easy.”
- The “Roadmap” – custom build these ten applications
To help identify vendors with value-generating AI in a landscape littered with “Snake Oil”, we’ve created a list of key questions that any company can use as they evaluate AI providers.
- Is this a project or a product?
- Can you show me your data model and accelerators?
- Do you have proven data science models?
- How long until I see value?
- What investments have you made in your people and your tech stack?
The two final questions, in particular, raise issues that come up frequently. For the question of “How long until I see value?” there is an obvious caveat pertaining to the state of your data infrastructure. But in general, we urge supply chain leaders to push vendors for a clearer definition of value and much shorter implementation time frames on their AI initiatives – and be wary of extended time to go-live.
Q: How long to realize value from AI/Machine Learning (ML)?
AI/ML has sparked everyone’s imagination around the promise of new, untapped value. However, realizing value, timely or otherwise, often remains a challenge.
So how long until one can expect to see hard value? Rule of thumb – anything longer than 90 to 120 days (3-4 months) signals that a vendor may be spending too much of their R&D time on your dime (often masked as co-development). Results range based on use case complexity.
Firstly, value measurement in the ‘counter-factual’ world of AI/ML risk predictions is a complex subject that is often treated too simplistically during the sales and budgetary processes. We advise customers to ask all vendors to explain how they would measure and attribute their product’s own true impact on supply chain drivers such as Lost Sales Recovery, Inventory Reductions and Logistic Costs.
Furthermore, AI/ML application use cases tend to be experimentation-heavy and there is no shortage of AI/ML concepts that fail to scale to production (a.k.a. proof-of-concept purgatory).
Successful value realization requires a certain repeatability of prior R&D successes. How does one quickly ascertain whether underlying data meets standards for target variable predictions to be meaningful? How does one ensure that AI/ML results are actionable for planners and operators to adopt as part of their workflow? If any of these are not identified and remediated quickly, a customer’s path to value is delayed, often indefinitely.
Q: What investments have you made in your people and your tech stack?
In order to support customers through their value journey from pilot configuration to production support, successful vendors invest heavily in product development and deployment teams spanning full-stack data scientists, data engineers, software engineers and supply chain domain specialists. Considerable development effort should already have been spent to ensure product features are generalizable and repeatable.
The best path to value: Noodle.ai + ERP and/or Concurrent Planning Tools
The skepticism about AI-Snake Oil and marketing hype is understandable. However, there are plenty of examples of organizations successfully handling the current uncertainty by using intelligent planning tools. In order to be successful, an AI/ML application vendor needs to be explicit about their target functional use case, the value impact timeframe and how they will measure it and prove it.
Get started with Athena Insights, the first step in our Supply Chain AI Suite. It often pays for itself in the very first month.
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