Engineering a data-rich workflow for the modern supply chain planner
We’d all love to have business apps at work which are as engaging as the apps on our phone. Sadly, most of the software we use at work is disjointed, has poor usability, and leaves a large amount of tedious work to be done by the user. This means we find our supply chain workforce has fallen out of flow and is rampantly disengaged.
Signs you are in a data-driven flow state:
- You have clear goals and immediate feedback loops
- Your work experience is intrinsically rewarding
- You have found a balance between challenge and skill
- You have a feeling of control over outcomes
- Your action and awareness are merged, nullifying distractions
Signs your flow state is being blocked by a data-poor work environment:
- Frequently having to switch between systems/screens to find the necessary information, creating a “swivel chair” effect
- A constant flurry of notifications and distractions which block the focus you need to engage in the deep work your job calls for
- Not having all the information needed to come to a decision, or not understanding the uncertainty behind your information
- Waiting for inputs from others in decision processes which require a team effort
- Poor asynchronous communication resources for global teams where you don’t have synchronous or face to face communication
- Feeling a lack of ownership over your work, resulting in a bystander effect where the buck is passed to others in the organization
The digital transformation of the past couple of decades has been instrumental in helping businesses become more objective and data driven. The ERPs, dashboards, reports, spreadsheets, and KPIs the modern supply chain planner works with are indispensable for making better business decisions. However, these deterministic systems only take you so far.
The real world contains sources of variability which conventional software doesn’t know how to handle. For example, a demand signal which is a function of the latest social trends or a transportation lead time which is subject to changing conditions such as weather. These sources of variability lie beyond the scope of ERP systems and require humans that have years of domain experience with similar situations to make judgement calls.
One difficulty that a modern supply chain planner* has is this need to take all the experience and information in their head, apply your intuition, consult teammates, and speculate what the future may look like given a set of possible decisions. You are tasked with extrapolating into the future in the face of uncertainty. This is exhausting and incredibly inefficient. What if you had an app which ‘talked’ to all of your different IT systems, learned from you what expertise is important in certain situations, and used finely tuned predictive algorithms to show you what the future may look like for each of your possible response actions?
Our mission is to empower you with our AI systems. Our systems reduce wasted time, costs, and resources while optimizing your flow, which empowers you to make better business decisions.
A good configuration phase starts with a critical business decision in mind – something which cuts through the cacophony of chaotic organizations and provides a north star for supply chain planners to follow. We begin by having our enterprise services team work with your subject matter experts to understand the resource-intensive thought processes which go into your decisions. Once we understand which real-world business problems we’re solving with you to help you make your KPIs, we provide your planners with a suite of apps–apps that give you the power of efficient, data-optimized decisions, faster. Your planners can then activate the apps that work hardest to improve flow, quickly leveraging the power of AI to create a supply chain that is always learning.
*’Supply chain planner’ encompasses: materials planners, production/resource planners, deployment planners/brand supply managers, and demand planners.
- Introduction to MLflow for MLOps Part 3: Database Tracking, Minio Artifact Storage, and Registry
- Believe the Hype: Gartner, Noodle.ai, and Decision Intelligence
- Introduction to MLflow for MLOps Part 2: Docker Environment
- Atlas, Noodle.ai’s Machine Learning (ML) Framework Part 3: Using Recipes to Build a ML Pipeline
- Supply Chain Leaders Struggle with Unpredictability. Noodle.ai has the Cure.