People and machines working together is already rocketing companies to new highs.
This takes change and effort. But we’re here to make it effortless for you.
Build a globally defined and unified model production line. Enable model governance, in line with regulations.
We’ll help build up your team with AI skills in no time : train-the-trainer
To achieve impact with Algorithms, we need to start with rethinking how you can change the way you do business. How more and better predictions can help changing the way you serve your customers, and run your operations.
Predictions that can support or replace the decision making of your people. Through integrating these predictions into new digital applications, or into your existing system landscape.
In both scenario’s we go beyond the proof of concept and focus on AI that runs operational. Through connecting data, algorithms and applications we make your organization run smarter.
Inventory management, a critical component of the supply chain, involves ensuring that the right products are in the right place at the right time.
In this article, we will present a novel approach to Inventory Management demand prediction which incorporates overstock costs and under-order costs into the decision-making process, as a complement to machine-learning time-series modeling
While cost minimization lies at the core of this approach, supply chain managers may further customize this model to better address corporate goals and targets
Inaccuracies in high-variance fast-moving items are understandable, and near-perfect accuracy is unattainable by even the best of models, so for a company whose inventory centers around fast-moving items, even generic trend predictions have the potential to make a sizable impact, and some AI-oriented startups claim to have made progress on that front.
the forecast model with the highest possible accuracy is not necessarily that which is best for the business
On the one hand, a business must have a comprehensive understanding of the possible costs it faces, which requires deep business and domain expertise.
achieving near-optimal accuracy levels is nonetheless crucial, as a poor demand prediction model will impact all items alike
The risk-based approach to inventory management utilizes over-ordering costs and under-ordering costs in the inventory level decision-making process.
For instance, if an aircraft is sidelined due to engine failure and the airline does not have the exact engine model on hand, the airline will have no choice but to make an urgent shipment, often at a cost which may be 10-15% higher than the typical, non-urgent shipment; this is a prime example of an understock cost. Conversely, if the supplier orders too many engines which end up unused, they may be ultimately sold at a loss or written off entirely, in addition to the cost of capital incurred from ordering unnecessary items; both are considered over-ordering costs.
By combining statistical methods and the industry’s domain expertise regarding inventory costs, this system predicts the stock level which minimizes expected inventory costs.
Together with eBay, we are building the AI skills for a large and diverse group of employees to apply the power of AI and data
KPN
jointly built AI applications to optimize its network investments in DSL, fiber, and 5G.
LeasePlan
built a new AI platform and system that can value and price used cars more reliable than ever.
change is not a cool AI-project or algorithm, but rather, it is about driving breakthroughs in directions that will last for many decades and leverage the potential of AI.
We believe that to grasp the full potential of AI, a fundamental redesign of key processes is needed. This is a transformation that requires both a new way of working and new skill sets. We adopt a highly curious learning and highly practical doing mindset. Through our academy, we support our clients in building the skills required.
MIcompany aims to push the good from AI by inspiring our people and clients. AI can make our life easier, more meaningful and healthier. We inspire our people and companies to capture these opportunities by identifying and capturing new application areas for AI.
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