MDM Booster AI function
Sweet spot analysis: How companies find their best market opportunities
- there is a relevant need
- the company can deliver a particularly strong performance
- there is hardly any competition
- More targeted customer approach
- Efficient use of resources
- Stronger market position
Sweet spot finder
The Sweet Spot Finder is a strategic tool for identifying those areas in which a company can combine its expertise with specific market needs in a targeted manner – and thereby achieve a real competitive advantage.
Through the structured analysis of internal strengths, external market requirements and the competitive environment, the Sweet Spot Finder shows where the entrepreneurial leverage is greatest. The result: a clear focus for investments, product development and market cultivation – based on sound data rather than pure intuition.
For decision-makers, this means optimal decisions, greater efficiency and sustainable growth based on their own strengths.
The challenge: Without a clear focus, resources and decisions come to nothing
If a company lacks a defined sweet spot, strategic and operational uncertainties arise. The positioning on the market remains unclear – potential customers do not recognize any clear added value and the company appears interchangeable in the competitive environment.
Without a clear focus, resources are often spread across too many activities: Sales, marketing and product development operate in different directions and efficiency is significantly reduced. In manufacturing companies in particular, this type of situation often leads to overcapacity, bad investments or underutilized value creation potential.
The customer side also feels the uncertainty: offers seem arbitrary, loyalty remains low and price negotiations are often to the disadvantage of the provider.
Sweet spot analysis in retail
An online store for electronic goods would like to increase its turnover through optimized and dynamic prices. Previously, sales were forecast using classic, statistical methods and sales prices were calculated using a contribution margin calculation. Price adjustments were made 2-3 times a year – but only for selected product areas due to a lack of resources.
The challenge
- In order to maximize sales in retail, it is crucial to find the optimal price for a product. This price is where the product is neither too expensive nor too cheap – total sales should be maximized without sacrificing profits.
- The products have a variety of properties and the company sells a wide range of products
- Traditional methods only offer limited optimization options for increasing sales
Solution: AI-supported recommendations for the optimal price
An AI model, individually trained with the MDM Booster, evaluates all information on product features, the competition and current events. Optimum recommendations for the pricing of each individual item are provided in real time. Sales increase by a factor of 3.8 within one month.
The AI-supported sweet spot analysis enabled the company to improve the conversion rate, increase sales per customer and boost profits through dynamic price adjustments.If suitable product recommendations (based on collaborative filtering or neural networks, for example), the shopping cart value can be significantly increased once again.
Further examples
Purchasing and order quantity optimization
Area of application: Any industry
- Maximization and minimization functions can be used to support operational decisions in purchasing and warehousing – often with the aim of optimizing costs. AI models can be easily adapted to take account of discounts, seasonal and regional fluctuations or different types of stock, for example.
Controlling
Area of application: Budget planning
- Optimal allocation of the budget to different departments and projects so that the expected contribution to company profits is maximized or liquidity requirements are minimized (target function = maximize or minimize).
- AI-based sweet spot analysis, as a variant of classic operations research, supports you with the following tasks:
- Optimal budget decision
- Targeted use of resources
- Transparency and traceability of decisions
Logistics & warehouse management
Area of application: Supply chain management, logistics optimization
A company operates several branches and wants to find out at which locations storage centers should be set up in order to minimize transport costs – while at the same time ensuring short delivery times to the branches. This task is known as the facility location problem (location optimization)
The MDM Booster’s sweet spot finder helps you to find the optimum number and position of bearings – with the goal in mind:
to be able to reliably supply all stores
Minimize the total costs (transport + warehouse operation)
MDM Booster
Self-learning systems instead of one-off models
You don’t need any prior mathematical knowledge to use the MDM Booster. The sweet spot finder can learn from data without you having to explicitly model the relationships. For example, the AI model automatically recognizes which factors influence sales.
Optimization potentials are identified automatically. Static models work well as long as the assumptions remain unchanged. AI models are dynamic – they can be constantly improved with new data.
Automated optimizations with AI – easily and quickly integrated into your processes
Seamless integration into existing systems
Thanks to open interfaces, the MDM Booster can be easily integrated into existing MDM, ERP, PIM or CRM systems. Standard formats such as SQL, CSV, Excel, OpenAPI and S3 are supported, allowing companies to use their existing data directly for intelligent pattern analyses.
Individual AI models for customized forecasts
The MDM Booster enables the training of individual AI models, tailored to your company-specific requirements – without AI expertise. Maximize your process efficiency and exploit the full sales potential of your market.
With the AI-supported association analysis of the MDM Booster, our customers increase their efficiency every day, optimize their sales strategies and use valuable data for their long-term success.
Further use cases
Break-even point
The so-called break-even point provides information on the sales volume or turnover at which an investment becomes profitable.
By clearly separating fixed and variable costs, the analysis enables well-founded decisions to be made on pricing, production volumes or product design. Particularly in the planning phase of new products or business models, the break-even analysis can be used to identify economic risks at an early stage and calculate optimal scenarios.
With the MDM Booster’s sweet spot finder, the break-even analysis can be carried out quickly, efficiently and clearly, even for more complex structures – as a valuable basis for strategic decisions.
Tour optimization
The aim is to plan delivery or service routes in such a way that costs, time and emissions are minimized – while at the same time meeting all customer requirements.
While classic OR methods such as the Vehicle Routing Problem (VRP) mathematically calculate optimal routes based on fixed parameters, AI-supported systems such as the MDM Booster enable dynamic, adaptive route planning. They take into account real-time traffic data, weather, short-term changes or driver behavior, for example.
By combining both approaches, journey times can be reduced, capacity utilization improved and service quality increased – a real efficiency gain for logistics, field service and delivery services.
Energy consumption optimization
Optimizing energy consumption is a key lever for reducing costs and achieving sustainability goals. Modern companies rely on a combination of operations research and artificial intelligence (AI) to manage their energy use efficiently.
Operations research enables the model-based optimization of production processes, machine running times or load shifts – for example through mathematical models that minimize energy use depending on time, capacity utilization and electricity tariffs.
The MDM Booster analyses large volumes of consumption and environmental data in real time, identifies patterns and forecasts energy requirements. This makes it possible, for example, to avoid peak loads, control systems with foresight or prioritize the use of renewable energy sources.
The result: lower energy costs, greater transparency and an active contribution to CO₂ reduction – data-based and automated.
Demo date
Would you like to optimize your energy consumption, tours or cost structures based on data? We would be happy to demonstrate in a short live demo how you can use artificial intelligence to make potential immediately visible – quickly, easily and efficiently.
Contact us
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