Probabilistic Machine Learning
Probabilistic Machine Learning
Probabilistic Machine Learning
Traditional machine learning predicts the most likely outcome in a scenario, but this often leads to poor decision making. The solution is probabilistic machine learning, which uses the likelihood of different outcomes to holistically optimise decisions.
The Purpose AI Difference...
Understanding the likelihood of different outcomes shows the risks and opportunities from under or over performance. We use this to prescribe the optimum actions to take, such as how much inventory to hold, how much to discount a product by and when to order more raw materials.
Read our Probabilistic Machine Learning Article or Book a Demo
Food Waste Reduction
Probabilistic Machine Learning
Probabilistic Machine Learning
We help food manufacturers, distributors and retailers identify food at risk of going to waste. Then we change this from a cost into a revenue generating opportunity.
The Purpose AI Difference...
Most ERP and MRP systems are not truly designed with expiry dates in mind. Our solutions augment existing planning systems with AI-driven insights to first identify, and then act on surplus raw materials and finished goods.
Promotion Optimisation
Promotion Optimisation
Promotion Optimisation
Promotions are big business. Yet many promotions still destroy value. We help Account Managers and Category Managers understand the impact of promotions on sales. Then we give them the tools to avoid bad promotions, and maximise the return on promotion spend.
The Purpose AI Difference...
Our suite of promotion optimisation tools work across the promotion lifecycle. Whether you want to weed out poor performing promotions, need a scenario planning tool for negotiations, or want to flag when live promotions need trouble-shooting, we have the solutions you need.
Markdown Optimisation
Promotion Optimisation
Promotion Optimisation
Many retail markdowns are not data-driven, or at best predict the most likely sales uplift. We use probabilistic machine learning to holistically optimise the timing and discount for retail markdowns to maximise revenue and minimise residual inventory.
The Purpose AI Difference...
The sales impact of markdowns is highly uncertain. The same discount will lead to different results when run multiple times. Our holistic optimisation with probabilistic machine learning increases revenue from excess inventory by 10-20%, while also reducing residual inventory.
Read our Grocery Markdown Optimisation Article or Book a Demo
Demand Forecasting
Inventory Optimisation
Inventory Optimisation
Most machine learning demand forecasts predict the most likely demand for a product. This discards useful information on what range of future demand should be expected, and planned for. This is where probabilistic demand forecasting can help.
The Purpose AI Difference...
By understanding the range of demand that is likely to happen, planners can make informed decisions. Where a typical demand forecast will discard demand peaks as outliers, we capture this information to optimise inventory, ordering and production decisions.
Inventory Optimisation
Inventory Optimisation
Inventory Optimisation
Some products have more stable demand than others. Some raw materials are late more than average. This information is crucial to trade-off the cost of inventory vs the risk and cost of stocking out.
The Purpose AI Difference...
Going beyond solely maintaining availability, we enable holistic optimisation. We model demand forecast variability and the likelihood of supply disruptions. Then we use this to prescribe optimum inventory levels to minimise overall supply costs.
Order Optimisation
Price Risk Management
Price Risk Management
All too often ordering is based on maintaining inventory between static min and max targets. We model the likelihood of stock-outs or excess inventory for different order dates and volumes. This equips planners for data-driven decision making.
The Purpose AI Difference...
We model the likelihood of stock-outs based on changing supply chain demand and supply risks. This is traded-off against the cost of holding additional inventory to minimise total supply costs.
Read our Raw Material Order Optimisation Article or Book a Demo
Price Risk Management
Price Risk Management
Price Risk Management
Predicting future commodity prices is not an exact science, but it is also not 50/50 whether prices will go up or down. By understanding the likelihood or different price changes, pricing managers can make data-driven decisions on when to have price exposure.
The Purpose AI Difference...
We model the likelihood of different price changes over the medium term based on both market fundamentals and historical price changes. When there is sufficient upside to justify a calculated exposure risk, it makes sense to have price exposure.
Optimising New Product Launches
Demand predictions for new products are too-often based on high-level market share calculations. This leads to lots of excess inventory being wasted when demand is over-estimated, and missed opportunities when it is under-estimated. We help companies improve their demand predictions using POS data and optimise launch decisions such as initial production quantities.
The Purpose AI Difference...
Our algorithm analyses POS data for existing brand and third-party products to model the product characteristics driving consumer demand. We use this more accurate demand prediction to optimise initial production quantity by trading-off over/under-production risks. This transforms the planning of new products from an art into a science.