Problem

• Running out of production capacity, significant CapEx investment needed in the new production line to keep up with demand
• Goal: increase the production capacity by 5% via advanced analytics

Solution

• Data integration, AWS platform
• Anomaly detection algorithms
• Data correlations -> insights -> recommendations

Impact

• Trending towards 11% of productivity increase (throughput and yield optimization)
• 15x ROI

Developing a state-of-the-art forecasting approach by combining multiple forecasting models to achieve a 97-98% accuracy, leading to monetary and time savings

Problem

• The customer was looking to automate the sales forecasting process which to date was performed in a manual way, making it cumbersome and error prone

Solution

• New approach to improve forecasting quality and the effort needed to maintain it using new data sources like Forward looking indicators, Financial metrics: (GDP, Inflation, FX rates) and Big events (e.g. new years in china, olympic games etc.)

Impact

• Automation -> less manual work, less errors, less time
• Higher accuracy (97-98%)
• Better planning/cost savings

Developing a state-of-the-art forecasting approach by combining multiple forecasting models to achieve a 97-98% accuracy, leading to monetary and time savings

Challenge

• Needed to accurately forecast sales, raw materials supply and market demand for finished goods
• Existing forecasting methods were labour-intensive and inconsistent
• Models failing to provide accurate, reliable forecasts

Solution

• Created powerful new methods to generate and combine forecasting models
• Developed modern, state-of-the-art forecasting algorithm
• Delivered an automated solution which aggregated data sources and combined forecasting models together to attain higher accuracy

Business Impact

• Reached 97-98% in forecasting accuracy through combination of forecasting model, higher than any single model on its own
• Enabled an optimisation of operations, resulting in monetary and time savings
• Decreased risk of manual errors through forecasting automation

Creating custom statistical algorithm to define optimal “logistic bases” for formulas in order to minimize the amount of pouring and reduce costs

Challenge

• Ingredients need to be mixed at certain ratios to create flavours. Some ingredients are pre-mixed into what is called “logistic bases” to reduce the number of pours
• Choice of logistic bases by human expert is becoming harder
• Goal to find optimal logistic bases through an algorithm to increase pouring efficiency

Solution

• Created custom statistical algorithm that incorporates information about the creation process and historical production data
• Solution was however not implemented because deemed too risk to change the whole production process

Business impact

• Theoretically, the new logistic bases created through our algorithm would reduce the number of pours by 10%
• Would lead to a cost reduction after break-even with initial production process change investment

Analysing a production line to improve production speed and anticipate slow-downs resulting in 15% speed-up with a 8x ROI

Challenge

• Perfume production plant efficiency was very variable and production encountered several slow-downs

Solution

• Conducted initial data analysis and discovered that slow-downs were caused by some valves being clogged
• Cleaning the valves lead to overall equipment effectiveness (OEE) improvements
• Built custom application to monitor KPIs in real time and alert operators about undetected issues

Business impact

• Increased production line efficiency and speed
• OEE increased by 8%, speed-up of production by 15%
• 8x return on investment

Defining an mathematically optimal manufacturing sequence order to reduce total production time of perfumes

Challenge

• Manufacturing line produces perfumes by blending ingredients
• Sequence order can be optimised to reduce time and complexity of production
• Goal to compute optimal scheduling to pour ingredients for each blend
• Past factory data was scarce and implementation initially seemed complicated

Solution

• Implemented mathematical optimisation algorithm that defined optimal production sequence to manufacture perfumes
• Simulation to test impact of different optimisation methods compared to what happens in the factory
• Chose “Iterated Greedy” method

Business impact

• Faster manufacturing due to mathematically optimal production sequence
• Approx. 3% improvement in manufacturing speed
• Resulting in 6 digits cost saving figures

Problem

• The need to improve the current forecasting process (raw-materials, finished goods and sales)

Solution

• New approach to improve forecasting quality and the effort needed to maintain it using new data sources like Forward looking indicators, Financial metrics: (GDP, Inflation, FX rates) and Big events (e.g. new years in china, olympic games etc.)

Impact

• Automation -> less manual work, less errors, less time
• Higher accuracy (97-98%)
• Better planning/cost savings

Problem

• The customer’s production line went through a data driven optimization, the customer wants to ensure the production line maintains the highest possible OEE and wants to add advanced features like automated issue detection & prediction

Solution

• Near real time continuous monitoring system of key production processes with advanced analytics built in (alerting, trend detection, anomaly detection, predictive maintenance)

Impact

• Greater production process visibility
• Quicker time to resolution
• Improved uptime

Problem

• Customer struggled with hard to detect issues in manufacturing line that slowed down production

Solution

• Conducted a data driven production line optimization
• Created a real time monitoring system of the factory with anomaly detection algorithms and alerting system built in

Impact

• 15% increase in production speed (throughput)
• 8x ROI