Click the examples below to explore some examples of data visualisation and analytics we have developed during our projects.
Multivariate analysis on KPIs across the value-chain and through the hierarchy providing insight on the most critical KPIs, how they interact with each other and broken relationships.
Providing greater visibility of process capability through statistical analysis highlighting critical areas for management focus and process improvement.
Achieving optimum process performance is often a balancing act of multiple inputs and decisions. Simulation helps you find the correct balance and make better decisions.
Creating fresh insight and improving decision making for end-to-end business performance delivered by a combination of different business processes and organisational units with their own KPIs.
Increase change effectiveness by evaluating change capability and complexity factors.
Benchmark organisational effectiveness across multiple dimensions to Identify critical change drivers.
Improve business outcomes by understanding the correlation between behaviour and performance.
People & Change Analytics
Develop a deep understanding of the performance drivers in your organisation and the potential barriers to implementing effective change.
Rapid analysis of complex product porfolios providing categorisation, demand characteristics and price/volume relationships.
Enhance your S&OP with forecast and demand analysis matched with actual value-stream performance not theoretical capacity.
Visibility of inventory turns per portfolio category and optimum safety stocks and replenishment levels.
Supply Chain Analytics
Classic supply chain analysis made easy using direct downloads or real-time data from existing systems with no limitation on the number of SKUs, order lines or stocking locations.
Statisical analysis of OEE performance at machine level to determine improvement potential and gross capacity.
Multivariate analysis on operating parameters, performance and failures, providing prioritisation and target setting for improvement activities.
Correlating machine performance to individual operators and teams providing specific control points and training requirements to deliver consistent, best-practice levels.
Improved performance visibility with integrated statistical analysis and machine learning providing on-the-fly prioritisation, action and target setting to increase performance through-out the manufacturing environment.