Food and Speciality Chemicals

Research and Development (R&D)

  • Descriptive statistics like mean, median, mode, and distribution  are useful for the  quality control, process monitoring, and data exploration.
  • Regression models to predict product quality attributes based on process variables, optimise formulations, and assess consumer acceptance.
  • Survival analysis for time-to-event data is a tool to provide insights into product stability, shelf-life estimation, and consumer safety.
  • Design of experiments (DoE) for optimising food and chemical processing parameters and studying their effect on product quality and process yield.
  • Sensory analysis plays a vital role in evaluating food products. Statistical tools like analysis of variance (ANOVA), discrimination testing (e.g., triangle tests, duo-trio tests), and descriptive analysis (e.g., profiling, check-all-that-apply) are helpful for analysing sensory data to understand consumer preference, product development, and sensory quality assessment.

Manufacturing

  • Enhance your food and chemical manufacturing process efficiency using real-time monitoring and apply Lean Manufacturing and Six Sigma.
  • Process control charts (Xbar-R, Exponentially Weighted Moving Average {EWMA} etc.) to monitor chemical and food process manufacturing for detecting process shifts that lead to advanced quality and better product output.
  • Data analytics to optimise supply chain management including demand forecasting and streamline logistics in a fast-paced and complex chemical and food business.
  • Reduces equipment downtime and extends the lifetime by analysing historical and real-time data from your process to predict patterns that precede failure and schedule maintenance appropriately.

Marketing

  • In-depth customer behaviour understanding includes segment group analysis, lifetime value, and sentiment analysis by diverse statistical tools like Pareto distribution, negative binomial distribution, K means clustering etc.
  • Predicting the product’s sales trends and demand forecasting using time series analysis and regression techniques like Linear, Bayesian, and Autoregressive integrated moving average (ARIMA).
  • The concept of big data has an essential role in R&D analytics, market gap identification and feedback analysis for new food or chemical product development.
  • Take advantage of the transformative role of data analytics in supply chain management,inventory and logistics optimization.
  • Perform A/B testing and customer journey analysis using binary logistic regression for very effective marketing campaigns and customer behaviour.

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