Purpose: The fluctuation in the price of crude oil on the global market has created a lot of attention to the researchers to investigate its price movement. This study tries to address the problem of predicting crude oil prices in a situation of unusual circumstances. Methodology: In this study, Box Jenkins methodology was used to analyze monthly dynamics of the Brent oil price from January 2002 to February 2022. Data were first differenced to achieve stationarity, and then ACF and residual diagnostics were utilized to choose models that were used for analysis Findings: The performance of various models were evaluated and ARIMA (0, 1, 1) was found to be the best model for forecasting crude oil prices. This study further reveals that despite the corona virus and the Ukraine war having a considerable impact on crude oil prices, such a model is still capable of capturing the underlying volatility in crude oil prices. Originality/Value: Oil demand suddenly decreased as a result of the corona outbreak, but then abruptly increased as a result of the conflict in Ukraine. Therefore, there is a need to update the ARIMA model in order to best predict the price of crude oil in a time of exceptional circumstances. Because of the nature of world oil market, predictions for the medium and long term are often therefore, we have limited the scope of our forecasts in this study to a single year in order to achieve the highest level of accuracy.

Due to rising global competition and major changes in consumer demands, preferences, and tastes, many businesses today use the Japanese management technique known as Kaizen to adjust their production processes to meet these expectations. The sole goal of this study was to improve the overall productivity of medium-scale industries by integrating the kaizen philosophy into the production process. Most learning exercises carried out as part of kaizen initiatives were designed to improve social outcomes regarding employees’ problem-solving skills and attitudes. In particular, Kaizen coordinators are concerned with employees’ understanding of continuous improvement and their communication and problem-solving abilities. The pilot project was carried out in the production lines where vanilla extracts were being produced. The Plan-Do-Check-Act (PDCA) cycle technique was used in this study to improve productivity in the production process. The pitch diagram was used as a supportive tool. The two prominent techniques that mainly contributed to the increase in productivity were the PDCA cycle and the pitch diagram. Kaizen implementation led to a 37% increase in the company's total productivity. To increase productivity even further, water tapping and measurement were merged. As a result, it took just 2 minutes instead of 5 to fill a bottle with 20 liters of water. This study concludes that the Kaizen philosophy and elimination of non-value-adding tasks from the production process improve output.

Crude oil is, without a doubt, one of the most significant commodities in the modern world. The highly contagious coronavirus, the conflict between Ukraine and Russia, and not to mention the unusual turn of events worldwide have all significantly impacted crude oil prices. Since oil is required for all critical economic activities, such as production and transportation, a forecast for crude oil prices is essential. Using a range of GARCH models at such an intense time, this study attempted to close this gap by forecasting crude oil volatility. To forecast the returns of Brent crude oil prices from January 2002 to February 2022, this study uses a family of GARCH models. In the respective family of models, GJRGARCH (1,1) was the most effective in predicting the volatility of crude oil prices. The GJRGARCH model was chosen since it had a higher likelihood value and a lower information criteria value. A diagnostic check was done to evaluate the produced model further to ensure that the proposed model was good enough for forecasting crude oil volatility. The study suggests employing the GJRGARCH technique to predict future fluctuations in exceptional circumstances.