JB
J.L.T. Blank
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1
Design, Development, and Implementationof a Demand Forecast Model
A case Study to Improve Cargill’s Demand Management
This thesis project studied the demand planning process of Cargill Global Edible Oil Solutions (GEOS) in the EMEA region and had the objective of developing a demand forecast model for the Retail Food Services customers to improve their satisfaction levels. The scope of this covered the commercial and contractual relationships, for the delivery of the bottle oil ready for distribution at the retail market; the procurement and production processes, as well as the Sales and Operation Planning (S&OP) process in detail. For the S&OP, Cargill relies on the Make to Forecast (MTF) production strategy, which has two main mechanisms for demand management and production planning. The first one is the use of a demand forecast and the second is the management of safety stocks. The demand forecast helps to guide the production planning to meet the upcoming weekly demand until it becomes available 7 days before the delivery time. While the safety stocks help to compensate for forecast errors and deviations, as well as for production setbacks that may occur. Unfortunately, for Cargill GEOS the automatic demand forecast has an accuracy of 42%, and the safety stock levels rose considerably and were managed on a constant level all year round to compensate. This impeded Cargill to meet the customer demand on time and in full according to the contracted service levels.
The main challenges in this situation were the limited data availability and the demand data fragmentation due to changes in the product specifications. Which required the development of specific procedures and frameworks to align the fragmented data and to obtain a weekly demand forecast. For the development of the forecast model, 3 statistical seasonal forecasting models were considered to maintain the transparency of the process, the ARIMA model, the ETS exponential smoothing, and the STL+ETS. Due to the limited data availability, the chosen model was the STL+ETS model as this was the one that was able to replicate the observed demand, with an average weighted accuracy of 76%.
The insights obtained from the seasonal forecast allowed to improve the safety stocks management, from a fixed level all year round to a seasonal management according to the expected demand and its variability throughout the year.
The solutions developed in this thesis project were not limited to the demand forecast development but they aimed to improve the demand management process in different aspects. Which will allow Cargill GEOS to improve considerably their service levels without considerable additional costs, such as the increase of the production or warehouse capacity.
...
The main challenges in this situation were the limited data availability and the demand data fragmentation due to changes in the product specifications. Which required the development of specific procedures and frameworks to align the fragmented data and to obtain a weekly demand forecast. For the development of the forecast model, 3 statistical seasonal forecasting models were considered to maintain the transparency of the process, the ARIMA model, the ETS exponential smoothing, and the STL+ETS. Due to the limited data availability, the chosen model was the STL+ETS model as this was the one that was able to replicate the observed demand, with an average weighted accuracy of 76%.
The insights obtained from the seasonal forecast allowed to improve the safety stocks management, from a fixed level all year round to a seasonal management according to the expected demand and its variability throughout the year.
The solutions developed in this thesis project were not limited to the demand forecast development but they aimed to improve the demand management process in different aspects. Which will allow Cargill GEOS to improve considerably their service levels without considerable additional costs, such as the increase of the production or warehouse capacity.
...
This thesis project studied the demand planning process of Cargill Global Edible Oil Solutions (GEOS) in the EMEA region and had the objective of developing a demand forecast model for the Retail Food Services customers to improve their satisfaction levels. The scope of this covered the commercial and contractual relationships, for the delivery of the bottle oil ready for distribution at the retail market; the procurement and production processes, as well as the Sales and Operation Planning (S&OP) process in detail. For the S&OP, Cargill relies on the Make to Forecast (MTF) production strategy, which has two main mechanisms for demand management and production planning. The first one is the use of a demand forecast and the second is the management of safety stocks. The demand forecast helps to guide the production planning to meet the upcoming weekly demand until it becomes available 7 days before the delivery time. While the safety stocks help to compensate for forecast errors and deviations, as well as for production setbacks that may occur. Unfortunately, for Cargill GEOS the automatic demand forecast has an accuracy of 42%, and the safety stock levels rose considerably and were managed on a constant level all year round to compensate. This impeded Cargill to meet the customer demand on time and in full according to the contracted service levels.
The main challenges in this situation were the limited data availability and the demand data fragmentation due to changes in the product specifications. Which required the development of specific procedures and frameworks to align the fragmented data and to obtain a weekly demand forecast. For the development of the forecast model, 3 statistical seasonal forecasting models were considered to maintain the transparency of the process, the ARIMA model, the ETS exponential smoothing, and the STL+ETS. Due to the limited data availability, the chosen model was the STL+ETS model as this was the one that was able to replicate the observed demand, with an average weighted accuracy of 76%.
The insights obtained from the seasonal forecast allowed to improve the safety stocks management, from a fixed level all year round to a seasonal management according to the expected demand and its variability throughout the year.
The solutions developed in this thesis project were not limited to the demand forecast development but they aimed to improve the demand management process in different aspects. Which will allow Cargill GEOS to improve considerably their service levels without considerable additional costs, such as the increase of the production or warehouse capacity.
The main challenges in this situation were the limited data availability and the demand data fragmentation due to changes in the product specifications. Which required the development of specific procedures and frameworks to align the fragmented data and to obtain a weekly demand forecast. For the development of the forecast model, 3 statistical seasonal forecasting models were considered to maintain the transparency of the process, the ARIMA model, the ETS exponential smoothing, and the STL+ETS. Due to the limited data availability, the chosen model was the STL+ETS model as this was the one that was able to replicate the observed demand, with an average weighted accuracy of 76%.
The insights obtained from the seasonal forecast allowed to improve the safety stocks management, from a fixed level all year round to a seasonal management according to the expected demand and its variability throughout the year.
The solutions developed in this thesis project were not limited to the demand forecast development but they aimed to improve the demand management process in different aspects. Which will allow Cargill GEOS to improve considerably their service levels without considerable additional costs, such as the increase of the production or warehouse capacity.
Household mobility post-crisis
Examining the impact of macroeconomic and institutional developments on household mobility in the Netherlands
Policy reforms in response to the crisis were initially expected to dampen future house price increases. Instead, the Dutch owner-occupied housing market has overheated not even five years after its recovery. As this could indicate a structural change in the housing demand of households, models were estimated using logistic regression analysis to examine how household mobility has changed following the crisis and position these changes in the context of macroeconomic and institutional developments. Based on the macroeconomic developments, the demand for owner-occupancy was expected to increase after the crisis. For one, the economy was gaining momentum rather than losing it, offering much better prospects for the near future. In addition, house prices were still relatively low and credit cheaper than ever, which greatly increased the accessibility of the owner-occupied sector for lower income households and offered interesting investment opportunities. Regarding institutional developments, the various reforms following the crisis are believed to cause an increase in the demand for affordable owner-occupied housing as a result of the tightened lending criteria in combination with the restricted accessibility of the social rental sector. In addition, the reforms are also expected to polarise households by widening the gap between the rental and owner-occupied sector through equity requirements. The results of the empirical analyses reveal a significant quantitative increase in demand with the intention to move nearly doubling from 21% in 2002 to 39% in 2015. The preference for homeownership itself, on the other hand, remained relatively stable. The most surprising development over time is the convergence of households from different income categories with respect to the intention to move, the preferred tenure, and desired purchase price. It is therefore believed that favourable macroeconomic conditions shortly after the crisis temporarily ameliorated the negative effects of the institutional changes. Further research is necessary to confirm whether the surge in the intention to move is merely a temporary effect of the crisis or an indication of a growing mismatch between housing demand and supply.
...
Policy reforms in response to the crisis were initially expected to dampen future house price increases. Instead, the Dutch owner-occupied housing market has overheated not even five years after its recovery. As this could indicate a structural change in the housing demand of households, models were estimated using logistic regression analysis to examine how household mobility has changed following the crisis and position these changes in the context of macroeconomic and institutional developments. Based on the macroeconomic developments, the demand for owner-occupancy was expected to increase after the crisis. For one, the economy was gaining momentum rather than losing it, offering much better prospects for the near future. In addition, house prices were still relatively low and credit cheaper than ever, which greatly increased the accessibility of the owner-occupied sector for lower income households and offered interesting investment opportunities. Regarding institutional developments, the various reforms following the crisis are believed to cause an increase in the demand for affordable owner-occupied housing as a result of the tightened lending criteria in combination with the restricted accessibility of the social rental sector. In addition, the reforms are also expected to polarise households by widening the gap between the rental and owner-occupied sector through equity requirements. The results of the empirical analyses reveal a significant quantitative increase in demand with the intention to move nearly doubling from 21% in 2002 to 39% in 2015. The preference for homeownership itself, on the other hand, remained relatively stable. The most surprising development over time is the convergence of households from different income categories with respect to the intention to move, the preferred tenure, and desired purchase price. It is therefore believed that favourable macroeconomic conditions shortly after the crisis temporarily ameliorated the negative effects of the institutional changes. Further research is necessary to confirm whether the surge in the intention to move is merely a temporary effect of the crisis or an indication of a growing mismatch between housing demand and supply.
In het basis- en voortgezet onderwijs zijn fusies aan de orde van de dag. Fusies die soms eindigen in megalomane grotestructuren. Om de fusiedrang te beteugelen, heeft het ministerie van OCW in 2011 een tamelijk bureaucratische fusietoetsingesteld. Die fusietoets wil de minister nu veranderen. Volgens Jos Blank en Thomas Niaounakis is die nieuwe toets een stap in de goede richting. Echter voor de grote risicovolle fusies is deze toets ineffectief en daardoor overbodig. Grote besturen opereren vaak al onder schaal nadelen. De minister kan beter heldere criteria opstellen om een duidelijke grens te trekken in plaats van de deur voor te grote structuren alsnog op een kier te zetten.
...
In het basis- en voortgezet onderwijs zijn fusies aan de orde van de dag. Fusies die soms eindigen in megalomane grotestructuren. Om de fusiedrang te beteugelen, heeft het ministerie van OCW in 2011 een tamelijk bureaucratische fusietoetsingesteld. Die fusietoets wil de minister nu veranderen. Volgens Jos Blank en Thomas Niaounakis is die nieuwe toets een stap in de goede richting. Echter voor de grote risicovolle fusies is deze toets ineffectief en daardoor overbodig. Grote besturen opereren vaak al onder schaal nadelen. De minister kan beter heldere criteria opstellen om een duidelijke grens te trekken in plaats van de deur voor te grote structuren alsnog op een kier te zetten.