Understanding business cycles

4C4CHEM UNDERSTANDING BUSINESS CYCLES IN THE UPSTREAM CHEMICAL INDUSTRY A forecast model for relevant product price based on maintenance, oil prices and GDP growth

PROJECT FACTSHEET

Large-scale maintenance activities typical to the upstream process industry explain a fair share of product price spread variance of commodity chemicals. A forecast model covering a six months horizon for relevant product price spreads based on planned maintenance activities, historical oil prices and GDP growth is created. A System Dynamics model is applied to a hydrocarbon supply chain providing a volume forecast feature. Recommendations for further applications of the model are given.

Problem description Crackers and subsequent production units are operated in a strong push manner from upstream towards downstream echelons with little knowledge of the supply chain behaviour and consideration of end market demand. The impact of own and competition’s facility outages on business cycles and prices is not fully understood. Likewise, the relationship between business cycles and commodity pricing as well as feedstock prices is not fully understood or incorporated into planning decisions.

Observed demand shows significantly higher volatility following the Lehman Shock and price sensitivity of customers has increased and is reflected in order patterns. In case of excess production, prices have to be lowered considerably to “push” the product into the market thus eroding margins and partake in next period’s demand. Moreover, since the Lehman Shock demand forecast quality has decreased substantially. Incorrect planning and operating decisions can lead to disadvantageous purchases, sales and contracting caused by prevention of bottleneck starving or blocking.

Solution methodology This work is based on two conceptually independent models. A Maintenance-Price Regression Model and a Basic Supply Chain Model based on System Dynamics. Structure and findings of both models are then combined in an Advanced Supply Chain Model.

Case study/Implementation This study investigates structural and dynamic reasons for high fluctuation in price and demand observed in the upstream plastics supply chain in Europe. The work covers a time span of eight years (2005 to 2012) covering the disrupting and severe effect of the financial crisis triggered by the Lehman bankruptcy in September 2008 and leading to a recessive phase with long-lasting weak demand in Europe. Supply chains have been exposed to a synchronized destocking effect coined the “Lehman Wave”. Figure 1 depicts the elements in scope. The polymers discussed are HDPE, LDPE and LLDPE. All data used is aggregated to a European industry level.

Figure 1 - Supply chain of plastics in scope

Two models have been developed to address the problem: a) a price spread forecast model based on multiple linear regression analysis and b) a System Dynamics supply chain model including a forecast feature for ethylene production and polyethylene inventory levels (see Figure 2).

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Maintenance-Price Model Since 2009, the extent of planned maintenance activities such as cracker-turnarounds has an effect on commodity prices detectable in spreads. The extent of maintenance explains around 25% of the observed variance in Figure 2 - Model features ethylene-naphtha and polyethylenenaphtha spreads. Due to low deviation from the published schedules, the quantified effect is suitable for forecasting. Figure 3 shows the ²-values for a regression model utilizing the planned maintenance extent, crude oil price (respectively its lags) and GDP growth.

Supply Chain Model Studies conducted in the industry showed that the distinct drop in production, known as Lehman Wave, was caused by synchronized de-stocking throughout the value chain (Udenio, et al., 2012). A System Dynamics model previously applied to the industry (Corbijn, 2013) is extended to the ethylene producer echelon and end markets characterized by application (e.g. food, construction) rather than production technique (e.g. blow molding). The model is Figure 3 - Explanatory power of the regression model calibrated against data of industry reports but eventually solely reacts on end-market demand, the only input. Outputs of the model show an ²-value of .814 for the ethylene production from 2007-2012 (see Figure 4). The forecast feature of the model is able to capture upcoming rampups and overshoots but requires interpretation and is prone to miss inter-month shifts due to the interpolation of monthly input data.

Results/Managerial insights Both models have in common that the Lehman Shock marked the beginning of a new period characterized by instability and nervousness. Only since 2009 price spreads react on large-scale maintenance activities. Likewise, the effect of price on order size has Figure 4 - Supply chain model increased. On the other hand, a model only fit taken into account volumes and excluding price can explain a great deal of the variance in ethylene production and polyethylene stock levels. This leads to the conclusion that price alone cannot be a proxy for demand but has to be seen in combination with volume.

This fact sheet was produced at part of the project 4C4CHEM - Cross chain collaboration in the Chemical Industry. The work reported on in this factsheet was conducted by David Brandstädter, as part of his Master Thesis work at Eindhoven University of Technology.

Partners in 4C4CHEM are: Shell Chemicals, Dow Chemical, SABIC Chemicals, Den Hartogh Logistics, Cargogator, and Eindhoven University of Technology. The project is co-funded by DINALOG, the Dutch Institute for Advanced Logistics.

Interested to receive more information? Please contact Professor Jan Fransoo, 4C4CHEM project leader at j.c.fransoo@tue.nl

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