Destocking, the bullwhip effect, and the credit crisis : empirical modeling of supply chain dynamics

In this paper we analyze the strong sales dip observed in the manufacturing industry at the end of 2008, following the bankruptcy of Lehman Brothers and the subsequent collapse of the financial world. We suggest that firms’ desire to retain liquidity during these times prompted a reaction characterized by the reduction of working capital, which materialized as a synchronized reduction in target inventory levels across industries. We hypothesize that such a reaction effectively acted as an endogenous shock to supply chains, ultimately resulting in the demand dynamics observed. To test this proposition we develop a system dynamics model that explicitly takes into account structural, operational, and behavioral parameters of supply chains aggregated at an echelon level. We calibrate the model for use in 4 different business units of a major chemical company in the Netherlands, all situated 4 to 5 levels upstream from consumer demands in their respective supply chains. We show that the model gives both a very good historical fit and a prediction of the sales developments during the period following the Lehman collapse. We test the model’s robustness to behavioral parameter estimation errors through sensitivity analysis, and provide a comparison with experimental studies based on the ‘beer game’. We observe that the empirical data is aligned with experimental observations regarding the underestimation of the supply pipeline

Responding to the Lehman wave : sales forecasting and supply management during the credit crisis

In this paper we analyze the strong dip in the manufacturing industry seen at the end of 2008 and provide evidence from various sources that it was caused by cumulative de-stocking, triggered by the bankruptcy of Lehman Brothers. This de-stocking created a giant dampened wave, the so-called Lehman wave. We model the Lehman Wave using system dynamics and validate the model using data from a number of business units and market segments of Royal DSM. We show that the model gives a very good prediction of sales development during the credit crisis. We provide insights into how these results can be used to improve sales forecasting and supply chain management during times of severe crises. We also show that the effects of the current financial crisis are far from over and suggest that our methods be used to predict sales during the year 2010

Port connectivity indices:an application to European RoRo shipping

In recent years, there has been significant interest in the development of connectivity indicators for ports. For short sea shipping, especially in Europe, Roll-on Roll-off (RoRo) shipping is almost equally important as container shipping. In contrast with container shipping, RoRo shipments are primarily direct, thus the measurement of its connectivity requires a different methodology. In this paper, we present a methodology for measuring the RoRo connectivity of ports and illustrate its use through an application to European RoRo shipping. We apply the methodology on data collected from 23 different RoRo shipping service providers concerning 620 unique routes connecting 148 ports. We characterize the connectivity of the ports in our sample and analyze the results. We show that in terms of RoRo connectivity, neither the number of links nor the link quality (frequency, number of competing providers, minimum number of indirect stops) strictly dominate the results of our proposed indicator. The highest ranking ports combine link quality and number. Finally, we highlight promising areas for future research based on the insights obtained

Sales forecasting during the credit crisis

The work contained herein concerns the influence of supply chain dynamics in the effects of the (ongoing) international credit crisis. This project was carried out within Royal DSM N.V, a dutch life sciences and performance materials company. During the second half of 2008, DSM sales plummeted; sales levels across the board were falling to historical lows. This was a time of financial crisis with banks going bankrupt, consumer confidence dropping and fatalistic headlines covering most of the worlds' newspapers. Nevertheless, the unprecedented drop in sales was not aligned with the fluctuations seen at the end markets: some of these were dropping, but nowhere near the levels seen at DSM. During those uncertain times, it became imperative to understand the reasons of this dip in order to plan the necessary strategic actions. De-stocking hypothesis We have analyzed a series of research leads that ultimately led us to postulate a generalized de-stocking wave as the main cause of the appearance of an amplification effect throughout the supply chain. Companies were seeing progressively decreasing sales related to their position in the supply chain, this -the bullwhip effect- has been extensively analyzed in the operations research literature. The inherent structure of supply chains, information delays and inventory dynamics are used to explain the observed effects. Modeling the supply chain To test this hypothesis we have built -in January 2009- a system dynamics model of a representative supply chain within DSM NeoResins+. This model accurately explained the historical developments of the demand and produced a forecast, predicting a W shaped curve. When, during the first half of 2009, the actual developments of demand accurately followed the model forecasts, DSM NeoResins+ used this knowledge to institute several key policies; investments were continued and stocks started to be rebuilt ahead of the market pick up. Following the NeoResins+ success story, the rest of the company was invited to join the project and have supply chain models developed specifically for each group. These additional models have proved to be accurate when explaining the historical sales developments and the trends they forecasted. Each business unit used the insights within their S&OP processes during the duration of the crisis. Insights and learnings Every business unit should know and track their end markets. Business intelligence efforts should not be limited to understanding the developments of the markets they sell directly to (e.g, resins) but they should understand and follow the markets where consumers actually buy them (e.g, Automotive). Analyzing consumer market developments with the benefit of hindsight, we can identify enough early signals that (had they been understood) would have afforded the company a strategical advantage in dealing with the advent of the crisis. Inventory dynamics have had a strong influence on the world economy as a whole. Little research is available on the subject of extending supply chain and inventory theories to the global scale and government statistics are severely lacking in this area. There is a great opportunity of untapping this potential knowledge. Investments in the area can directly lead to gaining a strong competitive advantage both in a local and global scale. Individual decisions taken by a company cannot affect the behavior of the supply chain. Communication throughout the whole chain is needed to mitigate the effects of a crisis, DSM is in a position to become leader in these strategical communications and benefit from the advanced knowledge of business developments and the subsequent increased reliability in forecasts. The supply chain models, used in stable times, can help differentiate between real business growth and stockpiling over the chain. This knowledge is critical for accurate strategic and capacity related decisions. Finally, the research efforts required of the business units during this project have effectively increased their own understanding of their supply chains and end markets. Internal awareness of the effects of these in their day to day operations have increased tenfold. Furthermore, the existence of duplicate market analysis efforts in different business units has been exposed. Many business units share final markets but do not share the data and analysis. There is a unique opportunity for the creation of synergy between sectors; not only can resources be shared but insightful discussions should be encouraged

Lehman wave shakes the chemical industry

End markets such as Construction in 2009 went down 15 percent compared to 2007. This article will provide an explanation why the sales volume of upstream suppliers to Construction markets first went down 30-50 percent, and then recovered to around original levels, and then went down again. Royal DSM together with a group of scientists from Eindhoven University of Technology have investigated this effect based on the hypothesis that de-stocking in the long value chains of the chemical industry is the cause of a significant part of the decline, and that de-stocking has been triggered by the collapse of Lehman Brothers mid September 2008. It is further based on the hypothesis that the supply chains act elastically to a strong impulse, thus creating wave-like effects

Water risk assessment in supply chains

Sustainability has long been recognized as a fundamental practice in manufacturing. In recent years, firms have been devoting resources to reduce their carbon footprint, greenhouse gas emissions, and water use. However, the problem of measuring and acting upon water risk in the supply chain has not yet been tackled in the literature. Unlike other environmental concerns, water risk is a local phenomenon that needs to be quantified at the catchment level. Thus, the impact of a production process cannot be location-agnostic and must be analyzed within its particular context—ideally at the production site level. Furthermore, recent trends in manufacturing (such as “local production”) are expected to put increased pressure in areas where regulations are lax and water risk is high (e.g., India, China). Such considerations should be taken into account within the context of supplier management processes. We introduce a hierarchical framework, using Monte Carlo Analytic Hierarchy Process (MCAHP), to aggregate relevant indicators into an index score designed to assess suppliers' water risk based on their location. Our framework distinguishes between strategic sourcing decisions and tactical supplier management. Thus, it supports two applications of particular importance for managers: the top down identification of regional-level water-risk and variability, and the bottom up supplier management at a raw-material level. We illustrate the application of our framework with a case study conducted within a business unit of Procter & Gamble (P&G), the global consumer products company; examining 1066 direct suppliers in over 75 countries. Our strategic sourcing analysis identifies 340 suppliers with high water-risk and singles out 3 countries in critical condition; experiencing high water-risk in addition to precarious conditions for civilian access to water. Additionally, our bottom up analysis identifies a single supplier of a water-intensive raw material that is expected to become critical in the coming years; thus enabling targeted supplier management from a water-stewardship perspective

Behavioral causes of the bullwhip effect: an analysis using linear control theory

It has long been recognized that the bullwhip effect in real life depends on a behavioral component. However, non-experimental research typically considers only structural causes in its analysis. In this paper, we study the impact of behavioral biases on the performance of inventory/production systems modeled through an APVIOBPCS (Automatic Pipeline, Variable Inventory, Order Based Production Control System) design using linear control theory. To explicitly model managerial behavior, we allow independent adjustments to inventory and pipeline feedback loops. We consider the biases of smoothing/over-reaction to inventory and pipeline mismatches, and the under/over-estimation of the pipeline. To quantify the performance of the system, we first develop a new procedure to determine the exact stability region of the system and we derive an asymptotic stability region that is independent of the lead time. Afterwards, we analyze the effect of different demand signals on order and inventory variations. Our findings suggest that normative policy recommendations must take demand structure explicitly into account. Finally, through extensive numerical experiments, we find that the performance of the system depends on the combination of the behavioral biases and the structure of the demand stream