What are the three issues in an integrated supply chain that can lead to distortion of information and create the bull whip effect?

Future uncertainties

Uncertainty is a state of having limited knowledge about present or future outcomes. It may manifest itself in varied dimensions, from affecting macro-economic stability to intra-organisational dynamisms. With dynamic business environments and multiple variables, organisations around the world encounter the challenge to recognise uncertainty and take remedial measures on time, every time. During a conversation with the British Prime Minister on how to make society more robust (that is, more tolerant of unexpected events), it was noted that that we are living in a world that is extremely different from that we inherited from the past (Taleb 2009). Now rumours are global and we are much more vulnerable to extreme deviations. It was also indicated that globalisation has made companies very efficient, yet very fragile (Taleb 2009). With the ever increasing use of IT, the speed of interactions and reactions has become remarkably fast-paced. Due to the tools we have in our hands, we can no longer make the same mistakes that we have done in the past (Taleb 2009). In other words, the frequency of uncertainties in the business environment have increased manifold with an ever increasing need to carefully look once more into the core components of business sustainability in order for an organisation to stay sustainable during the karmic phase and beyond.

Much uncertainty is not introduced by the marketplace, but is rather system induced, that is, it germinates within the organisation due to the various internal dynamics it has within its processes, policies and practices. It is then magnified by the ‘bullwhip effect’ (a term indicating the way the amplitude of a whip increases down its length). This concept of amplification is aptly observed in supply chains whereby unpredictable elements introduced by human behaviour in the lower part of the chain become more pronounced the higher up the chain they move (Lee et al. 1997). This effect is important because it is frequently the cause of serious inefficiencies that result from ordering too much or too little of a given product as links in the chain over-react to changes further downstream (Baugher 2010: 1).

Hence, the best way to cope with uncertainty is to work hard to reduce it (Jones and Towill 2000). Those organisations who understand the principles of uncertainty and act proactively to cope with it, often survive and sustain better than the rest. For instance, as noted by Mr Sim Kah Bin, Logistics Department, SE Net Fashion Development Pte Ltd, Singapore, some businesses fail to unlearn what they have learnt and do not know how to relearn (SHRI 2009).

A business cannot prosper over the long term without the capacity to manage risks and uncertainties. It will stumble from crisis to crisis, but it will not survive and it will fail. Risk and uncertainty have real impacts on earnings, cash flow and shareholder value. They cut across all that a business must do in order to succeed (Csiszar 2008: 3). However, though uncertainty is a phenomenon experienced by all businesses, its magnitude may vary across industries and over time.

How then can businesses reduce uncertainty and thrive over time? In times of crisis, panic sets in and more often than not businesses tend to ignore the fundamentals. There are a few elements which are fundamental and work in most situations to reduce uncertainty. First, there is the regular analysis of what is working and what is not. Second, assessing new ways to control the quality and price of products and services is needed. Third, creating a ‘win-win’ proposition for all stakeholders is commonplace. The success of the above elements depends in turn mostly on senior management’s clarity of thought and vision, ability to take unbiased decisions in both good or difficult times and the willingness of the organisation to undergo Schumpeterian ‘creative destruction’ (Schumpeter 1942). The following section highlights examples of some innovative business sustainability initiatives implemented by organisations in various parts of the world.

4.2 Bullwhip effect

The bullwhip effect is an important phenomenon in supply chain management that has been studied for about fifty years. It was explained by Stevenson (2007) that the demand variations of the customer become increasingly large when they diffuse backwards through the chain. The bullwhip effect was first introduced by Forrester (1958). He observed a fluctuation and amplification of demand from the downstream to the upstream of the supply chain. He stated that the variance of the customer demand increases at each step of the supply chain (customer, retailer, distributor, producer and supplier). Furthermore, he concluded that the main cause of this amplification is the difficulties in the information sharing between each actor of the supply chain.

Including Forrester's approach, several authors analyze the sources of bullwhip errors and the factors to control the bullwhip effect. Lee et al. (1997) present the main sources of bullwhip effect such as demand forecast, order batching, price fluctuation and gaming principle. Wang et al. (2008) conclude that lead time, market sensitivity and resource allocations in supply chains can cause bullwhip effect.

Geary et al. (2006) review the literature on bullwhip effect and analyze the previous approaches and conclude that the main cause of bullwhip errors is poor material flow. Wamba et al. (2008a) indicate that controlling the bullwhip effect can optimize material resources by decreasing unnecessary locations or safety stocks along the supply chains. Metters (1997) quantifies the bullwhip effect in supply chain by comparing the effects of increased demand seasonality and forecast error of demand distortion. They show that eliminating the bullwhip effect can increase profits by an average of 15–30%.

Information sharing is indicated as one of the main factors to control the bullwhip effect. Chen et al. (2000) develop an analytical approach in order to evaluate the impacts of information sharing between supply chain actors on the bullwhip effect. Holweg et al. (2005) also indicate that supply chain collaboration and the visibility of information flow can reduce the bullwhip effect that improves service quality, decreases inventory levels and reduces stockouts.

Several authors conclude that Auto-ID technologies such as RFID can reduce the bullwhip effect and improve supply chain performance. Bottani and Rizzi (2008) indicate that an automated information system can improve the inventory visibility that can thus reduce safety stocks and the bullwhip effect. Wang et al. (2008) conclude that RFID integrations into supply chains can reduce bullwhip effect and improve inventory replenishment management performance. Imburgia (2006) indicates that RFID technologies can prevent the bullwhip effect through more accurate forecasting. Zaharudin et al. (2006) indicate that Auto-ID technologies can reduce the bullwhip effect through information sharing between all supply chain actors by accessing information in a single way. Saygin et al. (2007) conclude that RFID can reduce the bullwhip effect by a better visibility obtained through real-time information of items and locations. However, they highlight that having too much visibility is equivalent to having no visibility because having a lot of unusable data can worsen supply chain performance.

Numerous authors analyze the bullwhip effect. A short list of the publications is given in Table 3.

Table 3. List of publications on the bullwhip effect.

AuthorsYearMain topicBuffa and Miller (1979)1979Bullwhip effect in planning and control systemsSterman (1989)1989Beer game: an effective method to understand the bullwhip effectde Kok and Shang (2007)2007Philips Semiconductor bullwhip effectsYucesan (2007)2007Main sources of bullwhip effectHuang et al. (2003)2003Impacts of information sharingChoi et al. (2008)2008The importance of information sharing in a virtual enterprise chainEmerson et al. (2009)2009The information sharing in a dynamic supply chainZhou (2009)2009Benefits of RFID information visibility using a manufacturing exampleAgrawal et al. (2009)2009Impact of information sharing and lead time on the bullwhip effect

Buffa and Miller (1979) deal with the bullwhip effect in planning and control. Sterman (1989) describe an effective method to understand the bullwhip effect named as “beer game”. It is a useful teaching tool where each participant represents an actor of a beer supply chain such as retailer, wholesaler, distributor and manufacturer. This game has been played many times by numerous students, professionals and managers. Every time, the same results are obtained; a small change in a consumer demand is translated into considerable fluctuation in both orders and inventory upstream. This fluctuation is caused by the lack of information sharing among the entire chain.

de Kok and Shang (2007) present a study of Philips Semiconductor bullwhip effects. In 1999, Philips conducted a project on bullwhip effects in some of its supply chains and developed a collaborative-planning tool to reduce inventory and increase customer service levels. The results of this project show important savings; minimum yearly savings of around US $5 million is from $300 million yearly turnover. This study presents an insight into complex stochastic problems, such as multi-item multi-level inventory control.

More recently, Yucesan (2007) writes that the main cause of the bullwhip phenomenon is the deficiency in information sharing, communication, and collaboration throughout the supply chain that causes information failure as well as delays in information and material flows. Huang et al. (2003) review the literature of the impacts of shared information on supply chain dynamics. They also discuss how to share information (information, time, people, format, etc.) to maximize the benefits for supply chains. According to them, more shared information leads to more efficient decisions on ordering, on capacity allocation and on production planning for each supply chain actor.

Choi et al. (2008) focus on the importance of information sharing through a new virtual enterprise chain collaboration framework. They analyze the impacts of enterprise collaboration on three aspects: business processes, service components and technologies that are essential for the collaboration of virtual enterprises.

Emerson et al. (2009) focus on the information sharing in a dynamic supply chain. They consider that the actors of a supply chain can update the knowledge independently when they need to keep the partners informed. They use a knowledge base framework in order to analyze the effects of inventory constraints on the performance dynamics of supply chains. They indicate that neither static nor dynamic configurations are consistently dominant. They show that dynamically choosing a supplier or assembler does not always optimize the profits, but it can be more profitable by choosing the right supplier.

Zhou (2009) analyzes the benefit of RFID item-level information visibility using a manufacturing example on multiple periods. He considers the reduced uncertainty as a key factor to increase the benefit in both static and dynamic scenarios. The analysis shows that the benefit due to item-level visibility increases through the improvement of the information system. The results also show that the information visibility in multiple periods can provide improved decision making.

Agrawal et al. (2009) analyze the impact of information sharing and lead time on the bullwhip effect and inventory levels in a two-level supply chain. They showed that, even if the information is shared inter and intra echelon, it cannot completely eliminate the bullwhip effect. Their results show that lead time reduction is more interesting to reduce the bullwhip effect than information sharing.

RFID technologies can deal with the bullwhip effect by considering supply chain as a whole as well as by reducing drastically the information distortion through data capture and real-time communication properties. There are several simulation studies conducted on this subject to analyze the impact of RFID technologies on the bullwhip effect (Joshi, 2000; Simchi-Levi et al., 2000; Fleisch and Tellkamp, 2005). We detail these papers in the next section.

Abstract

The bullwhip effect, also known as demand information amplification, is one of the principal obstacles in supply chains. In recent decades, extensive studies have explored its operational causes and have proposed corresponding solutions in the context of production inventory and supply chain systems. However, the underlying assumption of these studies is that human decision-making is always rational. Yet, this is not always the case, and an increasing number of recent studies have argued that behavioural and psychological factors play a key role in generating the bullwhip effect in real-world supply chains. Given the prevalence of such research, the main objective of this study is to provide a systematic literature review on the bullwhip effect from the behavioural operations perspective. Using databases, including Scopus, Wiley Online Library, Google Scholar and Science Direct, we selected, summarised and analysed 53 academic studies. We find that most studies build their models and simulations based on the ‘beer distribution game’ and analyse the results at the individual level. We also demonstrate the importance of studying human factors in the bullwhip effect through adapting Sterman's double-loop learning model. Based on this model, we categorise and analyse the behavioural factors that have been studied and identify the explored behavioural factors for future research. Based on our findings, we suggest that future studies could consider social and cultural influences on decision-making in studying the bullwhip effect. In addition, further aspects of human mental models that cause this effect can be explored.

5 Conclusions and implications for further research

The coordination of inventory replenishment decisions in a supply chain can increase the efficiency of the channel and improve the position of the companies involved. Benefits of coordination can include lower inventory-related costs, reduced lead-time, and higher product quality. Thus, via integrated inventory management, the competitive position of the whole supply chain can be improved. It is clear that this is especially important in industries facing high competitive pressure and in industries where internal logistics processes have already been rationalized. In such a case, reducing inefficiencies on the supply chain level may help to achieve further improvements in efficiency.

As has been shown, various different planning problems have been studied in the context of integrated inventory models. However, a closer look at the literature (and the online supplement to this paper) shows that several research gaps remain which need to be addressed in future research. The most important research gaps we identified are the following:

Research has thus far focused on relatively small sections of supply chains and concentrated on studying systems that consists of two, three or four stages. In fact, we identified only two papers (Leung, 2010; Seliaman and Ahmad, 2009) that did not predetermine the number of stages and considered an n-stage supply chain. It is clear that modeling an arbitrary number of stages may lead to a complex planning situation that is possibly very difficult to optimize. However, insights that can be gained from such models, for example with respect to the well-known bullwhip-effect, could be of great practical relevance. We therefore suggest shifting the research focus to multi-stage JELS models in the future.

Another aspect that became apparent when surveying the literature is that research has thus far concentrated on the sales side of the supply chain and studied primarily 1:1- and 1:n-relationships. Models that consider multiple suppliers have only infrequently been developed. The online supplement to this paper shows that only 7 out of 155 papers studied more than a single supplier. It is clear that the focus of past research does not adequately reflect the importance of the supply side in creating customer value, wherefore we suggest studying the coordination of the supplier base in integrated inventory models in the future. For the study of the impact of alternative delivery structures on total system costs, Glock (in press) could serve as a starting point.

A third aspect we identified is that the structure of the supply chain under study has in most cases been treated as given. However, it is clear that supply chain management does not only involve the coordination of material and information flows in predetermined channels, but also the selection of supply chain members and the design of delivery structures. Since this aspect has been under-researched in the past, we suggest developing JELS models where the linkages of the members of the supply chain have not been predefined, i.e. where the question of who delivers to whom has not been answered ex-ante. In addition, including the supplier selection decision in an integrated inventory model seems to be promising (see Glock (2011) for a first model in this area).

We found that only a single model has been developed which studies dynamic model parameters in an integrated inventory model (see Bylka, 1999). It is clear that today's business environment is highly dynamic, and that changes in the planning parameters of a member of the supply chain can have a huge effect on other supply chain members as well. For example, changes in raw material prices or expected shortages may necessitate careful planning and precautionary measures to avoid breakdowns in supply, wherefore considering such developments in planning models may lead to many benefits. To identify how changes in the model parameters of certain supply chain members affect the cost position of the supply chain as a whole, we recommend developing JELS models with dynamic model parameters and studying how negative effects of certain changes can be avoided by long-range planning.

Finally, most of the research on JELS models had a theoretical focus, and the applicability of this type of models has only infrequently been subject to research. We therefore suggest analyzing in empirical or case study research how the coordination of inventory replenishment decisions impacts the cost position of the affected companies. Substantiating theoretical results with empirical validation would further highlight the importance of this stream of research.

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