This increasing global complexity and turbulence has resulted in increased volatility of demand and turbulence in distribution channels, especially for multinational companies (Kersten, Grussenmeyer & Lammers 2012). In addition, marketing factors, such as the growth in scanner and retailer information systems, have given retailers more control than manufacturers, which has resulted in the growth of private label house brands that increase the supply chain complexity (Williams 1994; Roberts 2000).

Traditional planning and control systems cannot manage complexity (Windt & Hülsmann 2007) and many supply chains are finding it difficult to cope with the increasing risk in today’s turbulent and complex environments (Malik et al. 2011). Fawcett and Waller (2011) suggested that such problems need to be examined ‘through new lenses’. A growing body of research is showing that complexity theory is useful in understanding and gaining new insights into logistics systems (Milgate 2001; Nilsson & Waidringer 2004) and into the unpredictability in supply chains (Stapleton et al. 2006; Hashemi et al. 2013).

According to Maguire, Allen and McKelvey (2011), a complexity system is ‘a “whole” made up of a large number of interacting “parts”, or “agents”, which are each governed by some rule or force’. The outcome of such systems is usually emergent phenomena brought about by ‘upward causality’. These phenomena are not random or static – they are spontaneous, adapting to their environment, but are very difficult to predict, often because of small, seemingly inconsequential events. Complexity theory is the scientific study of complex systems and the emergence from, and self-organisation of, the phenomena arising from such systems. It has been shown to be applicable in systems such as biology, ecology, immune systems, economics and marketing. Sellitto et al. (2010) showed that complex behaviour occurs in supply chains.

However, despite the growth in literature about complexity and complexity theory, there is still a lack of focus on complexity of distribution (Kersten et al. 2012) and very little focus on transportation as a complex system (Frazier & Kockelman 2004), especially from a global perspective (Nilssen & Gammelgaard 2012). There is also a lack of research into appropriate methods to cope with supply change complexity (Gerschberger et al. 2012), which is important since complexity and turbulence in supply chains are expected to continue to increase (Hashemi et al. 2013).

The purpose of this study was therefore to add to the logistics and complexity theory literature by investigating the types of logistics or distribution tactics suggested by the complexity theory literature for complex and turbulent environments versus those suggested for simple and stable environments. This was applied via a multiple-case study, exploratory approach in South Africa, which is a developing country with many industries experiencing extreme complexity and turbulence and growing competition from global competitors (Mason 2004).

The underlying idea of complexity ‘is that all things tend to self organise into systems’ when simple rules are applied (Kelly & Allison 1999). These systems can produce unexpected patterns or behaviours (Goldberg & Markoczy 1998; Manuj & Sahin 2011) because of non-linear feedback networks (Stacey 1996), the interconnection and interdependence of complex systems (Bar-Yam 2000) and because the system’s parts interact and adapt to each other (Meade & Rabelo 2004). Complex behaviour is orderly, yet full of surprise; it is apparently uncontrollable, yet not totally chaotic. The rules that generate this behaviour are not enforced by a ‘manager’, and cannot be predicted from any single part of the system and thus cannot be controlled (Wu & Zhang 2007).

Several complexity concepts have relevance to business. The central concept is self-organisation; the process of order emerging from simple rules in a system, which a ‘manager’ does not control (Holbrook 2003), but which is due to the interaction between autonomous actors or agents in the system (Wilkinson & Young 2013). Creative and innovative responses emerge despite the difficulty of ‘managing’ the system (Dolan, Garcia & Auerbach 2003). This emergence, the second important concept, happens when the system changes, leading to disorder and prevention of the system from ossifying. Emergence happens at the edge-of-chaos, enabling new actions to emerge. New product development behaviour emerges from the operational level (McCarthy et al. 2006).

Third concept is feedback. Negative feedback damps change, pushing systems to equilibrium (Stacey 1995). Positive feedback amplifies small changes, pushing systems towards chaos (Doherty & Delener 2001). Together, positive and negative feedback balance the system at the ‘edge-of-chaos’, which is the best position for turbulent environments (Doherty & Delener 2001). Tarokh, Dabiri, Shokouhi and Shafiei (2011) showed how inventory management can produce self-reinforcing positive or negative feedback loops.

The fourth concept is sensitive dependence on initial conditions (Briggs & Peat 1999). In stable systems, small changes have small effects, but in complex or turbulent systems small changes can grow exponentially, making long-term prediction impossible (Doherty & Delener 2001; Holbrook 2003; Wu & Zhang 2007). This phenomenon, also known as the ‘butterfly effect’, is similar to the ‘bullwhip effect’ in supply chains (Badillo-Pina, Tejeida-Padilla & Morales-Matamoros 2012), which is typical of over-or under-reacting in dynamic environments (Wilkinson & Young 2013). Small nudges, at the correct time, can lead to major changes (Wheatley 1996). Patterns and clues indicate which changes to ‘nudge’ (Morrison & Quella 1999) and when to nudge them (Gladwell 2000). These patterns are known as attractors, which is the fifth concept. The edge-of-chaos attractor, known as a ‘strange attractor’, reflects the area where maximum creativity and innovation happens (Lewin 1992). A unique feature of the strange attractor is that it stays within certain boundaries (Holbrook 2003). How the system will develop cannot be predicted, but it will not go outside its attractor (Doherty & Delener 2001). Thus, the attractor allows change whilst maintaining some order.

The traditional approach to handling uncertainty in distribution also involves increasing safety stock, providing a broader range of products, lengthening lead times or improving distribution capabilities (Narus & Anderson 1996; Milgate 2001), thus avoiding late deliveries, poor quality and unforeseen demand. Planning provides a good, and maybe even an optimum, solution in a stable environment, but in turbulent environments where the past is not repeating itself, planning based on the past can be dangerous (Johnston & Betts 1996).

The place component is becoming less important and success can no longer be achieved by concentrating on improving the firm’s distribution activities alone (Wilding 1998; Tapscott 2000). Traditional linear optimisation of supply chains is not adequate for modern dynamic systems. Poor communication can break the link between end-product demand and the manufacturing process, as happens when channel members operate in transactional, rather than relationship, mode (Holmstrom et al. 1999). Success in dynamical systems comes from the interrelationship of the performance of the company and its suppliers and customers. Supply chains are so complex that it is not possible for individual managers or buyers to take optimal decisions, as they are knowledgeable about only a very small proportion of the chain. Therefore, their decisions can have unpredictable effects up and down the supply chain. The traditional, transaction approach of a firm managing its place function independently is thus inadequate in a complex, rapidly- changing environment (Whiting 2001).

This brief discussion shows that the traditional approach of planning and control, and the use of buffer stocks to manage the place component, still appears relevant in a simple and stable environment, but is not necessarily the most effective approach in a turbulent environment. Therefore the following propositions are suggested:

Proposition 1: in a simple or stable industry, a more successful company uses distribution tactics in a stabilising way.
Proposition 2: in a simple or stable environment, a less successful company uses distribution tactics in a destabilising way.

• Using correct supply chain information – when end-user or consumer-demand information (generated at the point-of-sale) is used to replenish inventory throughout the channel, much more consistent inventory levels result. Damping of fluctuations leads to reduction of safety stocks and minimisation of stock outs, resulting in reduced supply chain costs and overall improvement of channel profitability (Priesmeyer 1992; Wilding 1998; Milgate 2001; Stapleton et al. 2006). This is supported by De Leeuw, Grotenhuis and Van Goor (2013), who found that exchanging information and communication were key coping mechanisms, and by Chien-Yuan and Jinsheng’s (2011) finding that the use of radio-frequency identification (RFID) technology can diminish chaos in a supply chain.

• Accepting the uncertainty and adopting short term, or reactive, managerial actions within the prediction horizon. This approach is simpler, can respond more quickly to opportunities, reduces operating costs and increases the value of the system (Levy, 1994; Wilding 1998; Johnston et al. 2000). De Leeuw et al. (2013) supported this and found that flexibility is a key means of coping with complexity.

• Treating the supply chain as a complex adaptive system uses a few simple rules to manage the system. For example, Southwest Airlines simulated their cargo operation by using a few simple rules and saved millions of dollars by not transferring packages to the most direct flights – a decision that was counter-intuitive (Whiting 2001).

• Shifting competition from ‘between firms’ to ‘between supply chains’ leads to a growth in alliances and collaborations (Stapleton et al. 2006). Partnerships and strategic alliances in supply chains leads to a blurring between members of the supply chain with their roles overlapping and co-location taking place. This improves knowledge of customer needs, reduces stock holdings and speeds up the delivery cycle (Glazer 1991; Mohr 2001). This sees a shift away from rivalry towards partnerships and extended co-operation (Milgate 2001) and outsourcing to one or two suppliers (De Leeuw et al. 2013), with such collaborative relationships reducing supply chain complexity and turbulence (Manuj & Sahin 2011).

• Despite the above, buffer inventories, reserve equipment or flexible staffing are often used to stabilise the chaos-inspired uncertainties in the demand in the supply chain. This increases the flexibility of the system (Manuj & Sahin 2011). These buffers allow the system to operate in a chaotic environment as they absorb or dampen unpredictable fluctuations (Phillips & Kim 1996). In fact, De Leeuw et al. (2013) found inventory to be the main coping mechanism for complexity in supply chains.

Based on the above approach, the concept of ‘adaptive channels’ has been suggested because of the increasing dynamics of business and increasing competition, especially from global competitors who use unconventional methods because of their exclusion from the traditional channels (Narus et al. 1996). The ‘adaptive channels’ method involves co-operative arrangements, strategic alliances and partnerships between channel members and other suppliers, using shared information systems and integrated logistics systems. The network of capabilities in the channel enables it to be more flexible and responsive by having all the skills and inventories to satisfy the standard and emergency needs of customers. Instead of trying to cope with the chaos of unanticipated emergency demands by increasing inventory levels, the system copes with it by sharing the inventory load and by very quickly responding through integrated computer systems and express delivery suppliers. In other words, the chaos is coped with through the larger system, rather than each agent in the system trying to cope with its own locally generated problem through traditional planning and control (Wang 2008). This is consistent with managing supply chains as emergent and self-organising phenomena (Nilsson et al. 2004). In fact it might end up with increased supply chain complexity (De Leeuw et al. 2013).

Based on this analysis, two more propositions are suggested:

Proposition 3: in a complex or turbulent industry, a more successful company uses distribution tactics in a destabilising way.
Proposition 4: in a complex or turbulent industry, a less successful company uses distribution tactics in a stabilising way.

In summary, the literature has suggested that the following are the key place tactics that can be used in a stabilising or destabilising way, and that are important in, and can vary according to the nature of the environment: use of intermediary; partnership or alliances; physical distribution; stock levels; and stock replenishment. The literature also suggests that more successful companies in complex and turbulent environments are more likely to use the place tactics in a destabilising way, whilst more stabilising ways of using these tactics are more likely to be typical of successful firms in simple and stable environments. The research problem is thus whether this applies in practice, and moreover, whether it applies in a developing country like South Africa. Therefore, the research objective of this study is to assess whether the proposals developed from the literature review can be accepted in the South African environment.

Due to the paucity of complexity-oriented research in this field, a qualitative exploratory approach, namely the case study, was chosen (Gerschberger et al. 2012; Wilkinson & Young 2013). The research was conducted in South Africa, where most components of the external environment are turbulent, which make it a good ‘laboratory’ (Morris et al. 1996; Joubert 1998).

• First, the most complex or turbulent and simple or stable industries were selected via a questionnaire posted to experts who were industry analysts and management consultants. The results highlighted information technology as being the most complex or turbulent, and packaging as the most simple or stable.

• Within each industry, more successful and less successful companies were chosen using a Delphi process, with panels of industry experts who were consultants, journalists and buyers. They were asked to subjectively or qualitatively consider ‘success’ in terms of the companies’ performance over the previous three to five year period. The more successful firms being those that had achieved consistent growth in sales, profits and assets, that had increased market share, or that had adapted well to their changing environment. The less successful firms were those that had performed poorly on these factors. To maintain anonymity, artificial names were developed for the companies – C was used to indicate an information technology (computer) company and P indicated a packaging company, whilst A was used for a more successful company and B for a less successful company. The experts thus nominated CA as more successful and CB as less successful in the information technology industry, and PA as more successful and PB as less successful in the packaging industry. Table 1 provides a profile of the four companies.

‘Method-appropriate criteria’ and multiple data collection methods validated the procedures, increasing rigor and trustworthiness, namely: (1) the data triangulation (multiple interviewees, documentation and field notes); (2) methodological triangulation (qualitative interviews with some quantitative content analysis); (3) prolonged engagement (multiple interviews and minimum of three days spent in each firm); and (4) an audit trail (all data collected was kept on CD) (Flick 1998). Using multiple data sources increased construct validity. Internal validity was increased by comparison and pattern matching across the cases. External validity was increased by using cross-case analysis of multiple cases (thereby allowing some generalisation). Reliability was increased by using a data collection protocol and by keeping a database of the empirical data and a chain of evidence (Yin 2003). This method adhered to the ten design considerations of Lincoln and Guba (in Rudestam & Newton 1992) and thus met the criteria for a high quality, rigorous and trustworthy study.

Both companies make use of partnerships and strategic alliances, but for CB this is a recent innovation, whereas for CA there are long standing alliances and it is more prepared to partner with competitors if it is in their interests.

Differences between the two companies exist in the area of physical distribution, because CA deals, to a considerable extent, in computer hardware, whereas CB does not. CA does not carry stock due to the rapidly changing nature of its industry, so physical distribution is generally less important. However, not carrying stock means that it has to manage the supply of materials very carefully, so logistics is critical as mistakes can result in lost sales. For CB, physical distribution is not seen as important because it does not sell ‘products’. However, CB is aware of the importance of distribution of its services, which involves deploying staff geographically.

The nature of the two companies’ business seems to influence their attitudes towards distribution. CA’s heavier emphasis on business outside South Africa and the fact that it sells hardware means that it places more emphasis on intermediaries and logistics than CB, which predominantly sells software and systems. CB’s emphasis on customised software leads to problems in the physical distribution of its main resource, which is, people. Taking this factor into account, it appears as if ‘place’ does not account for much difference between the two companies. Furthermore, from the relatively low-key approach to distribution, it appears as if both companies consider ‘place’ to be less important than the other components of the marketing mix.

These findings are generally what would have been expected for a more successful company in a turbulent and complex market (CA), and so Proposition 3 can be accepted, namely that a more successful firm in a complex or turbulent market tends to use destabilising place tactics.

Regarding the less successful company (CB), almost the opposite of what was anticipated was found. Therefore, Proposition 4 could only be partially accepted, namely, the less successful firm tends to use a mix of stabilising and destabilising tactics.

The reason for this anomaly seems to be that CB has not yet had to face distribution problems as it has been operating mainly in a domestic niche market, both in terms of a specific industry, and in terms of a geographic region, whereas CA involves physical distribution of products in both the domestic and global markets.

Both companies emphasise the importance of physical distribution for the success of their operations, because of the criticality of on-time delivery.

Both companies do carry small quantities of stock of both raw materials and finished goods, but the stock levels are kept to a minimum. They generally produce to order, or only keep stocks for selected, preferred or larger customers. Raw material stocks are also kept to a minimum by encouraging suppliers to keep back-up stocks for them. Both companies, rather than producing as and when necessary or carrying large buffer stocks do stock replenishment on a planned basis for customers. PA’s inventory planned replenishment is indicated by its ’stock schedule system’, whereas PB’s stock replenishment is more reactive, at the request of the customer.

The distribution activities of both companies were very similar. No significant differences between the two companies were found. As a generalisation, it appears as if both companies see distribution as a fundamental activity that has to be done well, regardless of the environment or any other tactical activities. As such, the successful company in the stable and simple environment (PA) places sufficient attention on distribution to ensure that its stabilising influence supports its other activities. On the other hand, the less successful company (PB), which was expected to downplay the importance of distribution, does place some importance on the distribution function, seeing it as fundamentally important.

For both companies, the distribution tactics were generally as expected, although for company PB, the lack of alliances and the planned stock replenishment was not as expected. Both Proposition 1 and Proposition 2 can only be partially accepted, implying that both companies use a mix of stabilising and destabilising place tactics.

In summary, the following are the conclusions drawn from the findings regarding each proposition:

Proposition 1: in a simple or stable industry, a more successful company uses stabilising distribution tactics: partially accepted as PA uses a mix of stabilising and destabilising place tactics.
Proposition 2: in a simple or stable environment, a less successful company uses destabilising distribution tactics: partially accepted as PB uses a mix of stabilising and destabilising place tactics.
Proposition 3: in a complex or turbulent industry, a more successful company uses destabilising distribution tactics: accepted as CA tends to use destabilising place tactics.
Proposition 4: in a complex or turbulent industry, a less successful company uses stabilising distribution tactics: partially accepted as CB tends to use a mix of stabilising and destabilising place tactics.

The net result of these findings is that place tactics should not just be seen as the basic stabilising foundation upon which other tactics are built. Seeing place tactics as just increasing safety stock, lengthened lead times or improving distribution capabilities (Narus & Anderson 1996; Milgate, 2001), or as Kersten et al. (2012) puts it, using regulating strategies, is no longer sufficient. In turbulent environments, supply chains should be treated as emergent and self-organising phenomena (Nilsson et al. 2004), and therefore the ‘adaptive channels’ method involving co-operative arrangements, strategic alliances and partnerships, shared information systems and integrated logistics systems throughout the whole supply chain should be used, as suggested by Narus et al. (1996).

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