1 Introduction

Uncertainty is an inherent characteristic of today’s business environment. Some typical sources of uncertainty may include demand and supply interruptions, lead time variability, exchange rate volatility, and capacity availability (Gong 2008; Das 2011; Merschmann and Thonemann 2011). Flexibility has been recognized in various disciplines as a strategy to manage different types of uncertainty. The definition of flexibility varies from one discipline/context to another with confusion surrounding its dimensions and stages (Sawhney 2006). In manufacturing, flexibility is referred to various states a system can adopt to manufacture different product types at different volumes (Upton 1995; Slack 1983). That is the ability of a manufacturing system to react by shifting between various states of the system with little penalty in time, cost and performance (Swafford et al. 2006; Upton 1995). Manufacturing flexibility and its measures have been well studied in previous research (Beamon 1999; Yi et al. 2011; Swafford et al. 2006; Koste and Malhotra 1999; Koste et al. 2004; Borenstein 1998).

Supply chain (SC) is an integrated network of organizations involved in the physical flow of products from suppliers to customers (Fahimnia et al. 2013b). A flexible SC is able to respond more quickly to various interruptions in supply and demand as well as changes in other environmental parameters such as lead-time, exchange rate, and capacity limits (Stevenson and Spring 2007; Merschmann and Thonemann 2011). Supply chain flexibility (SCF) has a broad process-based view that incorporates the flexibility of core processes including procurement/sourcing, manufacturing, transportation, and warehousing (Merschmann and Thonemann 2011; Vickery et al. 1999). Research in the context of SCF has evolved over the past decade from infancy to theoretical and conceptual development to modeling efforts and empirical exploratory studies (Stevenson and Spring 2007; Beamon 1999; Sawhney 2006).

Vast literature investigating SCF at the strategic level (known as SC robustness) exists. At the strategic level the objective is to redesign or reconfigure the network or its elements enabling it to adapt quickly and efficiently to major disruptions (Klibi et al. 2010; Swafford et al. 2006). For example, several quantitative models have been developed for the optimal design of SCs when disruptions occur in the downstream SC, that is at the demand side (Pan and Nagi 2010; Georgiadis et al. 2011; Shen 2006; Guillén et al. 2006; You and Grossmann 2008; Gupta et al. 2000). Some others have studied robust SC network design when disruptions are likely to occur at the supply side or upstream SC (Lin and Wang 2011; Peidro et al. 2009b).

Strategic robustness and flexibility through redesigning or reconfiguring an existing network can be expensive. Corporations with established SC configurations are reluctant to adopt costly strategies for mitigating major, yet less frequent, SC disruptions such as natural disasters and financial/political chaos (Sodhi and Tang 2012). Instead, the more frequent uncertainty types such as interruptions in supply, demand, production, and logistics need to be tackled more carefully.

From a practical perspective, one approach to adjust the flexibility of an existing SC is to develop and analyze a SC planning model with inherent flexibility options incorporated into it. The use of such tools can help a corporation manage its usual operations efficiently and effectively, whilst improving its SC resiliency when facing supply, demand, production, and logistics interruptions and variances. A primary issue is that SCF is achieved only if all key processes across the SC (i.e. procurement, manufacturing and distribution processes) have the ability to rapidly respond to environmental changes (Sánchez and Pérez 2005; Vickery et al. 1999; Swafford et al. 2006; Merschmann and Thonemann 2011; Hodder and Triantis 1993). Complexity arises in the development of tactical SC planning models that incorporate multiple flexibility options for all key SC processes.

Despite the dynamic and rapid evolution of research in this field, there has been no attempt to present a review of the existing literature of tactical SC planning with a comprehensive flexibility classification of published models (i.e. the multiplicity of flexibility options). In this paper, we attend to this important issue. A framework is developed in Sect. 2 for defining and quantifying SCF at the tactical planning level by identifying tactical SCF measures and options. Section 3 classifies the published tactical SC planning/optimization models based on the flexibility options incorporated. Other classifications are also presented according to the integration of flexibility measures used, solution methods utilized, and real world applications. These classifications provide important insights and suggest potential directions for future research in the field, presented in Sect. 4.

2 Defining SCF and its measures

‘Flexibility’ and ‘Robustness’ are the two terms that have been used frequently, and in some cases interchangeably, when dealing with SC uncertainties. We differentiate between the two. Robustness decisions are taken at the strategic level and a robust system is meant to remain unaffected or less affected by major less-frequent SC disruptions (e.g. labor strikes, flood and earthquake disasters). On the other hand, a flexible SC is meant to be quickly adaptable in the presence of more-frequent uncertainties such as interruptions in supply, demand, manufacturing, and logistics operations. Flexibility is often present in the form of volume flexibility, delivery flexibility and operational flexibility (Schütz and Tomasgard 2011; Kumar 1988). SC robustness is out of area of our investigation and instead we place our focus on measuring SCF at the tactical (mid-term) planning level.

Unless flexibility measures are adequately defined, it is not possible to compare the flexibility of one SC against another (Gunasekaran 1999; Lummus et al. 2003; Vickery et al. 1999; Stevenson and Spring 2007). SCF measures have been studied in some of the past research (Pujawan 2004; Giachetti et al. 2003; Vickery et al. 1999; Beamon 1999; Stevenson and Spring 2007). A number of potential flexibility categorizations have been given, some of which include the followings:

  • Volume flexibility: the ability to expand the production capacity, e.g. capacity expansion, overtime production and additional production shifts (Sabri and Beamon 2000);

  • Delivery flexibility: the ability to change delivered amount and delivery date, such as a backlogging option (Sabri and Beamon 2000);

  • Operational decision flexibility: the ability to respond to changes in operational decisions such as changes in the bill of material and assignment of jobs to machines (Sánchez and Pérez 2005);

  • Storage flexibility: the ability to respond to sudden changes in supply, demand and production, such as the use of just-in-case inventory (Schütz and Tomasgard 2011);

  • Process flexibility: the ability to manufacture a range of product types at each manufacturing plant (Garavelli 2003; Sánchez and Pérez 2005);

  • Logistics flexibility: the ability to adopt different logistics strategies to deliver the final products to end-users (Garavelli 2003; Sánchez and Pérez 2005);

  • Vendor flexibility: flexibility options offered by different vendors that support manufacturing, warehousing or transport operations (Gosling et al. 2010; Swafford et al. 2006); and

  • Sourcing flexibility: the ability to reconfigure a SC network through selection or deselection of vendors (Gosling et al. 2010).

SCF can also include flexibility in SC relationships, flexibility in SC design, as well as flexibility in inter-organizational information systems (Stevenson and Spring 2007). A generic framework for formulating SCF measures in procurement, manufacturing and distribution has been proposed (Swafford et al. 2006) that provides a range of operational, tactical and strategic measures for procurement, manufacturing, and distribution processes.

Making use of these existing frameworks (Sodhi and Tang 2012; Stevenson and Spring 2007; Swafford et al. 2006), we present a framework to specifically formulate quantifiable tacticalFootnote 1 SCF measures. The framework will then be used for classification of published tactical SC planning models. Three key principles that set the foundation for the development of this framework include:

  1. 1.

    For large corporations with established SC configurations, SCF initiatives are less likely to be introduced at the strategic level (i.e. SC design/reconfiguration level) due to substantial investment requirements. Instead, the focus is on effective use of available resources and adjustment of flexibility options to enhance SC responsiveness. We use the term “inherent flexibility” for those flexibility options incorporated in tactical SC planning models.

  2. 2.

    The overall flexibility of a SC depends on the flexibility of all SC processes and their interrelations; hence, the flexibility-related decisions within a SC must be made while considering the available/achievable flexibility options in other SC processes (Gong 2008; Swafford et al. 2006; Stevenson and Spring 2007). In other words, SCF is multi-dimensional and being flexible in one dimension does not necessarily contribute to the overall SC flexibility.

  3. 3.

    It is widely recognized that flexibility options may be employed both reactively and proactively and hence the options may be differently weighted in various environments (Stevenson and Spring 2007; Sawhney 2006). Given the case-specific nature of SCF options, tactical SC planning models can be utilized to determine the weight of each flexibility option and adjust the SC flexibility in certain environments. This weighting scheme helps determine the degree of flexibility required for various SC processes in order to attain the appropriate level of global flexibility.

Figure 1 illustrates a three-dimensional framework quantifying SCF measures that can be used for tactical SC planning and optimization. The three dimensions of SCF include supply flexibility (i.e. flexibility in procurement and sourcing processes), manufacturing flexibility (i.e. flexibility in manufacturing and assembly processes), and distribution/logistics flexibility (i.e. flexibility in transportation and warehousing processes).

Fig. 1
figure 1

SCF measures (options) for three flexibility dimensions of tactical SC planning

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A typical manufacturing firm spends between 55 and 80 % of its earned revenue on procurement processes and hence supply flexibility options can play a key role in the SC’s financial performance (Benton 2010; Burke et al. 2007). At the tactical planning level, supply flexibility can be addressed in two ways: ‘make-and/or-buy decisions’ and ‘sourcing decisions’. A SC can be more flexible in facing common supply disruptions, if certain products are both manufactured in-house and outsourced (Sodhi and Tang 2012). Make-and/or-buy tactics can be used to build this important flexibility option into tactical SC planning models. Sourcing decisions may include single versus multiple and cross sourcing. While sourcing from a single supplier may incur lower cost per unit through quantity discount and reduced supply management and admin costs, multiple and cross sourcing are the more flexible options enabling a SC to become responsive when facing supply interruptions and demand fluctuations (Burke et al. 2007).

Manufacturing flexibility options at the tactical planning level may include process flexibility (i.e. manufacturing of multiple product types at each plant), manufacturing capacity flexibility (i.e. overtime production, additional operating shifts, and other possible mid-term capacity expansion investments), and delivery flexibility (i.e. backlogging to change the delivery date at a certain penalty cost). While process flexibility initiatives are generally practiced at the strategic level (Beamon 1999; Schütz and Tomasgard 2011), it has been argued that manufacturing firms can effectively mitigate demand risks by establishing some additional tactical process flexibility at each plant (Jordan and Graves 1995). This can be through the addition of multipurpose machines and multi-skill labors enabling a manufacturing plant produce multiple product types in one manufacturing plant.

Distribution/logistics flexibility at the tactical planning level can be addressed through transportation and storage flexibility options. Flexibility options in transport may include the use of multiple modes of transportation (i.e. rail, ocean, road and air), multiple carriers or logistics providers (e.g. TOLL, DHL, FedEx among others), and multiple transport routes to avoid long delays due to local bottlenecks, such as traffic jams. Another approach to enhance the flexibility of a distribution network at the tactical level is to store extra inventory (at rental warehouses) that can be utilized against economies of scale in transport, quantity discount in purchasing, or a manufacturing boom in a certain period.

3 Literature classifications

The methodology we use for the classification of the literature is based on the review strategy suggested by Seuring and Müller (2008). Once the appropriate search terms are identified, the related articles are collected from the Scopus database and stored using the Endnote bibliography software. To avoid possible conflicting judgments, we involve three researchers (including one junior and two senior scholars) in analysis and classification of the collected literature. Keywords included different combinations of “SC”, “Procurement”, “Production”, “Manufacturing”, “Distribution”, “Logistics” “Model”, “Planning” and “Optimization”. We do not include “Flexibility” as one of the search keywords because our objective is to classify the published ‘tactical SC planning models’ with respect to the inherent flexibility options incorporated. We found that the inclusion of “flexibility” as one of the keywords together with the other aforementioned keywords would considerably reduce the search space resulting in only few related articles. Using the Scopus databaseFootnote 2 and a “title, abstract, keywords” search, manuscripts were located and stored using the Endnote bibliography software. The initial search attempts resulted in 114 related journal articles in tactical SC planning (published optimization models with minimization and maximization objective functions) from which 67 models were identified to incorporate at-least one of the SCF measures/options.

Table 1 shows the contribution of different journals in publishing the tactical SC planning models reviewed in this paper. More than a third of the identified articles were published in four journals. The leading journal, the International Journal of Production Research (IJPR), published ten model-based articles that included SCF factors, with the European Journal of Operational Research standing in second place with six. These two journals are very much modeling and methodology oriented journals with strong operations and SC management research covered over the years. They also have some of the largest number of issues published per year, with IJPR growing to 24 issues per year. Most of the journals in the top ten in our list are known for operations and SC modeling. The only one that is less-known in these fields is Industrial and Engineering Chemistry Research. This was one of the surprising journals in the list since it is sponsored by the American Chemical Society, and focuses on Chemical Engineering and production.

Table 1 Contribution of journals in publishing tactical SC planning models
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Surprisingly, in Table 1, some of the leading SC, logistics, and decision sciences journals are not included. For example, the Journal of Supply Chain Management, the Journal of Business Logistics, Management Science and Decision Sciences, which regularly publish SC modeling research, do not even have one article that addresses SCF dimensions at the tactical level. Although recently the Journal of Supply Chain Management has eschewed formal modeling papers, they have historically published a number of modeling articles.

3.1 Classification based on the SCF measures

One classification of the published models can be made based on the SCF measures incorporated. Table 2 groups the published models against the SCF measures they incorporate. Articles were reviewed by each of the three researchers to determine whether articles explicitly covered one of the dimensions listed, at least two people had to agree that it fit within a category. As can be seen, in many cases published models included more than one dimension. Not surprisingly, manufacturing flexibility received the most attention among the three SCF dimensions. For example, almost all the published models have taken into account process flexibility in flexible manufacturing systems; that is manufacturing of multiple product types at each plant. Distribution/logistics flexibility received the least attention among the three SCF dimensions. Multi-route transport has received some attention, while fewer studies in recent years have tried developing multi-modal and multi-carrier transport models. There are only two models studying tactical storage capacity expansion.

Table 2 Classification of the published models based on the SCF options incorporated
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There is a positive trend in the use of supply flexibility options at the tactical planning level. In particular, due to the increasing interest in procurement function in the past few years (Burke et al. 2007), studying the cons and pros of multiple sourcing against single sourcing has been an attractive area of research. By the same reasoning, make-and/or-buy decision making has become a key consideration in the more recent models, especially after 2004.

Overall, we see significant opportunities in SC distribution (external to the organization) opportunities for research. Modeling multi-modal transport research, in addition to capacity flexibility, have significant opportunity for expansion. Another issue with the lack of tactical planning is the possibility that many of the organizations that provide these external services may consider some of these tactical dimensions to be their strategic dimensions.

3.2 Classification based on the integration of SCF measures

In the previous section we made the observation that a number of publications may be grouped into multiple categories based on our general functional dimensions. This multidimensionality of modeling is an important issue. It is widely acknowledged that being flexible in one SC function does not guarantee an equivalent impact on the overall SCF and hence the flexibility-related decisions needs to consider the availability of flexibility options in other SC processes (Gong 2008; Swafford et al. 2006; Stevenson and Spring 2007). In fact, it could be detrimental to organizations investing in flexibility of some functions without proper consideration of its impacts on the other SC processes and the overall SCF (Sawhney 2006). Based upon this viewpoint, we present a classification of the modeling efforts concerning the integration of SCF options in single or multiple SC dimensions (Table 3).

Table 3 Classification of the published models based on the integration of SCF options in different SC dimensions
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Overall, despite the large percentage of papers having some integration, true broad-based integration across all functions and dimensions of the SC is relatively underrepresented. Also, most of the work is after 2004, further evidence that much of this research is in its relative infancy. An important observation is that in every single situation, the operations function is focused on manufacturing. Flexibility modeling in procurement and distribution processes has received relatively limited attention. This situation could be tied to flexibility emerging from the operations and manufacturing literature and then expanding to other SC functions over the years. For example, in the early to mid-1980’s there was a significant growth in the study of advanced, flexible manufacturing systems (Narain et al. 2000).

With a rapidly increasing rate of global sourcing and the growing significance of the procurement function (Benton 2010; Burke et al. 2007), the integration of manufacturing flexibility and sourcing flexibility has continued to rapidly evolve. This increased focus makes the integration of tactical flexibility research even more attractive.

Manufacturing and distribution flexibility integration modeling has a longer history than other integrative approaches. Whereas, SC planning models (in particular after 2006) tend to consider the integration of SCF-related decisions along the three SC dimensions (i.e. sourcing, manufacturing and distribution). These observations all point to greater focus and need for research on the flexibility of non-manufacturing and broadly integrative tactical SCF analytical modeling.

3.3 Classification based on the solution methodology

Solution methodologies are important from a research perspective. Developments and application of specific types of methodologies is important since it not only provides resources on the type of methodologies that exist and can be applied to various settings, but it also identifies areas of methodologically oriented research. Of course, much of the type of solution methodology is based on model characteristics. Thus, we also classify the overall models into general linear and non-linear approaches. Further, we also break down the categories into exact, non-exact, heuristic and meta-heuristic approaches, as shown in Tables 4 and 5.

Table 4 Classification of the published models based on the solution methods used: exact methods
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Table 5 Classification of the published models based on the solution methods used: non-exact methods
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Only about a third of the solution methodologies can be classified as exact solution methods. Exact solution methods can be used to solve small/medium size linear, which could include mixed-integer-linear, problems. Due to the nature of SC planning problems, most of linear and nonlinear SC planning models are mixed-integer in form containing both continuous and binary (0-1) integer values. Some of this may be driven by the real world situations, but some may also be driven by the need for greater complexity in modeling to get published in leading journals. The same sort of pressure for identifying novel and innovative solution methods may be driven by publishing pressures, rather than practical applicability. In terms of solution tools and solvers, CPLEX has been the most popular linear solver that has been adopted to solve 52 % of all linear models.

Non-exact solution methods (including heuristics, meta-heuristics, stochastic, probabilistic, and fuzzy methods) have been used to deal with more complex, larger linear models as well as nonlinear models. Larger linear models (57 % of all published models) have resulted in unsolvable situations when using the exact solution techniques. Only 9 % of all modeling efforts have led to nonlinear models. Part of this may be driven by finding better and easier to solve optimization problems. The complexity of the modeling environment has led to the greater use of heuristic and meta-heuristic methods. There is a greater emphasis on heuristic approaches rather than broader meta-heuristic approaches. This situation admits the extreme complexity of most tactical SC planning problems requiring customized heuristics solution techniques instead of adopting off-the-shelf solution methods. More recently, there has been greater tendency towards the utilization of stochastic, probabilistic, and fuzzy modeling approaches to deal with SC uncertainty. About 25 % of all the published models use such techniques (17 studies out of a total of 67), all of which have been published during the past decade. Like any field, as the area advances greater complexity and more robust and powerful solution techniques replace older techniques. This is evident in the published works in the area of tactical SC optimization.

3.4 Classification based on practical application

Many of the characteristics of a model, and even solution methodology, may be dependent on the type of industry and product type studied, if there was a specific application. A classification based on the practical industrial application of the published models is shown in Table 6. First, we found that more than half of the reviewed articles have been applied or investigated practical industrial applications. The remaining publications focused on simulated numerical experiments. Almost three-quarters of the industrial application studies have been in three industries, including automotive, chemical, and wood and paper industry.

Table 6 Classification of the published models based on the real world application
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The results show a broad industry application over a number of different product types and families from durable discrete production goods to process oriented products. When considering the types of models used, most of the industries are looking for integrated functional perspectives. Interestingly, all the pulp and paper industry models included all three functional dimensions in their models. Thus, it can be expected that models that included practical applications tended to have more complex and comprehensive optimization models.

4 Conclusions and directions for further modeling efforts

In this paper, we provided an initial review of SC modeling research that has seen little investigation. Within SC design and planning, understanding of SCF is critical for competitive advantages as competition starts to shift to the SC versus SC level. Uncertainties and the growth of made-to-order (build) mass customization demands require a better understanding of SCF. SCF is an accepted, growing and important area of research as evidenced by the literature reviews covering this field. Our findings show that a large percentage of SC planning review papers mainly highlight the modeling characteristics and solution techniques without explicitly discussing the issue of flexibility (Comelli et al. 2008; Fahimnia et al. 2013a; Min and Zhou 2002; Mula et al. 2010; Peidro et al. 2009a; Schütz and Tomasgard 2011). The focus has been on strategic SCF with a greater emphasis on non-modeling studies (e.g. empirical field study oriented works).

Research on tactical SC planning can be explored to a larger extent if SCF can be appropriately defined and measured. However, a limited definition of SCF exists with confusion surrounding its scope and applicability (Sawhney 2006; Stevenson and Spring 2007; Swafford et al. 2006; Gong 2008). This is one of the contributions we sought to make in this paper by providing a review of formal analytical modeling approaches that covered some aspect of SCF across multiple dimensions of the SC.

A number of general observations provide insights into future research directions. One direction for future research may be to develop quantitative models that can examine the value of flexibility options in different SC dimensions and their interrelation (noting that flexibility does not come free). That is, the tradeoffs in other performance measures such as cost, quality and speed can be influenced by tactical SCF measures. Also, greater integrative measures which are evident in most practical settings are needed which may be developed and tested conceptually first. One thing we did find was that integrative models did not consider their own flexibility investigations. For example, analytical approaches can be used to investigate the effectiveness of incorporating multiple flexibility measures across SC and to show under what conditions one SCF measure can dominate another SCF measure. Comparative analyses across and between classifications (applications, functional integrative complexity, and solution methodology) can be investigated. We did see that research into some classifications, such as distribution and transportation, are underrepresented. Expanding many of the models from operations and manufacturing flexibility into these underrepresented areas of tactical flexibility research can be a fertile area for future research.

Although this study helps fill an important gap in the SC modeling research literature, it is not without limitations. These limitations provide fodder for additional investigation. We limited our search to actual published works in peer reviewed journals. We used the Scopus database for the search. Although a very comprehensive search engine, it doesn’t cover all publishers and proceedings and research books that may contain some of the latest models. Also, research previous to 1996 may have fallen through the cracks, especially if electronic versions of the publications did not exist. A more exhaustive search could potentially provide a slight shift in our classifications. Another limitation of this review is that our SC functions were focused primarily on forward logistics. Our preliminary analysis on reverse logistics aspects of the SC shows that flexibility investigation in reverse SC operations is sparse. To the best of our knowledge, the only study that focuses on the development of a reverse logistics flexibility framework is the work of (Bai and Sarkis 2013) that attends to the strategic and operational flexibility options. A focus on tactical flexibility options and measures, especially analytical modeling, in a reverse SC is virtually non-existent in the current literature. Overall, SCF is a fertile area for research and we believe this paper helps set a foundation for additional understanding and research direction.