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Analyse and evaluate relevant subject knowledge, skills and abilities, in this case - supply chain management techniques, and thus develop their competencies in this area.

Purpose:

  1. To demonstrate comprehension of the major supply chain management areas including foundational concepts, supply chain functions and processes, competencies etc.
  2. To model and analyse supply chain networks, components, and processes with the aid of a SCM framework.
  3. To discuss the integrated nature of modern supply chain networks.

Required:

  1. This section of the portfolio assessment consists of writing an evaluation report, using the report template given on EIT Online, to present your analysis of an organisation’s supply chain network mapping and integration.
  2. The report will consist of two parts:

Part (a) is a written discussion about the nature and degree of supply chain process integration based on your analysis of the case study.

Part (b) is creating a supply chain model to illustrate an organisation’s supply chain network. The network should be mapped using a recommended supply chain framework.

  1. Assessment Tasks- There are two assessment sub-tasksto be completed:

Task 1- Building a supply chain network model using a framework: Using the recommended supply chain framework, model an organisation’s supply chain network. In your model, you should:

  1. a) Identify and label the major components of the supply chain including:
    • internal components of the organisation
    • external components of the supply chain
    • trace the flow of materials, information, products, finance etc. in and across the supply chain
      1. It is expected that students will refer to the course readings & resources, conduct own desktop/ secondary research to identify the generic industry supply chain network structure; which will then be used for modelling organisation’s supply chain.
      2. This is an illustration task using the Generalised SCM (GSCM) framework model template given in this document and must be used.
      3. Illustrations are to be made using MS-Word or MS-Visio which are available to EIT students through IT services.
      4. Usually the illustration is a one-page document, including all labelling.

Task 2: Discuss the nature and degree of supply chain integration: Based on the GSCM developed by your group, analyse and comment on the nature and degree of integration of organisation’s supply chain processes. A comprehensive and in-depth discussion will include:

  • A concise introduction explaining the meaning of the nature and degree of integration supported with evidence from relevant course reading.
  • Identification and classification of the organisation’s supply chain processes.
  • Using examples, analysing and discussing the nature and degree of supply chain process integration within the organisation’s supply chain.

Purpose:

  1. To demonstrate understanding of demand management and planning processes.
  2. To demonstrate understanding of demand forecasting techniques and usage.
  3. To develop and evaluate demand forecasting models

Required:

  1. The report will consist of two parts:
    1. Part (a) is a written report evaluating the demand forecasting models in predicting future demand, based on a given dataset.
    2. Part (b) requires applying demand forecasting techniques to develop multiple demand forecasting models to predict the future demand for organisation’s product/ services. Obviously, Part (b) needs to be completed before Part (a) can be completed.

  2. There are the sub-tasksto be completed:
  1. Three-period weighted moving average. Assume most recent period weight to be 0.6, the next period weight is 0.3 and last period weight is 0.1 to begin with. You may then change the weights to create the best possible forecast.
  2. Exponential smoothing method. Smoothing coefficient α = 0.7 to begin with.
  3. Trend projection model
  4. Compute the forecasting error for each model developed: using (a) Mean absolute deviation (MAD); (b) Mean squared Error (MSE)
  5. For each model, develop seasonal indices.
  6. Graphically represent all final demand forecasting models.
  7. Interpret the results of the demand forecasting models and compare the models based on their ability to accurately predict demand.
  8. Evaluate the demand forecasting models and processes at the organisation.

Purpose:

  1. To develop and evaluate aggregate planning scenarios to meet forecasted demand
    • Changing inventory levels.
    • Varying workforce size by hiring and layoffs.
    • Varying production rates through overtime/ idle time.
    • Sub-contracting.
  2. Develop a Material Requirements Plan (MRP) based on the MPS.
    • Develop gross requirements plan
    • Develop net material requirements plan.
  3. Demonstrate comprehension of inventory management and inventory modelling. Develop and evaluate inventory management plans based on the MRP requirements.
    • Develop economic order quantity model for inventory modelling.
    • Develop production order quantity model for inventory modelling.
    • Develop quantity discount model using EOQ for inventory modelling.Required:

There are 3 assessment sub-tasks to be completed in this section:

Develop and evaluate three aggregate production plan options for a given situation.

 Image 1

Create TWO more plans (Plan B and Plan C) and compute their total cost. Compare these THREE plans based on total cost, and some related implementation issues.

Develop Material Requirements Plan (MRP) for a given product for one production cycle. The MRP must include:

  1. Gross requirements plan.
  2. Net requirements plan.

Background: Alex Last, the production manager of a Health Equipment Manufacturer, needs to schedule an order for 60 Steppers, which are to be shipped in week 9. Details about the steppers are given in Table 1, where the numbers in the brackets indicate quantity required for each parent (i.e. the product at upper level). Assume lot-for-lot ordering model.

Table 1

Level

Item

Lead time

On-hand inventory

Components required

0

Stepper

2

20

A (1), B (3), C (2)

1

A

1

10

D (1), F (2)

1

B

2

30

E (1), F (3)

1

C

3

10

D (2), F (3)

2

D

1

15

 

2

E

2

5

 

2

F

2

15

 
  1. Develop a product structure for the Stepper.
  2. Develop a net material requirements plan for item F.

 Image 1

Supply Chain Process Integration

The concept of integrated interaction of counterparties in supply chains

The term "integration" is applied in various fields of knowledge and has its own specifics. From the point of view of linguistics, the term "integration" comes from the Latin integer - the whole, reconstructed. In other words, integration is a union in whole or parts.

In mathematics, an integral is a quantity obtained as a result of an action that is the inverse of differentiation, or by summation and transition to a limit. The integral is also an integer, considered as the sum of its infinitesimal parts (Brahimi & Aghezzaf,2015).

In the theory of supply chain management, integration can be seen as a process of interaction between participants in the supply chain, aimed at achieving common goals by expanding and deepening production-technological links, sharing resources, pooling capital and creating favorable partnerships for joint economic activities.

Nature of integration

Initially, the concept of Supply Chain Management was identified with the concept of integrated logistics. In the process of evolution, the theory of Supply Chain Management turns in independent science, there is a shift and separation of conceptual and semantic categories and individual terms between logistics and SCM. At the same time, the concept of integration at the moment is key both in logistics and in the concept of supply chain management (Christopher, 2016).

It is necessary to distinguish between internal and external integration. In Fig. 6.1 presents the integration of the maximum supply chain in a generalized form. Under the internal integration of the supply chain is understood the totality of the business processes occurring within the framework of the focus company (in the figure - the darkened area).

The process of internal integration in its economic essence can be identified with the term "integrated logistics". The degree defines the notion of integrated logistics as an end-to-end management of the flows of the logistics system, passing through all its links, which is consistent with its division into functional areas (supply logistics, production and distribution), whose activities are subject to the overall goal of the entire system as a whole. In his opinion, such a division "allows more accurately to define and solve local tasks of organization and control within the links and elements of the logistics system, since objects of practical logistics can be not only flows, but also individual transactions. Thus, the purpose of applying an integrated approach in logistics is the need to combine different functional areas and their participants within a single logistics system in order to optimize it (De Neufville,2016).

Creating a Supply Chain Model

In the context of the concept of supply chain management, the process of coordination between the focus company and other participants in the supply chain should also be added to the need to combine the various functional areas of logistics and their participants. Therefore, considering the relationship of the central company with its counterparties - suppliers of raw materials, purchasers of finished products and various intermediaries - we can talk about the external integration of the supply chain.

The application of solutions for supply chain management and demand management should be the first in maximizing the company's profit. But in many companies, the barriers that restrain their use are not yet fully understood. Changes in the behavior of organizations to overcome these obstacles are expected in the near future. In the academic literature, such obstacles were identified quite a long time ago. Over the past twenty years, several authors have clearly highlighted the conflicts between the tasks of supply chain managers and marketers. The table contains a number of frequent and repetitive complaints of logistics managers and marketers. Models can play an important role in resolving these conflicts, providing arguments for an objective assessment of disagreements and enabling them to be withdrawn (De Neufville, 2016). Without models or rational analysis, conflicts can easily be resolved through compromise or attitudes, but this makes managers feel that they are being asked to follow inefficient strategies. The first step in resolving conflicts through the use of data and models is the need to accept quantitative methods for constructing descriptive models for forecasting, or designing, demand for finished products, ideally it is used in marketing and sales decisions.

Explain the need for processes of harmonization and cooperation between organizations within the supply chain;

Show how the management of supply chains can be raised to a new level by improving the introduction of new products, promotion campaigns, product mix and replenishment;

Identify methods for implementing joint planning between participants in the logistics chain;

To develop a scheme for managing the supply chain.

Descriptive models of the supply chain, marketing, cost of sales are also needed, as well as other descriptive information such as production capacity, transportation, etc. When this is done, descriptive models should be built into the optimization models that determine the relationship of solutions along the chain supplies and solutions for sales and marketing. An effective synthesized model will appear when it is improved after applying scenarios, their estimates and conclusions.

Aggregate Production Plan Options

The task can also require some subtleties of descriptive models, for example, to create from them an acceptable form and structure of the optimal model. It seems that irreconcilable disputes between logistics managers and marketers, even when using models, can be resolved by applying a standard in-sample versus out-of-sample validation method. Turning to the historical period, for example, data for the last two years, descriptive and optimization models would be based on the data introduced in the first year (Mangan & Lalwani, 2016). Then the optimization model would be improved for the second year based on the forecasts for this year that come from the first year published. The strategy put forward by the optimization model will then be compared with what actually took place, that is, with the resulting data. Serious discrepancies between optimized and actual results can be the results of errors in the demand forecasting model, which the producers assumed in the second year, which is significantly different from what really happened (De Neufville, 2016). As a variant, inaccuracies could arise due to the structure of the optimization model, which led to unrealistic strategies. Of course, it is most likely that the differences between model results and reality lie in the shortcomings of both types of models. A rational discussion of the causes and consequences of these shortcomings and their elimination should, perhaps, go a long way toward changing the established beliefs of managers. It can be seen that this approach will help solve the first conflict between logistics managers and marketers, consisting in the possibility of losses due to insufficient capacity to meet unexpectedly high consumer demand or real losses due to excess capacity. Similarly, the second conflict can be assessed using a tactical 12-month model based on periodic data output during the year, say once a month, to better understand the capabilities of the supply chain of the company to respond to unexpected changes in demand (Sorbi & Sani, 2017). Models could help resolve most other conflicts. The key design we need is the integration of the supply chain with marketing models that predict the impact of price, product promotion, advertising and sales efforts on market share and demand (De Neufville,2016). In our discussion, we will only touch on marketing. Moreover, the quantitative analysis of decisions on the supply chain includes the cost, resources and properties of physical products that are easier to measure and anticipate. With this in mind, our approach to supply chain integration with marketing will be aimed at creating certain structures that will expand the supply chain models to solutions for sales and markets and will reduce the impact of demand and product range. Such an approach can be initiated with the construction of a supply chain model that minimizes the total cost of projected and real demand. We can assume or, finally, hope that these prospects will reveal the need and profitability of the marketing model, even if it turns out to be difficult to verify it for reliability.

Material Requirements Plan

Conclusion and Recommendation

What drives integration in the supply chain? The belief that joint activities to meet the needs of the end user are superior to independent relationships in many ways. Let's take as an example four principles of the logistics chain strategy of the company:

Issue all types of products that need to be produced continuously, with the help of short cycle production.

Operatively communicate with those suppliers with whom a long-term relationship is established.

The first three principles concern integration - both internal and external. The fourth principle relates to developments in the field of information technology, which ensure even closer cooperation.Evidence that increasing integration (both upward and downward) leads to an increase in the efficiency of the supply chain as a whole, have been obtained in the studies conducted for companies engaged in the production of prefabricated metal products, mechanical equipment and machinery.

The authors found that strategies of the most extensive integration lead to the highest rates of significant improvement in performance. They presented it in the form of "arches of integration". Our version is shown in.

Closer cooperation between marketing and logistics services led to improved performance and more effective interaction between departments. This may seem obvious, but among the results obtained, there is a reduction in cycle time, trade indicators, increased availability of goods, and a reduction in the time from receipt of the order to delivery.

Companies with a higher level of internal integration demonstrated higher logistics efficiency compared to companies that have a lower level of integration. Differences between high-performing companies and companies with low integration by main parameters of service were not observed: that is, constant delivery was carried out according to the information on demand, preliminary information was issued (Hill & Fredendall, 2016).

The concept of supply chain management and its structure is considered. The review of various approaches to the modeling of individual stages of supply chain management, revealed some advantages and disadvantages of existing models.

Supply chain management, material flow, suppliers, manufacturers, contractors, modeling, and method, decision-making

Over the past two or three years, a relatively small but important class of information systems has been formed. The need for such solutions is caused by the fact that SCM-systems (Supply Chain Management) create and record a huge amount of data, but it is not so easy to apply it for decision-making. In this sense, SCM-systems are similar to ERP-systems (Coyle, & Gibson, 2016). They are exactly the same, initially focused on supporting transactions and accounting for primary transactions. And, as in the case of ERP-systems, when using them for decision-making and optimization of work, analysis, aggregation and presentation of these data are required. Therefore, Supply Chain Intelligence (SCI) systems, a kind of BI solutions specifically for supply chains, became the "latest innovation" in the field of supply chain technologies (Hill & Fredendall, 2016).

Obviously, even before the advent of the SCI term, the data collected in the supply chains was recalculated, the figures analyzed, and so information was created to make decisions on the modernization of the supply chain. As a rule, for this purpose, one or another of the analytical functions of SCM-systems, which are in all systems, were used. For a while this data was enough. However, gradually the range of categories of information that had to be analyzed expanded. It took a very diverse data, allowing you to assess how the entire supply chain is capable of delivering cost-effective products. It was necessary to work with the "what if" scenarios for the modernization of key operations in the supply and distribution sectors. Traditional SCM-systems could not support these requests (Hill & Fredendall, 2016).

The second most important reason for the emergence of a new class of solutions is the growing importance of supply chains in the business of enterprises. Many world companies are gradually shifting part of their functions - from production to customer service - to the shoulders of contractors, often as branches or business partners located in other regions where such outsourcing is justified. Apparently, this trend has intensified in the current economic downturn, when companies are increasingly expanding the geography of their activities, seeking to reduce costs and increase profitability. This globalization has led to the dispersal of operations such as supply, production and distribution of goods and customer service, among many organizations around the world. As a result, the importance of supply chains has greatly increased. Recently, supply chains have dramatically expanded their "influence" in the enterprise (Kerzner & Kerzner, 2017).

However, there is a need for such an assessment tool (integrated model or complex of models) that would allow us to evaluate the influence of each parameter separately, and to consider complexly the influence of a number of parameters both at individual stages of supply chain management and the whole process as a whole. Models is that the individual components of the supply chain management process are both well-structured (formalizable) optimization tasks, so weakly and unstructured tasks and decision-making. For example, in the first stage (planning), among other methods and models of demand forecasting are used. When forecasting the volume of sales, there are two approaches (depending on the type of consumer) -the calculation of the volume of sales based on the end use of their product (for consumer enterprises) and on the "market share of the enterprise" (for end users). Methods for forecasting sales can be summarized in two groups: based on expert assessments and economic-statistical (Monczka & Patterson, 2015). These methods are used in practice in various fields, they involve studying the opinions of specialists of manufacturing enterprises and consumers of products, trade and intermediary firms, retail enterprises, consulting organizations on the possible volumes of sales of the enterprise's products in the planned period (Monczka & Patterson, 2015). On their basis, expertly defined three types of sales forecast: optimistic, pessimistic and rational (probable). Among the simple methods for forecasting sales volume is the calculation of the moving average of sales, the weighted moving average method (exponential smoothing), which introduces weighting coefficients that reflect the measure of the influence of various factors, the "tracking signal" method, which takes into account the forecast error and is calculated by dividing the absolute sum of deviations (without regard to the sign) by the mean On (Kerzner, & Kerzner, 2017). The effectiveness of applying a particular method of forecasting sales depends on the specific conditions and specifics of the economic activities of the enterprise. Demand modeling can be carried out using the methods of correlation and regression analysis, expert assessments, but it is very difficult to obtain the desired accuracy of the forecast of consumer behavior using only the listed methods. It seemed that the models of inventory management were sufficiently developed and successfully applied in supply chain management, but in the authors note the lack of unity in methodological approaches to the calculation of the component of the norms of production reserves.

Conclusion

In the model of formation of the optimal logistic chain on the basis of the EOQ model (Wilson's formula for calculating the optimal order lot) is considered.

The subsystem of transport logistics and the model of forecasting orders related to it are considered. An optimization model of route search is given. To solve the problems, new algorithms are proposed using evolutionary meta-heuristics. In conclusion, a diagram of the transport logistics system for optimizing routes is given.

Forecasting demand in the logistics environment of commodity production complexes. There are various approaches to forecasting demand, but any forecast procedure can be attributed to one of four: The evaluation approach is to assume that the right answer to someone is known and this expert can be asked (Wang & Papadopoulos, 2016).

The experimental approach to forecasting demand works well if the product is a novelty and we do not have information on which to base the forecast. The approach is to conduct an experiment on a small group of consumers, measure the demand and extrapolate the results to groups of a larger size (Fahimnia & Sarkis, 2015).

The causal approach is based on the assumption that consumers buy goods for a reason that can be used to forecast demand.

The approach of time series. Forecasting using time series is fundamentally different from the first three approaches. The essence of the approach is an understanding (or assumption) of the fact that the level of demand varies in a characteristic way and these characteristic changes are repeated - at least approximately (Monczka & Patterson, 2015). If it is possible to identify and describe these general patterns and trends without regard to their causes, predictions can be made on this description. This approach will be considered in more detail.

Logistics is the management of the whole process of the movement of materials, goods and products to the firm, inside and out of it. Purchasing logistics - management of supplies of materials from suppliers. Material management describes the movement of materials, semi-finished products and components outside and inside the company. The physical distribution considers the movement of finished (stored) products from the firm to the consumer. The most common situations are related to the transportation (delivery) of goods, materials and components. The forecasting systems precede the supply chain itself. To guess at this stage is a guarantee of success with a big profit, and underestimation in forecasting can lead to loss of profit (Ho & Talluri, 2015).

In this article, we confine ourselves to setting the tasks of forecasting and transportation arising in the logistics environment of commodity-production complexes. The meaningful formulation of these problems and other issues of the supply chain are well studied. At the same time, the modules of logistics systems are often developed on an intuitive level, without the use of rigorous calculations and optimization methods. Inclusion of un-adapted mathematical models and methods also does not bring the desired results. Thus, the development of logistics-oriented methods for solving optimization supply problems and physical distribution in the overall supply chain is required.

Production and planning management in inventory control should be done by every company. It is clear that the technology itself is secondary. It is just a means of speeding up the answer to the question posed. But the questions, which the top managers of companies want to receive answers to, have become increasingly complex and complex. For example:

"At what stage of the value chain does the value of the product increase?"

"What alternative sources of supply and production can we use to reduce the cost of goods sold?"

"What makes our partners in the supply chain raise their payment for their services?"

Aggregate planning

The most difficult task in creating such a framework for the analysis of supply chains is to aggregate data from multiple sources. Before starting any SCI analysis, data from multiple sources (ERP and SCM applications and life cycle management solutions) must be extracted and brought to a single format. The main difficulty of such integration is the need to combine a huge amount of structured and unstructured data produced and received by a modern enterprise. Structured data usually comes from transactional systems (Rozali & Klemeš, 2016). It can be files in EDI, XML or simple ASCII format. The task of data integration for SCI is even more difficult, as the data is "sucked" from all sources of data within the enterprise as well as the entire supply chain.

Obviously, the received answers deeply influence the structure of the enterprise and can have far-reaching consequences. But for the answer it is necessary to analyze almost all possible data on the supply chain. In addition, at the moment, SCI technologies in the "competence" area include not only the supply chain itself, but also production as such - naturally, in some generalized and aggregated form. This turned out to be absolutely necessary for answering such complex questions. It is clear that traditional SCM-systems can not satisfy these requests. As a result, the class of SCI-systems was crystallized.

Supply chain management is a generalized concept of logistics from the moment of forecasting the order of goods until they reach the final consumer. Inventory is very important in planning. This concept defines the upper level of logistics - an integrated supply chain [2]. It relies heavily on retailers, supermarket chains, specialized distribution centers, processing enterprises. In large-scale industry (production of large-sized machines, mobile machinery, processing machinery, etc.), it is more common to deal with systems of design automation and technological preparation of production equipped with automated equipment.

Conclusion

A clear understanding of the business question to be answered is limited by efforts to integrate and clean up data when selecting SCI technology and at the implementation stage. This is the most important moment, because the business task that needs to be solved clearly shows the difference between the SCI solution and the typical transaction processing application. The latter processes hundreds and millions of transactions per day and must provide 100% accuracy of the data. In the SCI solution, priorities are different (Zhong & Xu, 2015). In contrast, SCI-applications can be used a few times a year. And getting an answer to SCI questions at a reasonable time is more important than providing an absolutely accurate data integration.

Prior to the start of the SCI project, specialists in specific subject areas, such as finance, accounting and strategic planning, should clearly set the task and determine what level of detail is reasonably sufficient to obtain an operational response to the question posed to the SCI system. Not just deliveries finally, the last trend I wanted to mention is the extension of the scope of SCI systems to the entire value chain. Some experts and suppliers of SCI-solutions believe that SCI-systems should cover, in addition to such traditional stages of the chain as supply and distribution, also production (I already wrote about it above), and even the development of a product or service. This will allow you to accurately determine the value added at all stages of the enterprise. The idea is undoubtedly interesting. This kind of analysis, for example, can lead to the idea of ??getting rid of production assets and focusing on product development - an approach that is very popular now in many industries. In carrying out this truly comprehensive analysis, companies are increasingly discovering that product development and innovation provide the basic consumer value of the product, while production facilities and equipment do not allow the customer's needs to be met with sufficient profitability and efficiency. This is what leads to the desire to learn to ask the right business questions. However, it is not a very good idea to take all these issues into a very new and "raw" class of SCI solutions. At least for now. Although the potential of SCI technology in transforming a modern enterprise is enormous.

However, with a well-considered approach to providing their information, all risks from integration between counterparties of different supply chains are minimized, and the benefits of integration with counterparties from other supply chains make it possible to significantly improve the efficiency of each individual company, increasing its competitive advantages. A model of a unified information system that links contractors of various supply chains to the FMCG sector is presented.

Keywords: supply chain integration of macro level FMCG interaction of counterparties level of reserves level of returns effect of whip bullwhip effect lever of logistics model DuPont information system

Let's imagine the actual issues for many modern FMCG-sector companies, the solution of which would allow to obtain a significant economic effect.

To increase the effectiveness of marketing initiatives for the lack of statistical data on the effectiveness of a particular action, distributors are compelled to independently analyze the effectiveness of initiatives or order costly reports from companies specializing in this, such as AC Nielsen.

References

Kerzner, H., & Kerzner, H. R. (2017). Project management: a systems approach to planning, scheduling, and controlling. John Wiley & Sons.

Monczka, R. M., Handfield, R. B., Giunipero, L. C., & Patterson, J. L. (2015). Purchasing and supply chain management. Cengage Learning.

Rozali, N. E. M., Alwi, S. R. W., Manan, Z. A., & Klemeš, J. J. (2016). Process Integration for Hybrid Power System supply planning and demand management–A review. Renewable and Sustainable Energy Reviews, 66, 834-842.

Zhong, R. Y., Huang, G. Q., Lan, S., Dai, Q. Y., Zhang, T., & Xu, C. (2015). A two-level advanced production planning and scheduling model for RFID-enabled ubiquitous manufacturing. Advanced Engineering Informatics, 29(4), 799-812.

Brahimi, N., Aouam, T., & Aghezzaf, E. H. (2015). Integrating order acceptance decisions with flexible due dates in a production planning model with load-dependent lead times. International Journal of Production Research, 53(12), 3810-3822.

Christopher, M. (2016). Logistics & supply chain management. Pearson UK.

De Neufville, R. (2016). Airport systems planning and design. Air Transport Management: An International Perspective, 61.

Hill, E., & Fredendall, L. D. (2016). Design and Management of the Transformation................... Review of Volume and Layout Decisions Manufacturing Planning and Control Systems Influence of Supply Chain on Demand Management Detailed Material Planning Detailed Capacity Planning Just-in-Time (JIT). In Basics of Supply Chain Management (pp. 129-156). CRC Press.

Mangan, J., Lalwani, C., & Lalwani, C. L. (2016). Global logistics and supply chain management. John Wiley & Sons.

Sorbi, S., Zorrieh, S., Jalilian, I., & Sani, M. M. S. (2017). THE ROLE OF MANAGEMENT IN THE EVOLUTION OF THE COMPANY'S SUPPLY CHAIN. European Journal of Management and Marketing Studies.

Coyle, J. J., Langley, C. J., Novack, R. A., & Gibson, B. (2016). Supply chain management: a logistics perspective. Nelson Education.

Fahimnia, B., Tang, C. S., Davarzani, H., & Sarkis, J. (2015). Quantitative models for managing supply chain risks: A review. European Journal of Operational Research, 247(1), 1-15.

Ho, W., Zheng, T., Yildiz, H., & Talluri, S. (2015). Supply chain risk management: a literature review. International Journal of Production Research, 53(16), 5031-5069.

Monczka, R. M., Handfield, R. B., Giunipero, L. C., & Patterson, J. L. (2015). Purchasing and supply chain management. Cengage Learning.

Wang, G., Gunasekaran, A., Ngai, E. W., & Papadopoulos, T. (2016). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International Journal of Production Economics, 176, 98-110.

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