Integration of the Food Supply Chain as a Driver of Sustainability: A Conceptual Framework
Abstract
:1. Introduction
2. Theoretical Background
2.1. Enhancing Sustainability across Economic, Environmental, and Social Dimensions
2.2. Efficiency and Integration
3. Methodological Considerations
3.1. Supply Chain Integration Measurement
Author(s), Journal | Criteria/Aspects | Description |
---|---|---|
Molinaro, M.; Danese, P.; Romano, P.; Swink, M. [45] | Top-down integration with suppliers | Coordination and collaboration with suppliers of raw materials, goods, and services. |
Peppers, D.; Rogers, M. [48] | Bottom-up integration with customers | Integration with customers who receive goods and services produced at various stages of the supply chain. |
Patterson, M.G.; West, M.A.; and Wall, T.D. [52], Feyissa, T.T.; Sharma, R.R.K.; and Lai, K.-K. [53] | Internal integration | Coordination within a single company, encompassing various internal functions. |
Ralston, P.M.; Blackhurst, J.; Cantor, D.E.; Crum, M.R. [54] | External integration | Integration with external partners beyond immediate suppliers and customers. |
3.2. Selected Approaches for Efficiency Assessment of the Supply Chain
Approach | Financial Indexes Approach | Deterministic Approach | Multi-objective Evolutionary Algorithm (EA) | Deterministic and Stochastic Approach | Deterministic and Stochastic Multicriteria Acceptability Analysis (SMAA) | Stochastic Approach in Connection to Integration Degree |
---|---|---|---|---|---|---|
Methods of efficiency assessment | Economic value added (EVA), profit and market capitalization | Data envelopment analysis (DEA) | Multi-objective evolutionary algorithm (EA) to solve a bi-objective mixed-integer nonlinear programming model for supply chain design with location-inventory decisions and supplier selection. | Data envelopment analysis (DEA) and the stochastic frontier analysis (SFA) | The two-stage SMAA-DEA stochastic multicriteria acceptability analysis (SMAA) technique and data envelopment analysis (DEA) methodology. | The SFA method, with extension with the supply chain integration degree measure (SCIDM), is used. |
Variables/Sample | The framework consists of seven steps: map the supply chain, analyze each link, develop profit and loss statements, realign supply chain management processes, align non-financial factors with profit and loss, compare across firms, and replicate US industry. | Large-scale longitudinal dataset of listed US companies (2007–2015) that covers 13 manufacturing industries. The implications of the 2008/2009 financial crisis for operational SC performance was also included. | The supply chain has four echelons: suppliers, plants, distribution centers, and retailers. The decision variables are the opening of plants and distribution centers and the flow of materials between the different facilities, considering a continuous review inventory policy. | Review of 40 studies on the TE measurement of seaports. | A total of 27 supply chains, comprising the supplier (process 1) and the manufacturer (process 2), are included for efficiency evaluation and ranking of two-stage supply chains (e.g., supplier–manufacturer) with stochastic criteria values. | The supply chain integration degree measure includes integration with both suppliers and customers. |
Notes on applied approach | The link-by-link approach provides means for aligning performance from point-of-origin to point-of-consumption, with the overall objective of maximizing shareholder value for the total supply chain, as well as for each company. | The method captures operational SC performance using accounting-based top-level metrics that are consistent with the framework of value-based SCM. The paper outlines how and to what extent operational SC performance contributes to firm value. | The conflicting objectives include minimizing total costs on the entire chain, and maximizing a combined value of overall equipment effectiveness from suppliers. The potential for solving large-scale and complex combinatorial optimization problems using EAs is high. | The results compare fixed effects against a random-effects regression model, where the latter assumes that the individual study-specific characteristics matter, while the former assumes that there is one general tendency across all studies. | The developed model extends existing two-stage DEA models to handle uncertain or imprecise inputs, intermediate measures, and outputs using stochastic distributions. Variable process weights are allowed in the model, and any prior preference information on processes is not needed. | This is a three-step procedure: (1) the differentiation of integration direction (downward, upward, or in both directions); (2) the determination of the range of integration, i.e., areas of cooperation; (3) the analysis of the level of integration, which can be described as the degree of development of integration activities, in both directions, for the supplier (ILS) and the customers (ILC), respectively. |
Critics/Research gaps | An advanced, complex approach. Possible problems with the access/collection of datasets to assess the performance of several supply chain participants might occur. | Small number of analyzed units, which is same for both the nonparametric method and the DEA. | The model should be extended to other industrial applications, such as the manufacturing of electronics and appliances. For future studies related to the model, stochastic values can be introduced in facility capacities (dispatch and inventory), transportation costs, delivery times, etc. | This result is in contrast to those of other studies which showed that panel data produce higher MTE scores than cross-sectional data. | The relationship between the chain and the processes in two-stage and general SCs requires further study. The application to more real-world cases, such as business firms, government agencies, and educational institutions, is needed. | Possible problems with the access/collection of a detailed dataset to assess performance might occur. |
3.3. The SFA Method Extended by an External Variable—The Supply Chain Integration Degree Measure
4. Empirical Visualization Using the Extended SFA Method
4.1. Dataset
4.2. Assessment of Integration and Efficiency
5. Discussion
5.1. Limitation of the Study
5.2. Development of Future Studies
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Conflicts of Interest
References
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Limitation of Market Weaknesses through Integration | Pillar of Sustainability |
---|---|
Reduction of transaction costs | Economic sustainability |
Mitigation of uncertainty, opportunism, and trust deficits | Economic sustainability |
Reduction of information asymmetry | Environmental sustainability |
Reduction of external effects | Environmental sustainability |
Reduction of social inequality | Social sustainability |
Information flow | Social sustainability |
Author(s), Journal | Criteria/Aspects | Description |
---|---|---|
Sohrabpour, V.; Oghazi, P.; Olsson, A. [55] | Material flow | Strategies to bridge the gap between packaging design and development and supply chain requirements. |
Blanchard, D. [56] | Planning and control | Application of the supply chain operations reference (SCOR) model to integrate business process reengineering with benchmarking, best practices, and process measurement for comprehensive supply chain project execution. |
Braunscheidel, M.J.; Suresh, N.C.; Boisnier, A.D. [57] | Organization | Analysis of organizational culture characteristics that influence supply chain integration and delivery performance. |
Qrunfleh, S.; Tarafdar, M. [58] | Information flow | Identification of IS application portfolios that enhance benefits from specific supply chain strategies, with tools for measuring SC strategies and IS strategies. |
Kim, Y.H; Schoenherr, T. [59] | Product development/innovation | Examination of how customer and supplier integration activities, whether focused on products or processes, impact contract manufacturing returns. |
Subsector | Period 1 | Period 2 | Period 3 | Period 4 | Period 5 | Period 6 |
---|---|---|---|---|---|---|
milk processing | 137 | 160 | 103 | 155 | 141 | 122 |
meat processing | 202 | 210 | 195 | 208 | 204 | 197 |
fruit and vegetable processing | 138 | 148 | 125 | 130 | 132 | 119 |
Sector | Variable/Parameter | Period 1 | Period 2 | Period 3 | Period 4 | Period 5 | Period 6 |
---|---|---|---|---|---|---|---|
dairy processing | Intercept/b0 | 0.74 | −0.03 | 2.47 | 1.08 | −0.95 | 2.19 ** |
X1/b1 | 0.24 *** | 0.20 | 0.47 | 0.25 * | 0.24 *** | 0.22 *** | |
X2/b2 | 0.57 *** | 0.70 *** | 0.26 * | 0.57 *** | 0.68 *** | 0.44 *** | |
Intercept/δ0 | 4.99 *** | 3.62 *** | 5.68 | 4.70 *** | 3.57 *** | 4.00 *** | |
SCIDM/δ1 | −0.03 *** | −0.01 *** | −0.03 *** | −0.02 *** | −0.03 *** | −0.03 *** | |
meat processing | Intercept/b0 | 2.32 *** | 0.48 | 2.14 ** | 1.74 ** | 1.27 * | 1.29 |
X1/b1 | 0.16 ** | 0.20 *** | 0.30 *** | 0.29 *** | 0.27 *** | 0.31 *** | |
X2/b2 | 0.44 *** | 0.58 *** | 0.35 *** | 0.40 *** | 0.46 *** | 0.41 *** | |
Intercept/δ0 | 3.06 *** | 3.33 *** | 3.39 *** | 3.64 *** | 4.00 *** | 3.62 *** | |
SCIDM/δ1 | −0.03 *** | −0.05 *** | −0.03 *** | −0.04 *** | −0.05 *** | −0.04 *** | |
fruit and vegetables processing | Intercept/b0 | −1.93 ** | −1.43 | −1.32 | 0.47 | −0.16 | 0.49 |
X1/b1 | 0.27 *** | 0.14 *** | 0.33 *** | 0.27 *** | 0.11 | 0.26 *** | |
X2/b2 | 0.74 *** | 0.83 *** | 0.66 *** | 0.56 *** | 0.75 *** | 0.57 *** | |
Intercept/δ0 | 3.72 *** | 2.67 *** | 4.99 *** | 3.93 *** | 3.88 *** | 4.40 *** | |
SCIDM/δ1 | −0.03 ** | −0.02 * | −0.06 * | −0.03 ** | −0.02 ** | −0.02 *** |
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Bezat-Jarzębowska, A.; Krieger-Güss, S.; Jarzębowski, S.; Petersen, B. Integration of the Food Supply Chain as a Driver of Sustainability: A Conceptual Framework. Agriculture 2024, 14, 1403. https://doi.org/10.3390/agriculture14081403
Bezat-Jarzębowska A, Krieger-Güss S, Jarzębowski S, Petersen B. Integration of the Food Supply Chain as a Driver of Sustainability: A Conceptual Framework. Agriculture. 2024; 14(8):1403. https://doi.org/10.3390/agriculture14081403
Chicago/Turabian StyleBezat-Jarzębowska, Agnieszka, Stephanie Krieger-Güss, Sebastian Jarzębowski, and Brigitte Petersen. 2024. "Integration of the Food Supply Chain as a Driver of Sustainability: A Conceptual Framework" Agriculture 14, no. 8: 1403. https://doi.org/10.3390/agriculture14081403