Supplier Selection Using Analytical Hierarchy Process at PT. Indolakto

Dairy supply chain is one of food supply chain that has its own uncertainty both in upstream and downstream process due to the durability of product. Dairy market has good demand trend, because the supply is still below the consumption level. Indonesia use imported dairy product rather than use the domestic ones, because the supply of domestic dairy still below the demand. So, there are opportunities for dairy company to compete in this industry and reach competitive advantage by solving the upstream problems. Selecting supplier is one of upstream supply chain area which affected the quality of dairy product and mitigate supply chain risk management from the beginning. This research aim to develop a framework for supplier selection. According to AHP method this research will be determine main criteria by interview, pair wise comparison on developing the AHP, determine sub criteria based on main criteria, and rank the supplier. The result is forming a framework of supplier selection based on company requirements. Also, the main criteria for supplier selection are quality, quantity, delivery, warranty, and pricing with sub main criteria which already deployed. Maltodextrin A will be choose rather than Maltodextrin B. The sensivity analysis also shown that all of criteria were robust

Location of Facility Based on Simulated Annealing and “ZKW” Algorithms

To cope with the facility location problem, a method based on simulated annealing and “ZKW” algorithm is proposed in this article. The method is applied to some real cases, which aims to deploy video content server at appropriate nodes in an undirected graph to satisfy the requirements of the consumption nodes with the least cost. Simulated annealing can easily find the optimum with less reliance on the initial solution. “ZKW” algorithm can find the shortest path and calculate the least cost from the server node to consumption node quickly. The results of three kinds of cases illustrate the efficiency of our method, which can obtain the optimum within 90 s. A comparison with Dijkstra and Floyd algorithms shows that, by using “ZKW” algorithm, the method can have large iteration with limited time. Therefore, the proposed method is able to solve this video content server location problem

Supplier Selection And Supplier Performance Evaluation At PT. Indolakto

Dairy supply chain is one of food supply chain that has its own uncertainty both in upstream and downstream process due to the durability of product. Dairy market has good demand trend, because the supply is still below the consumption level. Indonesia use imported dairy product rather than use the domestic ones, because the supply of domestic dairy still below the demand. So, there are opportunities for dairy company to compete in this industry and reach competitive advantage by solving the upstream problems. Selecting supplier is one of upstream supply chain area which affected the quality of dairy product and mitigate supply chain risk management from the beginning. This research aim to develop a framework for supplier selection and improve a supplier performance evaluation form. According to AHP method this research will be determine main criteria by interview, pair wise comparison on developing the AHP, determine sub criteria based on main criteria, and rank the supplier. After selecting the supplier, this research conduct interview for determining main and sub criteria, developing the AHP method with pair wise comparison and forming supplier performance evaluation. The result is forming a framework of supplier selection and forming supplier performance evaluation form based on company requirements. Also, the main criteria for supplier selection are quality, quantity, delivery, warranty, and pricing with sub main criteria which already deployed. For supplier performance evaluation, there are four main criteria which are quality, quantity, delivery and warranty. Maltodextrin A will be choose rather than Maltodextrin B. The sensivity analysis also shown that all of criteria were robust

Conceptual framework for designing agri-food supply chains under uncertainty by mathematical programming models

This is an Author's Accepted Manuscript of an article published in [include the complete citation information for the final version of the article as published in the International Journal of Production Research (2018) © Taylor & Francis, available online at: http://doi.org/10.1080/00207543.2018.1447706[EN] Agri-food sector performance strongly impacts global economy, which means that developing optimisation models to support the decision-making process in agri-food supply chains (AFSC) is necessary. These models should contemplate AFSC¿s inherent characteristics and sources of uncertainty to provide applicable and accurate solutions. To the best of our knowledge, there are no conceptual frameworks available to design AFSC through mathematical programming modelling while considering their inherent characteristics and sources of uncertainty, nor any there literature reviews that address such characteristics and uncertainty sources in existing AFSC design models. This paper aims to fill these gaps in the literature by proposing such a conceptual framework and state of the art. The framework can be used as a guide tool for both developing and analysing models based on mathematical programming to design AFSC. The implementation of the framework into the state of the art validates its. Finally, some literature gaps and future research lines were identified.This first author was partially supported by the Programme of Formation of University Professors of the Spanish Ministry of Education, Culture, and Sport [grant number FPU15/03595]; the partial support of Project 'Development of an integrated maturity model for agility, resilience and gender perspective in supply chains (MoMARGE). Application to the agricultural sector.' Ref. GV/2017/025, funded by the Generalitat Valenciana. The other authors acknowledge the partial support of Project 691249, RUC-APS: Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems, funded by the EU under its funding scheme H2020-MSCA-RISE-2015.Esteso, A.; Alemany Díaz, MDM.; Ortiz Bas, Á. (2018). Conceptual framework for designing agri-food supply chains under uncertainty by mathematical programming models. International Journal of Production Research. 56(13):4418-4446. https://doi.org/10.1080/00207543.2018.1447706S44184446561

Gresilient supplier assessment and order allocation planning

Companies are under pressure to re-engineer their supply chains to ‘go green’ while simultaneously improving their resilience to cope with unexpected disruptions where the supplier selection decision plays a strategic role. We present a new approach to supplier evaluation and allocating the optimal order quantity from each supplier with respect to green and resilience (Gresilience) characteristics. An integrated framework that considers traditional business, green and resilience criteria and sub-criteria was developed, followed by a calculation of importance weight of criteria and sub-criteria using analytical hierarchy process (AHP). We evaluate suppliers using the technique for order of preference by similarity to ideal solution (TOPSIS). The obtained weights from AHP and TOPSIS were integrated into a developed multi-objective programming model used as an order allocation planner and the ε-constraint method was used to solve the multi-objective optimization problem. TOPSIS was applied to select the final Pareto solution based on its closeness from the ideal solution. The applicability and effectiveness of the proposed approach was illustrated using a real case study through a comparatively meaningful ranking of suppliers. The study provides a helpful aid for managers seeking to improve their supply chain resilience along with ‘go green’ responsibilities

Modelling supply chain network for procurement of food grains in India

The procurement of food grains from farmers and their transportation to regional level has become decisive due to increasing food demand and post-harvest losses in developing countries. To overcome these challenges, this paper attempts to develop a robust data-driven supply chain model for the efficient procurement of food grains in India. Following the data collected from three leading wheat producing Indian regions, a mixed-integer linear programming model is formulated for minimising total supply chain network costs and determining number and location of procurement centres. The NK Hybrid Genetic Algorithm (NKHGA) is employed to cluster the villages, along with a novel density-based approach to optimise the supply chain network. Sensitivity analysis indicates that policymakers should focus on creating an adequate number of procurement centres in each surplus state, well before the start of the harvesting season. The study is expected to benefit food grain supply chain stakeholders such as farmers, procurement agencies, logistics providers and government bodies in making an informed decision

A hybrid MCDM-FMOO approach for sustainable supplier selection and order allocation

The growing interest in sustainability increases the challenges for decision makers in selecting the sustainable suppliers in which consider economic, environmental and social aspects. Particularly, decision makers are being increasingly motivated to improve their supply chain activities in coping efficiently with the objectives of sustainable development. Where the era of sustainability threatens the current supply chain partners to either cope with the new regulations of sustainability or leave the field for new players. Notwithstanding, most of the recent studies considered economic and green criteria in handling sustainable supplier selection and order allocation (SSS/OA) problems overlooking the social criteria which represents the third pillar of sustainability. This work aims at putting forward a hybrid Multi Criteria Decision-Making (MCDM)-Fuzzy Multi-Objective Optimization (FMOO) approach for a sustainable supplier selection and order allocation problem by considering economic, environmental and social criteria. Thus, an integrated Fuzzy AHP-Fuzzy TOPSIS is proposed to assess and rank suppliers according to three sets of criteria (i.e. conventional, green and social). A Multi-Objective Optimization Model (MOOM) is developed for choosing suppliers and allocating the optimal order quantities. To cope with the multiple uncertainties in the input data, the MOOM is reformulated into a Fuzzy Multi-Objective Optimization Model. The ε-constraint and LP-metrics approaches are used to reveal two sets of Pareto solutions based on the developed FMOO model. Finally, TOPSIS is applied to select the final Pareto solution that is closest to the ideal solution and furthest from the nadir solution. The effectiveness and the applicability of the developed hybrid MCDM-FMOO approach is demonstrated through a case study

Propuestas para reducir la incertidumbre en la cadena de suministro agroalimentaria

[ES] En el presente trabajo se presenta un estudio detallado sobre el sector Agroalimentario desde el punto de vista de su Cadena de Suministro; identificando que eslabones la componen, cuáles son las principales decisiones que deben tomarse, que fuentes y tipos de incertidumbre están asociadas a estas decisiones y cómo el uso del concepto de la Industria 4.0 puede ayudar a reducir el grado de incertidumbre existente y a hacer mucho más eficiente la Cadena de Suministro del sector Agroalimentario. Por lo tanto, este trabajo está dividido en 3 grandes apartados de investigación: el primero relacionado con el estudio y caracterización de las decisiones en la Cadena de Suministro del sector Agroalimentario, el segundo relacionado con la identificación de la incertidumbre y la incidencia que tiene en la toma de decisiones y, el tercero, relacionado con el estudio de la Industria 4.0 ó Internet de las Cosas, sus principales ventajas y algunos ejemplos de aplicación en el sector Agroalimentario, que pueden ayudar a la reducción de la incertidumbre. Finalmente, se hace una asociación de las fuentes de incertidumbre detectadas con las principales decisiones en la Cadena de Suministro para seleccionar las de mayor relevancia; a las cuales se les plantean propuestas de solución a través de aplicaciones prácticas de la Industria 4.0 para el sector Agroalimentario.TFG