Supply Chain Planning Research Articles

Supply chain planning: A case for Hybrid Cross-Docks

This research studies the logistics and inventory planning problem encountered at an oilfield services company whose supply chain consists of suppliers, a Hybrid Cross-Dock (HCD) facility, and production facilities. The HCD provides an option for the company to store inventory for a period of time without incurring inventory holding costs. This research aims to provide solutions that minimizes the total logistics and inventory-related costs, and obtain several managerial insights. The investigation into the computational complexity shows that the problem is NP-hard. Hence, an efficient bin-packing based heuristic is developed to provide good solutions. Furthermore, to account for the demand uncertainty, we develop an effective rolling horizon operating policy. Besides the proposed heuristics, the research also provides insights into the effects of free storage time at the HCD, which can guide the managers in their interactions and negotiations with an HCD operator. We also show that the length of free storage time at the HCD affects the outbound shipment sizes.

Fine-tuned robust optimization: Attaining robustness and targeting ideality

The paper provides a Robust Optimization framework with original concepts and fundamentals that combine ideals from relative regret models and static robust optimization with novel decision-making concepts. The algorithm uses a fine-tuning strategy to attain a robust solution close as possible to an ideal target, while simultaneously guarantees robustness according to a preset trade-off risk. The framework comprises original concepts, a mathematical approach, and an algorithm. Statistical treatment based on the original framework concepts supports short, middle, or long-term decision-making settings. The framework is highly tractable as the algorithm forces the creation of a setting for a robust optimization considering the specified risk. This manuscript provides an in-depth discussion of its philosophy, objective, original concepts, fields of application, as well as statistical and probabilistic fundamentals. The framework can be applied in linear and nonlinear mathematical models if the objective function is monotonic in the domain of the active convex region. In addition, it is needed to follow some axioms stated in the paper. The framework is recommended for discrete and continuous optimization problems, including SDP, LP, NLP, MILP or MINLP. Real-world applications regarding nonlinear models are restrict. Operations research are a great scope of application having a myriad number of linear models. Numerical simulations were done considering a planning of a sales problem, solving several examples to validate the study and to gain a better understanding of the framework. All results have showed high tractability and performance. The paper discusses real-world applications with references in fields such as supply chain planning, design optimization, MPC control theory, and portfolio optimization.

Coordination of Multiechelon Supply Chains Using the Guaranteed Service Framework

Problem definition: We use the guaranteed service (GS) framework to investigate how to coordinate a multiechelon supply chain when two self-interested parties control different parts of the supply chain. For purposes of supply chain planning, we assume that each stage in a supply chain operates with a local base-stock policy and can provide guaranteed service to its customers, as long as the customer demand falls within certain bounds. Academic/practical relevance: The GS framework for supply chain inventory optimization has been deployed successfully in multiple industrial contexts with centralized control. In this paper, we show how to apply this framework to achieve coordination in a decentralized setting in which two parties control different parts of the supply chain. Methodology: The primary methodology is the analysis of a multiechelon supply chain under the assumptions of the GS model. Results: We find that the GS framework is naturally well suited for this decentralized decision making, and we propose a specific contract structure that facilitates such relationships. This contract is incentive compatible and has several other desirable properties. Under assumptions of complete and incomplete information, a reasonable negotiation process should lead the parties to contract terms that coordinate the supply chain. The contract is simpler than contracts proposed for coordination in the stochastic service (SS) framework. We also highlight the role of markup on the holding costs and some of the difficulties that this might cause in coordinating a decentralized supply chain. Managerial implications: The value from the paper is to show that a simple contract coordinates the chain when both parties plan with a GS model and framework; hence, we provide more evidence for the utility of this model. Furthermore, the simple coordinating contract matches reasonably well with practice; we observe that the most common contract terms include a per-unit wholesale price (possibly with a minimum order quantity and/or quantity discounts), along with a service time from order placement until delivery or until ready to ship. We also observe that firms need to pay a higher price if they want better service. What may differ from practice is the contract provision of a demand bound; our contract specifies that the supplier will provide GS as long as the buyer’s order are within the agreed on demand bound. This provision is essential so that each party can apply the GS framework for planning their supply chain. Of course, contracts have many other provisions for handling exceptions. Nevertheless, our research provides some validation for the GS model and the contracting practices we observe in practice.

Information sharing for replenishment planning and control in fresh food supply chains: a planning environment perspective

This study explores how fresh food processors’ planning environment characteristics (PECs) affect the information sharing in supply chains during replenishment planning and control (RP&C). The research design is a multiple case study covering a triadic supply chain with five fresh food product (FFP) processors, one wholesaler and nine retail stores. The analysis investigates how the product-, demand-, supply- and production-related PECs affect the material requirements planning and master production scheduling at the FFP processors. Further, it investigates how these affect information sharing at a product level. The findings propose a means to differentiate the information sharing in a supply chain at a product level, rather than differentiation based on demand type or processor which currently dominates the field. The study derives propositions regarding appropriate frequency, timing, direction, modality, content and dynamism of information sharing during RP&C.

A core reference ontology for steelmaking process knowledge modelling and information management

Following the trend of Industry 4.0, the business model of steel manufacturing is transforming from a historical inwardly focused supplier/customer relationship to one that embraces the wider end-to-end supply chain and improves productivity more holistically. However, the data and information required for supply chain planning and steelmaking process modelling are normally distributed over scattered sources across organisation boundaries and research communities. This leads to a major problem concerning semantic interoperability. To address this issue, this paper introduces a Common Reference Ontology for Steelmaking (CROS). CROS serves as a shared steelmaking resource and capability model that aims to facilitate knowledge modelling, knowledge sharing and information management. In contrast to most of the existing steelmaking ontologies which merely focus on conceptual modelling, our work pays special attention to the real-world implementation and utilisation aspects of CROS. The functionality and usefulness of CROS is evaluated and tested on a real-world condition-based monitoring and maintenance task for cold rolling mills at Tata Steel in the United Kingdom.

Exploring options for carbon abatement in the petroleum sector: a supply chain optimization-based approach

This work aims to address the problem of green supply chain planning in the petroleum industry. Our primary objective is to minimize the total cost of crude, refinery, and petrochemical sectors and meet environmental regulations. It presents an optimization model for planning the supply chain. Furthermore, the study examines the impact of incorporating investment decisions in different carbon emission reduction options and evaluating the supply chain performance based on the economic and environmental dimensions. A novel mixed-integer linear programming model is developed to assess the impact of introducing a stringent environmental regulation limiting greenhouse gas emissions. Experimental results based on the Libyan petroleum industry are analyzed and demonstrate model capabilities to deal with the trade-off between the total cost and environmental issues. A sensitivity analysis is carried out on some parameters to design a mitigation plan to manage the environmental risks. This study shows that it is possible to reduce carbon emissions by up to 32% if the carbon capture and storage projects are implemented in the petroleum sector. However, if the reduction objective is more than 32% for the next 20 years, it is necessary to implement green (solar) energies in extraction, refineries, and petrochemical plants.

The Outlook of Supply Chain Management Amid Covid 19 Pandemic : An Explorative Analysis of Recent Research

The outbreak of Corona virus pandemic started in China near the end of 2019. Since then, the virus has spread over all corners of the world and has been taking huge toll of people’s life. The nature of contamination and spread of the virus has disrupted all sorts of business and social activities let alone the supply chains. Supply chain professionals and their stakeholders are working hand in hand to tackle the challenges and exploring ways to covert the challenges into opportunities to tap on. Scholars with interest in supply chain management are not only scrutinizing the situation but also taking up research to analyse the situation and provide data driven, knowledge-based recommendations. This research takes an explorative approach to identify the major themes, approaches, findings, recommendations generated by the body of literature focusing on supply chain management issues during and after the COVID 19 pandemic. Recent research articles on the topic are identified and downloaded using key word search from Google Scholar, Taylor & Francis, Harvard Business Publishing, Elsevier, Wiley, Emerald and Springer sites. 20 articles were selected for in-depth analysis based on the following criteria-written in English, relevance to the topic, publication in peer reviewed journals listed in Scopus data base, and accessibility of full paper. The systematic analysis of literature done in this study helps in identification and categorization of the key research in the field in terms of their context, focus, current and potential impacts, prescribed responses, and future outlooks. The study also detects the research gaps and offers direction to advance future research on the topic. Practitioners can use the findings of the study as a reference to devise their supply chain strategies and plan of actions amid COVID 19.
 Social Science Review, Vol. 37(2), Dec 2020 Page 161-181

Estimating Lost Sales for Substitutable Products with Uncertain On-Shelf Availability

Problem definition: We address the problem of how to estimate lost sales for substitutable products when there is no reliable on-shelf availability (OSA) information. Academic/practical relevance: We develop a novel approach to estimating lost sales using only sales data, a market share estimate, and an estimate of overall availability. We use the method to illustrate the negative consequences of using potentially inaccurate inventory records as indicators of availability. Methodology: We suggest a partially hidden Markov model of OSA to generate probabilistic choice sets and incorporate these probabilistic choice sets into the estimation of a multinomial logit demand model using a nested expectation-maximization algorithm. We highlight the importance of considering inventory reliability problems first through simulation and then by applying the procedure to a data set from a major U.S. retailer. Results: The simulations show that the method converges in seconds and produces estimates with similar or lower bias than state-of-the-art benchmarks. For the product category under consideration at the retailer, our procedure finds lost sales of around 3.0% compared with 0.2% when relying on the inventory record as an indicator of availability. Managerial implications: The method efficiently computes estimates that can be used to improve inventory management and guide managers on how to use their scarce resources to improve stocking execution. The research also shows that ignoring inventory record inaccuracies when estimating lost sales can produce substantially inaccurate estimates, which leads to incorrect parameters in supply chain planning.

Marginalization index as social measure for Acetone-Butanol-Ethanol supply chain planning

Most of methodologies for evaluating supply chain have been focused on environmental and economic criteria. Even though, social impact has been addressed in some methodologies, these methodologies have not considered the location where social impact takes place, which is a crucial issue when the social impact is measured. Therefore, the research purpose is to consider the social impact as a function of the supply chain facilities location. This is accomplished through a multi-objective approach for planning of a biomass supply chain considering simultaneously several objective functions: a) the social impact in function the location where it occurs, b) net profit and c) net CO2 emissions. Specifically, proposed mathematical model considers a social objective function based on the marginalization index. Multi-objective approach was addressed via generating several Pareto curves to illustrate the tradeoff between the considered objectives. Maximum reached profit was around $US 13,572 Million per year that can be obtained with two different pairwise analysis. Nevertheless, if the social benefit is maximized, the profit decrease until $US 6000 Million per year. Therefore, results indicate that supply chain entity's location has a crucial effect in the social impact. Additionally, a direct correlation between social functions other objectives was not observed. This approach addressed the lack of studies for the supply chain planning involving social impact functions, which should be multi-factorial. The proposed approach is applied to an important industrial process, the Acetone-Butanol-Ethanol (ABE) process, to contribute to the bioenergy sector developing.

Logistic planning for pharmaceutical supply chain using multi-objective optimization model

PurposeAppropriate logistic planning for the pharmaceutical supply chain can significantly improve many financial and performance aspects. To this aim, a multi-objective optimization model is proposed in this paper that considers different types of pharmaceuticals, different vehicles with determining capacities and multi-period logistic planning. This model can be updated based on new information about resources and newly identified requests.Design/methodology/approachThe main objective function of the proposed model in this paper is minimizing the unsatisfied prioritized requests for pharmaceuticals in the network. Besides, the total transportation activities of different types of vehicles and related costs are considered as other objectives. Therefore, these objectives are optimized hierarchically in the proposed model using the Lexicographic method. This method finds the best value for the first objective function. Then, it tries to optimize the second objective function while maintaining the optimality of the first objective function. The third objective function is optimized based on the optimality of other objective functions, as well. A non-dominated sorting genetic algorithm II-multi-objective particle swarm optimization heuristic method is designed for this aim.FindingsThe performances of the proposed model were analyzed in different cases and its results for different problems were shown within the framework of a case study. Besides, the sensitivity analysis of results shows the logical behavior of the proposed model against various factors.Practical implicationsThe proposed methodology can be applied to find the best logistic plan in real situations.Originality/valueIn this paper, the authors have tried to use a multi-objective optimization model to guide and correct the pharmaceutical supply chain to deal with the related requests. This is important because it can help managers to improve their plans.

Integrating Mathematical and Simulation Approach for Optimizing Production and Distribution Planning With Lateral Transshipment in a Supply Chain

Supply chain planning aims to maximize the chain's profit and find an effective way to integrate production and distribution. A mathematical and simulation-based optimizations are two common disciplines in which this study integrates both of them together to consolidate their advantages. A mathematical model is formulated to find an optimal production-distribution plan. Then, the result is fed into a simulation model operating under uncertainty to verify the feasibility of the plan. Our integrated approach tries to find a feasible plan that satisfies both required customer service level and makespan limitation where safety stock is used to hedge against uncertainties, and lateral transshipment is used for emergency measures against excessive fluctuation of customer demand. A case study that optimizes the profit of an entire chain is used to demonstrate the algorithm. The outcomes of the study show that our proposed approach can yield feasible results (with near or even optimal solution) with much faster computational time as compared to the traditional simulation-based optimization.

A multi-objective model for an integrated oil and natural gas supply chain under uncertainty

The oil and gas networks are overlapped because of the inclusion of associated gas in crude oil. This necessitates the integration and planning of oil and gas supply chain together. In recent years, hydrocarbon market has experienced high fluctuation in demands and prices which leads to considerable economic disruptions. Therefore, planning of oil and gas supply chain, considering market uncertainty is a significant area of research. In this regard, this study develops a multi-objective stochastic optimization model for tactical planning of downstream segment of oil and natural gas supply chain under uncertainty of price and demand of petroleum products. The proposed model was formulated based on a two-stage stochastic programming approach with a finite number of realizations. The proposed model helps to assess various trade-offs among the selected goals and guides decision maker(s) to effectively manage oil and natural gas supply chain. The applicability and the utility of the proposed model has been demonstrated using the case of Saudi Arabia oil and gas supply chain. The model is solved using the improved augmented ε-constraint algorithm. The impact of uncertainty of price and demand of petroleum products on the obtained results was investigated. The Value of Stochastic Solution (VSS) for total cost, total revenue, and service level reached a maximum of 12.6%, 0.4%, and 6.2% of wait-and see solutions, respectively. Therefore, the Value of the Stochastic Solution proved the importance of using stochastic programming approach over deterministic approach. In addition, the obtained results indicate that uncertainty in demand has higher impact on the oil and gas supply chain performance than the price.

Multi-Objective optimization of the Hydrocarbon supply chain under price and demand uncertainty

ABSTRACT The energy sector is known for its enormous investments, despite the erratic behaviour of the oil market (e.g. changes in crude oil prices). Therefore, strategic and tactical planning of the Hydrocarbon supply chain (HCSC), considering market uncertainty, is a significant area of research. HCSC construction involves the integration of crude oil and natural gas supply chains (SCs). In this study, a stochastic multi-objective optimization model is developed for the tactical planning of HCSC. The model considers price and demand uncertainty by formulating a two-stage stochastic programming model. Financial objectives are considered in terms of cost minimization and revenue maximization, while a non-financial objective is considered in terms of depletion rate minimization (i.e. reserves sustainability maximization). The model assists the decision-maker in quantifying the amount of production required to meet demand under different scenarios. Furthermore, the proposed model assists in evaluating the trade-offs among alternatives. A real-world HCSC is used to elucidate the practicability of the model, and some managerial insights are derived by conducting a sensitivity analysis. For instance, production can be reduced during high demand periods to maintain enough reserves, and the excess demand can be satisfied from the outside market based on medium-term contracts.

Design and Planning of The Porang Supply Chain in South Sumatra

The length of the existing porang (Amorphopallus Muelleri B) supply chain is causing a problem on porang farmers in South Sumatra. The purpose of this research was to construct a P4S Karya Tani porang supply chain with two evaluation steps. First beginning, identify the potential demand for porang in South Sumatra from either the demand and supply perspective. Second, consider the challenges of maximizing market demand in the porang supply chain in South Sumatra. The research employed a qualitative descriptive method with a philosophical approach to supply chain management theory. Based on the discussion, P4S as evaluated the porang supply chain design, determining that small and large traders must be eliminated as intermediaries, allowing them to sell porang raw materials direct to consumers and agro-industry enterprises that export porang. All elements, including that of the local government, Sriwijaya University, the corporate sector, and banking institutions, should be involved in the P4S porang supply chain planning. This collaboration is crucial in order to connect and transform the paradigm of direct sales to consumers, enabling P4S porang farmers to sell to the nearest consumer, PT PIP in South Lampung, via a contract farming system.

Development of a conceptual model for lean supply chain planning in industry 4.0: multidimensional analysis for operations management

A lean supply chain (LSC) is a set of organizations directly linked by upstream and downstream value streams between processes that work collaboratively to reduce costs and waste. Currently, supply chains (SCs) have been put to the test as the world has had to face a series of unprecedented disruptions in demand and supply caused by the COVID-19 pandemic. In this paper, a detailed study of constructs and multistructural components was carried out to develop a conceptual reference model that merges Industry 4.0 (I4.0) digital technologies with lean manufacturing tools to reduce waste and minimize costs in the lean supply chain planning (LSCP) context. The main theoretical contribution of this conceptual proposal is to establish a structured relation among the lean, agile, sustainable, resilient and flexible paradigms to improve SC performance by implementing I4.0 enabling technologies. The proposed conceptual model, dubbed as LSCP 4.0, is applied and validated with a case study in a large footwear company. It can help decision-makers and researchers to improve the planning and management of digital SC production processes, even with unexpected disruptions.

Exploring the critical success determinants for supply chain management in modular integrated construction projects

PurposeModular integrated construction (MiC) projects are co-created by a network of organizations and players providing different roles, information and activities throughout the supply chains. Hence, a successful delivery of MiC projects can hardly be decoupled from effective supply chain management (SCM). This study investigated the critical success determinants of effective SCM in MiC projects.Design/methodology/approachComprehensive literature research and expert review identified 20 candidate success determinants, which formed the basis for a structured questionnaire survey of experts in eighteen countries. The study computed the mean scores, normalized mean values and significance indices of success determinants for SCM in MiC projects.FindingsThe analysis revealed that design for SCM, effective communication and information sharing, organizational readiness and familiarity with MiC, seamless integration and coordination of supply chain, early involvement of critical supply chain stakeholders and extensive supply chain planning are the top five critical success determinants of effective SCM in MiC projects. The 20 success determinants are categorized into five: project strategy, bespoke competencies, process management, stakeholder management and risk management.Research limitations/implicationsThe study has some limitations. The smaller sample size could affect the generalizability of the results. The generalized analysis of the success determinants overlooked their sensitivities to specific contexts, industry climates and project types.Originality/valueThe study established a novel set of critical success determinants for SCM in MiC projects that have not been explicitly discussed in the MiC success literature and described their hypothetical dynamic linkages. It contributes to a better understanding of how best to manage the MiC project supply chain effectively.

Lassoing the bullwhip effect by applying blockchain to supply chains

PurposeA supply chain (SC) involves many stakeholders, directly or indirectly, for satisfying consumers’ requirements. SC management is restricted by a lack of information sharing among stakeholders as parties of SC do not have direct communication and/or are not willing to share private and competitively sensitive information. In the SC, the bullwhip effect (BWE) is an undesirable phenomenon that aggravates the SC performance and increases the overall cost of SC. The main culprit of BWE is the lack of SC coordination among the parties, which results from wrong and lack of information sharing. Blockchain technology (BT) has the main characteristic of distributed shared ledger that makes all parties in the SC network able to access data. This paper aims to develop a BT model and implement it into the SC.Design/methodology/approachA blockchain is developed consisting four SC stakeholders and an integrated development environment has been used for coding in Python.FindingsThe analysis of the impact of the adoption of BT in SC shows the reduction in BWE.Originality/valueIn SC, BT can be considered as an effective tool to share the demand data among all SC partners. Sharing of such data will improve SC planning and reduce the BWE.

Multi-objective models for biomass supply chain planning with economic and carbon footprint consideration

This paper focuses on designing multi-objective biomass supply chain planning models that aim to simultaneously minimize the total cost and the carbon footprint from the transportation. Stochastic and fuzzy models were developed for making strategic (optimal plant locations) and tactical decisions (material flows, truck types, etc.), while capturing the uncertainty of the demand. An epsilon-constraint method was applied to generate optimal solutions from these models. Managerial insights are provided based on a practical case study at a biomass plant in the Lower Northern region of Thailand; nine biomass plant candidates, nine rice husks suppliers and eight trucks were considered in the case study. A sensitivity analysis has been conducted to compare the results from the two models. The stochastic model can take into account all the scenarios of demand, while the fuzzy model can handle only a certain level of demand defined by a centroid value. The stochastic model needs to take into account a large number of variables and constraints, and therefore, requires more runtime to define an optimal solution, as compared to the fuzzy model. The trade-off between the operating cost and carbon emissions from both the models are provided with managerial insights.

Coronavirus, chip boom, and supply shortage: The new normal for global semiconductor manufacturing

Abstract Over the past 50 years, the semiconductor industry has faced its fair share of difficult challenges. The COVID-19 pandemic the world is currently experiencing has caused the worst downturn since the financial crisis of 2008, devastating global economies. And yet, the semiconductor industry has repeatedly shown incredible resilience in the face of adversity. Despite the pandemic, the market has not experienced such a dramatic upturn since 2003 and as a result, the new market upturn breaks open the fundamental flaws and risks of manufacturing concentration and outsourcing. At a time when the world is precariously dependent on Taiwan for semiconductors, how can the global supply chain forecast, manage, and plan for such sudden shifts in the future? Now more than ever before, advanced technology companies need to keep the finger on the pulse of supply and demand to successfully inform their strategic manufacturing decisions and remain agile to ensure continuity of supply. As chip demand booms in the midst of a supply shortage over the coming months and supply is no longer a given, what does the future hold for manufacturing fabs? Will greenfield become the rule rather than the exception in this new normal? How will the market upturn impact global fab location choices? Reflecting on some 20 years of experience completing international semiconductor manufacturing asset transactions in North America, Europe, and Asia, ATREG Founder, President, and CEO Stephen M. Rothrock will provide insights into the current global manufacturing asset market and how it is likely to evolve as part of the current market upturn.

Global sales and operations planning: A multinational manufacturing company perspective.

The purpose of this paper is to analyse the global Sales and Operations Planning (S&OP) process and investigate the steps to support consolidated business planning in worldwide operations and large-scale supply chains. The paper conducts a case study at a multinational manufacturing company applying an abductive approach. It combines the deductive logic from theory and the inductive logic from field observation in an attempt to elaborate further on theory on global S&OP. The analysis is structured and guided by a novel framework for global S&OP, which is developed based on the theoretical background and the case study findings. The research findings characterise the S&OP process for global operations and identify challenges related to the need to synchronise the subsidiaries’ S&OP efforts worldwide to deal with different contingencies of these subsidiaries, and to manage and analyse a large amount of information gathered. The research reveals how the subsidiaries’ performance is analysed by top executives along the global S&OP process, feeding strategic initiatives in the organisation and identifying business opportunities like benchmarking among subsidiaries, synergies with other management practices, and global gains. This paper offers a novel investigation of the global steps on S&OP in a real-life setting, offering a well-documented characterisation of the process that goes beyond the traditional local approach. Moreover, it is the first study to reveal challenges and expected outcomes of such a global perspective for S&OP. The theoretical advancements of S&OP research offered herein aid scholars, opening avenues for middle-range theorising, highlighting the cross-disciplinary nature of the domain, and discussing the use of concepts from related disciplines like Economics, Psychology, and Information Systems. The research findings can also assist executives, especially from multinational manufacturers, in their efforts to consolidate global planning.