Decentralised planning coordination with quantity discount contract in a divergent supply chain

This paper proposes to solve a supply chain planning problem with realistic features. The problem consists of planning productions, transportations and storage activities in a supply chain at a tactical level on a finite horizon. The main features considered are decentralised decision making and iteration of the planning process on a rolling horizon basis. In each planning process, the actors optimise their local planning and coordinate to achieve a good overall planning. A multi-agent system is used to model such supply chain behaviour. The study is conducted in a divergent two-echelon supply chain with one manufacturer and multiple independent retailers. Coordination is achieved using a standard contract in practice, known as the ‘quantity discount’ contract. The planning framework on the supply chain structure is detailed. Lot-sizing models integrating the quantity discount are presented for the local planning problems. Experimental tests are conducted with three major parameters: quantity discount price, quantity discount breakpoint and rolling horizon length. They are used to determine the quantity discount parameters in achieving the best supply chain profit, and to analyse the increasing profit of the actors. A decision-making tool which is able to consider realistic features of supply chain planning is therefore resulted.     

Decentralised capacitated planning with minimal-information sharing in a 2-echelon supply chain

This paper aims at modelling decentralised planning at the tactical level, with minimal-information sharing coordination, in a 2-echelon supply chain with multiple actors at each echelon. Suppliers manage production and storage at the upstream echelon, while retailers manage transportation and storage at the downstream echelon. The main features of the planning process are (1) decentralisation and coordination using contracts and sharing of only order/supply proposals, and (2) iteration on a rolling horizon. Actor planning is modelled as a capacitated lot-sizing problem on a finite horizon, with the focus on quality of service. The objective is to minimise costs, with a high lost sales penalty if demand is not met. Two other decision problems are pointed out and modelled with Mixed Integer Programming: (1) lost sales allocation between the retailers when their demands cannot be satisfied; and (2) allocation of orders between the suppliers. A multi-agent system combines simulation of the planning process and optimisation of the local decision processes. Several strategies, including retailers’ beliefs about suppliers’ production capacity are proposed and experimentally tested, with two patterns of production capacities. The results compare the proposed allocation strategies and highlight the relevance of the proposed framework for the studied planning problem.     

An integrated decision support system for strategic supply chain optimisation in process industries: the case of a zinc company

We introduce a menu-driven user-friendly decision support system (DSS) for supply chain planning based on optimisation. The DSS is based on a multi-source (supplier), multi-destination (warehouse) network having multiple manufacturing facilities, with multiple materials and multiple storage areas. This integrated supply chain model performs multiple period planning. The use of this DSS requires little knowledge of management sciences tools. We discuss the need for an integrated approach towards supply chain modelling for the process industry. We present the integrated model in the form of a database structure. We validate the model with the real data of a zinc company and demonstrate the impact of optimisation in terms of percentage improvement. The result shows that it is possible to improve unit contribution to profit from 1.89 to 4.66%.     

DETERMINATION OF OPTIMAL ROTATING FLOAT IN A CLOSED LOOP SYSTEM: A CASE STUDY

The present study relates to a transport organisation operating a fleet of aircraft and aims at determination of the optimal spare float of engines. The flow of the engines is viewed as a queueing problem in a closed loop system with variable demand and supply. The servicing facility consists of three stations in tandem. Two cases of the servicing facility operation are investigated, first when no inter-stage queue is permitted, and second when a queue length of one unit is allowed between the first and second stations. The solution is obtained by the Monte Carlo Simulation technique. The results are compared with those obtained from a reliability theoretical approach and conclusions are drawn.     

Novel model and kernel search heuristic for multi-period closed-loop food supply chain planning with returnable transport items

Closed-loop supply chain (CLSC) is of utmost importance to sustainable development and has received increasing attention in recent decades. However, food CLSC with returnable transport items (RTIs) has been rarely studied although its growing applications in practice. This paper aims to investigate a multi-period CLSC planning problem that coordinates the flows of perishable food products and RTIs considering food quality. The objective is to maximise the total profit of the holistic supply chain over a finite planning horizon. To this end, a novel mixed integer linear programming model is first formulated. As the problem is proven NP-hard, an improved kernel search-based heuristic is then developed. A real case study deriving from a food manufacturer in China shows the applicability of the proposed model and method. The results indicate that the manufacturer’s profit can be improved by more than 10% with our method. Numerical experiments on randomly generated instances demonstrate that the proposed heuristic can yield high-quality solutions with much less computation time compared with the commercial solver CPLEX and an existing heuristic.     

Information sharing and coordination mechanisms for managing uncertainty in supply chains: a simulation study

The study aims to investigate the effectiveness of information sharing and coordination mechanisms in reducing uncertainty. Supply chains are constantly subject to unpredictable events that can adversely influence its ability to achieve performance objectives. This paper primarily aims at managing uncertainties originating from unexpectedly large demand spikes. The supply chain literature is full of effective supply chain uncertainty management practices. This paper reviews the different practices for improving management of uncertainty and proposes several combinations of information sharing and coordination mechanism for reducing the uncertainty in supply chains. Next, the proposed combinations are tested on the make-to-stock supply chain of a paper tissue manufacturer using an agent-based simulation approach to show how the use of different levels of information sharing and coordination can be effective in managing uncertainty under daily operations facing a huge mismatch of actual and forecast demand. The findings of this research suggest that, a centralised information structure without widespread distribution of information and coordination is not effective in managing uncertainty of supply chain networks, even with increased frequency of information flow. Similarly, coordinating material flows without widespread information sharing does not improve supply chain uncertainty management. Central coordination of material flows with supply chain wide information sharing across different members is found to be essential in managing supply chains effectively under uncertainty.     

An approach to integrate production-transportation planning and scheduling in an aluminium supply chain network

This article is based on a real-life problem of a global aluminium supply chain network driven by an aluminium smelter. At each echelon of the aluminium supply chain network, several members are involved which are scattered around the world. Producing aluminium begins with bauxite mining. Next, aluminium oxide is made from bauxite and finally aluminium is produced from aluminium oxide. A novel type of mixed-integer decision-making model, including a time-continuous representation of the planning period, is presented. The model enables coordination of production quantities and times of all supply chain members in order to minimise production and transportation costs of the whole supply chain minus bonus payments for early deliveries which are stipulated between the supply chain network and its customers. Material flows can take place with or without temporary storage of intermediate products at supplying and/or receiving sites. Furthermore, relax-and-fix heuristics are presented. A number of randomly generated scenarios are presented to demonstrate that the heuristics can find nearly optimal solutions along with drastically reduced computation times. The relax-and-fix heuristic enables iterative planning between centralised and decentralised decision makers.     

Structure dynamics control approach to supply chain planning and adaptation

Many of the existing methods and models of supply chain planning and control consider only one supply chain structure and assume it's more or less static in nature. In practice, supply chains have a multi-structural semantics and those multiple interrelated structures (organisational, functional, informational, financial, topological, technological, product, and energy structures) are dynamic and subject to many planned and disturbance-based changes. These changes are tackled by control activities which make planning an adaptive process. Although the supply chain optimal planning domain for static structures has been extensively investigated, the domain of planning and control of supply chains with structure dynamics merits more systematic and critical attention. In this study, an original form of supply chain representation as a dynamic system with changing multi-structural characteristics is developed. The structure dynamics control is a dynamic interpretation of the supply chain (re)synthesis process and aims at both advancing the supply chain (re)planning domain and enlarging the scope of the supply chain analysis domain that is currently rather limited. The developed approach is based on an optimal program control theory, active modelling objects, and structural–mathematical concept blended with mathematical programming. In this paper, we describe its basic features and exemplify possible applications and extensions regarding real planning and control problems faced by international supply chains in recent years.     

Automated targeting model for aggregate planning in production and energy supply chains

Aggregate planning aims to maximize profit for a supply chain while satisfying its demand. In this work, the automated targeting model (ATM) that was originally developed for resource conservation network is extended for use in aggregate planning for production and energy supply chains. The ATM is an optimization framework that is based on the insight-based technique of pinch analysis, with the philosophy in setting target(s) ahead of detailed planning. Being an optimization framework, the ATM offers other advantages than conventional pinch analysis technique by incorporating more case-specific constraints, and is able to handle more complex optimization problems. Three literature examples and an industrial polymer production case study are solved to show the robustness of ATM.     

Auction-theoretic coordination of production planning in the supply chain

Most planning and optimization methods in manufacturing logistics assume centralized or hierarchical decision-making using monolithic models. Motivated by the increasing needs to coordinate diverse decision processes and systems, we investigate an auction-theoretic mechanism for production coordination in a supply chain. Our effort focuses on structural mappings between mathematical decomposition and iterative auction mechanisms wherein agents compete based on their local utilities, announced conflict pricing, and production targets. Building upon the rich literature in optimization and auction-theoretic analysis, we investigate the advantages and limitations of this distributed decision scheme on a large set problem in supply chain production planning. Experimental results show that the proposed auction mechanism provides impressive improvement over the traditional monolithic method without significant degradation to the solution quality.     

An Extended Kalman filter for collaborative supply chains

Supply chains can often be complex due to the large mesh of interconnected suppliers, manufacturers, distributors and customers. Recent advances in communication technologies can help participants collaborate across a supply chain. However, the huge amount of data generated can impede effective decision-making, particularly since some data may be incomplete or have errors. Inaccurate estimates of the state of the supply chain system can lead to incorrect decisions, with consequent adverse effects on product availability, lead times and inventory levels. What would be beneficial in overcoming this problem is an approach to obtain a better state estimation of the supply chain system. The paper aims to address this issue by proposing an approach that combines an extended Kalman filter with a network approach that models the supply chain as an abstraction. This approach is termed Augmented Trans-Nets and has several potential advantages: multiple participants in a supply chain can be modelled without undue complexity; and different considerations can be examined, such as cost and lead time. Furthermore, by using this approach, it is relatively straightforward to achieve an improved system estimation, which can help in managing the supply chain effectively.     

Closed loop supply chain network design and optimisation using fuzzy mixed integer linear programming model

Owing to the revolution in sustainable and green manufacturing the production planning and network design of closed loop supply chain concept has got the attention of researchers and managers. In this paper, a multi-product, multi-facility capacitated closed-loop supply chain framework is proposed in an uncertain environment including reuse, refurbish, recycle and disposal of parts. The uncertainty related to demand, fraction of parts recovered for different product recovery processes, product acquisition cost, purchasing cost, transportation cost, processing, and set-up cost is handled with fuzzy numbers. A fuzzy mixed integer linear programming model is proposed to decide optimally the location and allocation of parts at each facility and number of parts to be purchased from external suppliers in order to maximise the profit of organisation. The proposed solution methodology is able to generate a balanced solution between the feasibility degree and degree of satisfaction of the decision maker. The proposed model has been tested with an illustrative example.     

Scheduling and co-ordination of multi-suppliers single-warehouse-operator single-manufacturer supply chains with variable production rates and storage costs

We study a supply chain scheduling and co-ordination problem comprising multiple suppliers, a single warehouse operator, a single manufacturer, and multiple retailers, where the supply chain has limited production capacity that can take only some of the orders from the retailers. For a decentralised supply chain, the manufacturer is a decision maker that selects the orders and aims to maximise its own profit, where the profit is a function of the order storage time and storage quantity, order sequence-dependent weighted storage costs, and idle time of the orders. On the other hand, for a centralised supply chain, a supply chain co-ordinator exists that aims to maximise the profit of the whole supply chain and allocates the profit among the supply chain members. We first formulate the problem as a two-machine common-due-window flow shop scheduling problem. We then develop a theorem and two algorithms to solve the optimal scheduling problems in both the decentralised and centralised supply chains. With these results, we develop a method that can achieve channel co-ordination based on a profit sharing rule, together with an increase in the production rates and a decrease in the storage costs.     

Requirements for forming an ‘e-supply chain’

In today's digital economy, web-based integration of the enterprises to form an e-supply chain is a critical weapon for orchestrating the whole supply chain towards competitiveness. This paper intends to discuss the requirements for forming an e-supply chain from different perspectives, such as integration with the legacy systems, timing and prior presence of ERP (enterprise resources planning) systems, BPR (business process re-engineering) needs of internal and external business processes and business intelligence/decision support needs. A look at technical knowledge and structure to construct an e-supply chain is provided. Challenges involved in forming an e-supply chain are also briefly mentioned as a separate section in this paper. During the study, requirements are gathered by making a review of recent literature.     

Managing demand uncertainty through fuzzy inference in supply chain planning

In this paper, we investigate methods for managing the irregular and uncertain demands involved in supply chain planning. We first build a supply chain planning model based on fuzzy linear programming, which defines demand as a fuzzy parameter. Next, we propose a fuzzy inference approach for converting fuzzy demand into crisp demand. In the proposed fuzzy inference-based approach, judgments of upcoming demand from both internal and external experts are used as input variables to reflect the expected demand irregularity. By adopting fuzzy inference, we can compensate for the limitations of the existing demand treatment approaches, which usually demonstrate poor forecasting performance in cases of irregular demand and thus reduce the accuracy of supply chain planning. To verify the feasibility of the proposed approach, we present an illustrative example of a Korean electronics company.     

煤炭供应链SCOR模型研究

为促使煤炭企业实现从职能管理到流程管理的转变,提升煤炭供应链管理的效率和水平,构建了以煤炭生产商为核心企业的煤炭供应链SCOR模型。模型第1层建立包括计划、采购、生产、配送和退货的流程类别模型,第2层建立每一种流程的计划、执行和支持的详细类别模型。这个模型清晰地描述了以煤炭生产商为核心企业的煤炭供应链内部信息流向,为煤炭供应链信息的高度集成、业务流程的重组与优化进一步打下良好的基础。     

冷鲜鸡供应链过程的品质预测与动态优化

目的建立冷鲜鸡供应链品质预测模型。方法应用安全可靠度对冷鲜鸡供应链中的加工、装卸搬运、运输以及销售环节进行分析和研究,从成本和安全的视角出发,采用变权向量对不同情况下的冷鲜鸡供应链各环节进行动态优化,实现成本把控和品质保障的双平衡,并通过实例说明具体优化过程。结果所建冷鲜鸡供应链品质预测模型是有效的,可通过某个权重参数的调节来实现冷鲜鸡供应链安全可靠度的有效分配。结论该预测模型对冷鲜鸡政策的推广应用及保证食品安全具有一定的实际参考价值。     

面向SCM的生产计划与控制系统总体模型

研究供应链管理环境下企业生产计划与控制方法 ,分析了供应链环境下生产计划与控制系统的几个概念扩展 ,提出了生产组织新模式。探讨了新生产模式的计划信息组织与决策过程的几个特特征 ,提出生产计划与控制的总体模型 ,分析了模型的特点     

Decentralized supply chain coordination through auction markets: dynamic lot-sizing in distribution networks

As supply chains become bigger and dynamically structured involving multiple organizations with different interests, it is impossible for a single organization to control a whole supply chain. So, decentralization of decision rights is an inevitable facet of managing modern supply chains. However, controlling in a decentralized mode can fail without appropriate coordination mechanisms. When the agents of a supply chain are not willing to cooperate in terms of information sharing, the whole supply chain can exhibit expensive inefficiencies such as the bullwhip effect. In this paper, we design a trustworthy decentralized coordination mechanism for dynamic lot-sizing in distribution networks, as an effort to motivate information sharing. We design multiple auctions and each auction coordinates the plans of a supplier and its customers by trading the right of managing the supplier's inventory plan. The coordination mechanism makes a reasonable profit and the profit is divided in an agreeable way. Also, the profits are actual because of the capability of supporting a truth-telling environment. Therefore, the designed coordination mechanism is trustworthy in that it guarantees the right benefits to all the agents in return for information sharing.     

The impact of supply chain analytics on operational performance: a resource-based view

This study seeks to better understand the role of supply chain analytics (SCA) on supply chain planning satisfaction and operational performance. We define the architecture of SCA as the integration of three sets of resources, data management resources (DMR), IT-enabled planning resources and performance management resources (PMR), from the perspective of a resource-based view. Based on the data collected from 537 manufacturing plants, we test hypotheses exploring the relationships among these resources, supply chain planning satisfaction, and operational performance. Our analysis supports that DMR should be considered a key building block of manufacturers’ business analytics initiatives for supply chains. The value of data is transmitted to outcome values through increasing supply chain planning and performance capabilities. Additionally, the deployment of advanced IT-enabled planning resources occurs after acquisition of DMR. Manufacturers with sophisticated planning technologies are likely to take advantage of data-driven processes and quality control practices. DMR are found to be a stronger predictor of PMR than IT planning resources. All three sets of resources are related to supply chain planning satisfaction and operational performance. The paper concludes by reviewing research limitations and suggesting further SCA research issues.