The performance impact of implementing Web-based e-procurement systems

As Web-based e-procurement has become an important business avenue for the improvement of inter-organisational process efficiency, its implementation for purchasing direct materials nevertheless implies establishing collaboration mechanism among supply chain participants based on the adopted Internet based infrastructure. However, the value of Web-based procurement to the supply chain participants remains an arduous task for researchers. This study proposes a Web-based e-procurement impact model based on supply chain orientation, which includes both operational and strategic impacts. Specifically, the strategic dimension is about partner relationship, and the operational efficiency dimension includes supplier performance, buyer performance, process integration, and process automation. To prove the proposed model's contribution, a questionnaire survey was conducted on 137 firms in Taiwan who have all participated in e-procurement related projects. The results verify that the electronic execution of purchasing activities improves both the operational efficiency dimension and the strategic dimension. Furthermore, partnership has a positive impact on supplier performance and buyer performance.     

Leagile supply chain: design drivers and business performance implications

In the context of a dynamic and hypercompetitive business environment, effective supply chain design helps organisations to align resources for improved flow of products and services and satisfy customers’ diverse needs. Scholars have proposed several mutually exclusive supply chain designs such as efficient versus responsive, and lean versus agile. Quantitative testing has revealed that supply chain designs of many firms do not match with what was conceptually expected. To address this mismatch, in this study, a new approach to supply chain leagility is introduced and the impact of uncertainty as the key design driver of supply chains on leagility is investigated. The partial least squares (PLS) was employed to analyse data collected from 299 Australian firms by administering a structured questionnaire. Results indicate that higher performance is achievable on minimising the deviation from a balanced supply chain in which aspects of both leanness and agility are equally embedded. Further, the level of uncertainty directly and positively affects the Deviation from Leagility (DFL) index.     

Integrated inventory valuation in multi-echelon production/distribution systems

The pressure to reduce inventory has increased as competition expands, product variety grows, and capital costs increase. This investigation addresses the problem of inventory quantification and distribution within multi-echelon supply chains under market uncertainty and management flexibility. This approach is based on an optimisation model emphasising demand uncertainty and the relevant dimensions of network design as number of echelons, lead time, service level, and cost of processing activities. Overstock quantification enables the understanding of inventory level sensitivity to market uncertainty. A comparison among production sites and storage facilities revealed that higher downstream overstock levels decrease upstream echelons of uncertainty exposition. The contribution of this study relies on management's ability to establish inventory targets for each stocking point according to risk exposure and to promote the optimisation of working capital. Overall, this investigation increases knowledge related to the treatment of demand uncertainty in flexible and integrated supply chains     

Grammatical evolution in developing optimal inventory policies for serial and distribution supply chains

Recently, there has been a growing literature on biologically inspired algorithms, particularly genetic algorithms and genetic programming, applied to supply chain modelling and inventory control optimisation. Due to the rigidity of the genetic algorithms approach, it is difficult to change the underlying model logic and add richness to the supply chain. While genetic programming provides a more flexible approach than that provided by genetic algorithms, to date its application has been limited to small supply chain modelling problems in relation to optimal inventory policies. This research applies Grammatical Evolution, a relatively new biologically inspired algorithm, to the field of supply chain optimisation, employing human readable rules called grammars. These grammars provide a single mechanism to describe a variety of complex structures and can incorporate the domain knowledge of the practitioner to bias the algorithm towards regions of the search space containing better solutions. Results are presented showing Grammatical Evolution is at least competitive in cost terms, and superior in flexibility, with these methods applicable to any supply chain of the serial or distribution type. Furthermore, Grammatical Evolution shows an adaptive ability that augurs well for supply chains in dynamic environments, such as disruption.     

An impact of manufacturing flexibility and technological dimensions of manufacturing strategy on improving supply chain responsiveness: Business environment perspective

The main objective of this research is to investigate the impact of manufacturing flexibility and technological dimensions of manufacturing strategy on responsiveness in the supply chain. Based on the theoretical background of dynamic capability, this study also examines the role of the business environment on the relationship between manufacturing flexibility and supply chain responsiveness. 144 structured surveys were collected and the partial least squares of structural equation modelling approach were utilised for data analysis. The result establishes relationships among various dimensions of manufacturing flexibility. Although the technological dimensions in manufacturing strategy of such advanced manufacturing technology (AMT) and e-procurement do not have any direct impact on new product and market flexibility, they increase supply chain responsiveness, which helps to react quickly against supply chain disruptions. More importantly, the business environment has a moderating effect on the relationship between market flexibility and supply chain responsiveness.     

Entropy-based model for the ripple effect: managing environmental risks in supply chains

Due to increasing diversity and growing size of modern industrial supply chains, today problems of identification, assessment and mitigation of disruption risks become challenging goals of the supply chain risk management. In this paper, we focus on environmental (ecological) risks in supply chains which represent threats of adverse effects on living organisms, facilities and environment by effluents, emissions, wastes, resource depletion, etc. arising due to supply chain’s activities. Harmful environmental disruptions may ripple through the supply chain components like a wave. The paper presents the entropy-based optimisation model for reducing the supply chain model size and assessing the economic loss caused by the environmental risks subject to the ripple effect. A main advantage of the suggested entropy-based approach is that it permits to essentially simplify the hierarchical tree-like model of the supply chain, at the same time retaining the basic knowledge about main risk sources.     

An integrated approach to evaluating the production system in closed-loop supply chains

The increasing demands for environmental resource protection and sustainable development have been forcing enterprises to put sustainable supply chain management on their agendas in recent years. At the same time, intense global competition requires organisations to adopt practices that enable them to provide high-quality products and services. In this paper, we consider the problem of comprehensively evaluating the production system in closed-loop supply chains. We first propose an evaluation framework that consists of economic evaluation, product quality evaluation and ecological evaluation modules. Based on mathematical probability theory and the dynamic characteristics of reverse supply chain logistics, we then focus on the evolution dynamics in the quality evaluation dimension, where the concept of product quality, which builds on the reliability and the time-utility value of a product, is proposed. The basic production evaluation model is then extended to incorporate different sustainable procurement strategies, which take into consideration the trade-offs among cost, environment and quality. An outline and corresponding flow chart of corporate procurement strategy optimisation are provided which allow the proposed evaluation model to be implemented in computer-aided decision-making, further providing decision support for production system and supply chain management. Simulation and case studies are presented to promote a better understanding of the model approach and its managerial implications. Results also suggest that quality characteristics of components and sustainable procurement strategies are two important factors that determine the final production performance and should be paid special attention in closed-loop supply chain practice.     

Quantity discount coordination for allocation of purchase orders in supply chains with multiple suppliers

Studies about supply chain coordination have emphasised maximising the profit of the overall supply chain, but the profit changes of individual members in the supply chain have often been overlooked. It has been shown that profit increment of the whole supply chain may not be beneficial for every individual member. Therefore, the use of quantity discounts to achieve the coordination of a supply chain is discussed in this article. A two-echelon selling system with a single buyer and multiple suppliers is considered to enhance profitability for both sides at the same time. An acceptable quantity discount condition for both the buyer and the suppliers to determine an appropriate order quantity allocation to produce more profits in the supply chain is proposed. Furthermore, the profit distribution between the buyer and each supplier is studied. Finally, the results of the numerical application show that the buyer should focus on managing the procurement costs to decrease the acquiring costs, and the suppliers should focus on the fixed costs of management to reduce the production costs in machine operations.     

A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory industry 4.0

Smart factories Industry 4.0 on the basis of collaborative cyber-physical systems represents a future form of industrial networks. Supply chains in such networks have dynamic structures which evolve over time. In these settings, short-term supply chain scheduling in smart factories Industry 4.0 is challenged by temporal machine structures, different processing speed at parallel machines and dynamic job arrivals. In this study, for the first time, a dynamic model and algorithm for short-term supply chain scheduling in smart factories Industry 4.0 is presented. The peculiarity of the considered problem is the simultaneous consideration of both machine structure selection and job assignments. The scheduling approach is based on a dynamic non-stationary interpretation of the execution of the jobs and a temporal decomposition of the scheduling problem. The algorithmic realisation is based on a modified form of the continuous maximum principle blended with mathematical optimisation. A detailed theoretical analysis of the temporal decomposition and computational complexity is performed. The optimality conditions as well as the structural properties of the model and the algorithm are investigated. Advantages and limitations of the proposed approach are discussed.     

Robust closed-loop global supply chain network design under uncertainty: the case of the medical device industry

Improving performance of global supply chains requires careful consideration of various factors including distance from markets, access to resources, exchange and tax rates, import tariffs, and trade regulations. In this paper, a comprehensive optimization model is proposed to maximise the after-tax profit of a closed-loop global supply chain for medical devices under uncertainty. The uncertainty of the decision-making environment is modelled using the budget of uncertainty concept in interval robust optimization. International financial issues due to the Economic Cooperation Organisation Trade Agreement as well as national regulations including transfer pricing limitations, exchange rates, tax rates, and import tariffs are considered. The proposed model considers various realistic assumptions pertaining to medical device supply chains such as multiple products, multiple periods, multiple echelons, and limited warehousing lifetime. In addition, reverse flows of perished and defective products are considered to address environmental concerns and customers’ requirements as well as to gain economic advantages. To tackle this problem, an efficient memetic algorithm is developed that incorporates adaptive variable neighbourhood search as its local search heuristic. Computational results demonstrate the efficiency of the proposed model in dealing with uncertainty in an agile manufacturing context. In addition, several managerial insights are discussed based on the results.