Lap Keung Chu

An outsourcing decision model for sustaining long-term performance

Outsourcing is a strategically important activity that enables an enterprise to achieve both short and long term benefits. An important but less well researched issue concerns outsourcing in relation to the long-term performance of an enterprise. This paper addresses this issue from the point of view of the protection of core competencies during outsourcing and by considering the trade-off necessary when certain amounts of knowledge transfer/sharing is inevitable. Two major outsourcing decision variables have been identified and quantified—the significance of the component/process technology involved and the risk of disclosing this technology to suppliers. Nine scenarios are generated by considering each of the two variables at three levels of severity: high, medium and low. Each scenario is explored in terms of the appropriate outsourcing approach, the appropriate management of the core competencies/knowledge, and the selection of suppliers. The validity of the outsourcing decision model is established using four products with 861 components from four manufacturing companies based in China.

A portfolio approach to managing procurement risk using multi-stage stochastic programming

Procurement is a critical supply chain management function that is susceptible to risk, due mainly to uncertain customer demand and purchase price volatility. A procurement approach in the form of a portfolio that incorporates the common procurement means is proposed. Such means include long-term contracts, spot procurements and option-based supply contracts. The objective is to explore possible synergies among the various procurement means, and so be able to produce optimal or near optimal results in profit while mitigating risk. The implementation of the portfolio approach is based on a multi-stage stochastic programming model in which replenishment decisions are made at various stages along a time horizon, with replenishment quantities being determined by simultaneously considering the stochastic demand and the price volatility of the spot market. The model attempts to minimise the risk exposure of procurement decisions measured as conditional value-at-risk. Numerical experiments to test the effectiveness of the proposed model are performed using demand data from a large air conditioner manufacturer in China and price volatility data from the Shanghai steel market. The results indicate that the proposed model can fairly reliably outperform other approaches, especially when either the demand and/or prices exhibit significant variability.

A multi-stage financial hedging approach for the procurement of manufacturing materials

This paper addresses the problem of mitigating procurement risk that arises from volatile commodity prices by proposing a hedging strategy within a multi-stage time frame. The proposed multi-stage hedging strategy requires a commodity futures position to be correctly initialised and rebalanced with adequate volumes of short/long positions, so as to reduce the volatility in the total procurement cost that would otherwise be generated by varying commodity spot prices. The novelty in the approach is the introduction of the rebalancing of commodity futures position at defined intermediate stages. To obtain an efficient or near optimal multi-stage hedging strategy, a discrete-time stochastic control model (DSCM) is developed. Numerical experiments and Monte Carlo simulation are used to show that the proposed multi-stage hedging strategy compares favourably with the minimal-variance hedge and the one-stage hedge. A close-form optimal solution is also presented for the case when procurement volume and price are independent.

An approach to the valuation and decision of ERP investment projects based on real options

The risks and uncertainties inherent in most enterprise resources planning (ERP) investment projects are vast. Decision making in multistage ERP projects investment is also complex, due mainly to the uncertainties involved and the various managerial and/or physical constraints to be enforced. This paper tackles the problem using a real-option analysis framework, and applies multistage stochastic integer programming in formulating an analytical model whose solution will yield optimum or near-optimum investment decisions for ERP projects. Traditionally, such decision problems were tackled using lattice simulation or finite difference methods to compute the value of simple real options. However, these approaches are incapable of dealing with the more complex compound real options, and their use is thus limited to simple real-option analysis. Multistage stochastic integer programming is particularly suitable for sequential decision making under uncertainty, and is used in this paper and to find near-optimal strategies for complex decision problems. Compared with the traditional approaches, multistage stochastic integer programming is a much more powerful tool in evaluating such compound real options. This paper describes the proposed real-option analysis model and uses an example case study to demonstrate the effectiveness of the proposed approach.

Fuzzy chance-constrained programming model for a multi-echelon reverse logistics network for household appliances

Efficient planning and design of an appropriate reverse logistics network is crucial to the economical collection and disposal of scrapped household appliances and electrical products. Such systems are commonly modelled as mixed-integer programs, whose solutions will determine the location of individual facilities that optimize material flow. One of the major drawbacks of current models is that they do not adequately address the important issue of uncertainty in demand and supply. Another deficiency in current models is that they are restricted to a two-echelon system. This study addresses these deficiencies by embodying such uncertainties in the model using the technique of fuzzy-chance constrained programming, and by extending the model to a three-echelon system. A heuristic in the form of a hybrid genetic algorithm is then employed to generate low-cost solutions. The overall objective is to find economical solutions to the general problem of determining the volume of appliances to be moved between the three echelons of customer base to collection sites, collection sites to disposal centres and disposal centre to landfill centre/remanufacturing centre; and to the problems of positioning the disposal centres and the landfill centre/remanufacturing centres within the problem domain. A case example in China is presented and the quality and robustness of the solutions are explored through sensitivity analysis.

Choosing reverse channels under collection responsibility sharing in a closed-loop supply chain with re-manufacturing

This paper considers a closed-loop supply chain with re-manufacturing consisting of retailers, manufacturers and third-party logistics service providers; all participating in the product recycling responsibilities. The effectiveness of methods that can be used to share responsibilities amongst these parties is quantified using different reverse channels. First, re-manufacturing models with three different reverse channels for retailer collection, manufacturer collection and third-party collection are developed using collection responsibility sharing. Next, by comparing these models with the case of no collection responsibility sharing, the effectiveness of responsibility sharing is analysed and quantified. The results for the three models support the following conclusions: (i) from the point of view of the retailer, third-party collection is always the worst choice; (ii) the choice between retailer collection and manufacturer collection depends on the cost parameter representing the resources required in performing the reverse collection tasks; (iii) from the point of view the manufacturer, when the value of the cost parameter is small, collection by manufacturer is the best choice; retailer collection will be best for high values of the cost parameter.

Supplier selection for outsourcing from the perspective of protecting crucial product knowledge

This paper considers the supplier selection problem in terms of the characteristics of the products/processes to be outsourced. Emphasis is placed on the possible risk that may arise in sharing or transferring crucial product/process knowledge to suppliers during outsourcing. Two indices are employed to assess the suitability of a supplier for outsourcing a product/process. The first measures the suppliers capability for technological innovation in the products for which they offer outsourcing services, and includes investment in R&D projects and the ratio of R&D personnel to the total work force. The second index measures the suppliers practices in protecting its clients’ intellectual property rights, and includes corporate image, track record in protecting clients’ intellectual property rights, and compliance with internal and external requirements. These two indices are then used to classify suppliers into four major groups, which are in turn assigned to classes of components/processes depending on their strategic importance and vulnerability – a portfolio approach. The proposed portfolio approach has been validated via a substantial empirical study involving data for 401 parts, 216 suppliers, and 36 manufacturing companies operating in China.

Business process modelling in small‐ to medium‐sized enterprises

Data flow analysis is used in a novel context for business process modelling. A framework is presented together with its enterprise modelling concepts and the associated modelling tools. The framework was specifically developed for small‐ to medium‐sized industries. The strengths and weaknesses of the general data flow analysis approach are discussed in terms of its suitability for Hong Kong’s small‐ to medium‐sized industries. A case example is also presented to illustrate the methodology.

On the risk-averse procurement strategy under unreliable supply

Risk-averse procurement models are proposed under unreliable supply.Models are found to have unique solutions.Inventory strategy is effective to control supply risk, but not for a risk-neutral retailer.Analytical solution is derived for the single-period model.Order quantity of a more risk-averse retailer is more sensitive to various parameters. This study investigates an effective procurement/inventory strategy for a risk-averse retailer facing unreliable supply and stochastic demand. By using an increasing and concave utility function to describe risk aversion, we construct a basic newsvendor (single-period) model and its multi-period extension. Both models are found to have unique solutions, as the optimized expected utility is strictly concave in initial inventory level. As a result, there is a unique optimal order quantity for the effective control of supply risk. For the single-period model, the optimal order quantity is derived in its analytical form. We then show by numerical analysis that the value of the optimized expected utility is a function of the initial inventory level when the retailer is risk-averse, becomes less sensitive to initial inventory level when the degree of risk aversion decreases, and is insensitive for the risk-neutral case. This finding suggests that in our setting the inventory holding matters only when the retailer is risk-averse. For the multi-period model, we propose a solution procedure using backward induction since a direct extension of the single-period solution is impossible. We also conduct a sensitivity analysis of demand and supply with the aim of giving some managerial suggestions for demand risk control and supplier selection.

Service availability of a radio access telecommunication network

This paper describes the design process for a large-scale communication network that provides cordless access telephone services. A qualitative analysis of all the major aspects and main parameters is given. Quantitative models are also developed to find lower and upper bounds for the system blocking probability. The paper is directed to managers and analysts in the communications industry, and should help them anticipate and solve problems that are likely to arise when implementing similar systems.