reduced by implementing a discrete filter to reduce the complexity of the cost (ob-jective) function. The model reference control strategy performed better than the model predictive control strategy. Seferlis et al [68] approached the supply chain regulation problem for a multi-product, multi-echelon supply chain system using a multivariable model predictive strategy with the principal objective of maximizing the customer satisfaction at least operating cost. The performance of the system is described by two linear terms to account for inventory and transportation costs and two quadratic terms to penalize backorder and the changes in decision vari-ables. The penalization for the change in decision variable is equivalent to the rate of change in manipulated variables (replenishing orders) or move suppression to prevent aggressive control action. The optimization resulted in the best values of parameters characterizing the ordering policy. This strategy performed well for the system with large lead time and stochastic demand variation.
In general, all replenishment rules have their own advantages and disadvantages with respect to the business objective. Choosing the right replenishment rule with appropriate parameters in relation to the demand pattern and business goals is a challenging task for the overall network. The opportunity cost associated with the mismatch between demand and supplies are huge (e.g Piramuthu [69]): Boeing’s
$2.6 billion loss due to two inefficient key suppliers, Ericsson’s loss of three market share points against Nokia in 2000 due to supply disruptions in chips for new hand sets and General Motors decrease in earning by $900 million due to short supply of brakes.
2.4 Performance Metrics and their Quantification
2.4 Performance Metrics and their Quantification
Performance measures quantify the effectiveness and efficiency of the processes.
In modern supply chains, the performance measure should reflect the success and potential of the management strategies. It should contribute towards perfor-mance enhancement by identifying opportunities for further improvement. From the management perspective, performance measures provide necessary informa-tion for decision makers and process managers to diagnose problems and to assist the management in devising strategies through revision of supply chain goals, re-engineering and better business logistics.
2.4.1 Performance Metrics
A performance metric is one which can be measured or estimated to quantify the performance of the system of interest. A new systematic group of performance metrics (other than supply chain cost) may be useful to quantify the performance of the distribution unit and the overall network. Beamon [70] identified the per-formance measures of the manufacturing supply chain system and listed various performance metrics to characterize the production, distribution and inventory systems. Identifying the key performance metrics of the distribution system is tedious owing to the complex nature of the distribution network. In general, per-formance metrics can be categorized into three groups involving resources, output and flexibility [71]. Each group refers to different goals and therefore an effective performance measure system should include at least one performance metric from each category to attain strategic goals (Figure2.2). The chosen metrics must also
2.4 Performance Metrics and their Quantification
Outputs
Profit
Customer Satisfaction
Flexibility
Order Processing Inventory Allocation
Resources
Excess Inventory Back Order
Overall Performance
Figure 2.2: Performance Metrics
be compatible with the business objective. For the distribution network, the re-sources category represents the supply chain cost (i.e. inventory cost, backorder cost, transportation cost); the output represents the revenue, customer service level, number of stock outs, etc. Flexibility category represents the flexibility in managing inventory level, order quantity, transportation capacity and responsive-ness of the inventory target in accordance with the uncertain demand.
The performance measures can also be categorized into qualitative and quantita-tive metrics depending on their characteristics. A detailed review of qualitaquantita-tive and quantitative performance metrics at various decision levels and parts of the supply chain are discussed in detail by Gunasekaran et al [72]. Chan [73] character-ized new performance metrics like visibility, trust, innovativeness and quantified
2.4 Performance Metrics and their Quantification
the existing metrics like resource utilization and flexibility in several ways. Al-though qualitative measures are the important characteristics of a supply chain, it is more challenging to incorporate the qualitative measures into quantitative models like supplier performance, information flow and risk management. Gu-nasekaran et al [74] developed a framework for measuring the strategic, tactical, and operational level of performance in a supply chain. This deals mainly with supplier, delivery, customer service, inventory and logistics costs. Juran et al [75] organized the measures to track the behaviour of the supply chain into two categories as customer focused and company focused. Backorders, fill rates and customer satisfaction are metrics chosen to focus on customers. The lead-time vari-ability, compliance with production schedules and inventory levels are the metrics addressing companies. Different level of assessment techniques (termed satellite view, 30,000 foot view, 500 foot view and ground level view) were identified to understand the supply network topology and details of the measures depending on the complexity of the system.
The performance of the supply chain is also classified through the viewpoint as external customers (effectiveness) and internal organization (efficiency). The ex-ternal customer performance metrics are timeliness, accuracy and product quality.
The internal organization metrics are production cost, inventory cost and distri-bution cost. Inadequate effectiveness and efficiency are associated with customer dissatisfaction, loss of market share, deterioration in revenues, excessive produc-tion, excessive investment in work-in-process and finished goods inventory and deteriorating profitability.
2.4 Performance Metrics and their Quantification
2.4.2 Performance Benchmarking
Performance assessment is the process of benchmarking the current performance of the supply chain to a theoretically best or practically optimal supply chain system.
Benchmarking can also be defined as the process of identifying, understanding and adapting outstanding practices from within the same organization or from others to help improve performance. This involves the process of comparing practices and procedures to those of the best and identifies ways in which an organization can make improvements. Benchmarking methodology is a standard four phase im-provement cycle represented as benchmarking wheel diagram comprising of plan, do, check and act phases [76]. The Plan phase focuses on selecting the process for benchmark study, do phase decides the benchmark partners and metrics to evalu-ate existing performance. The Check phase identifies the gap between the existing process with the benchmark process and partners. Finally, the act phase, will take the responsibility for corrective action and implementation to gain performance improvement. Lee et al [77] described the pitfalls and opportunities involved in inventory management of supply chain system. The pitfalls are identified in the area of information, operational and strategic issues & supply chain design. In-consistent metrics, inappropriate definition of performance measures, inaccurate system data and inefficient information sharing system are the pitfalls identified in information area. The operational difficulties affecting the inventory management include ignoring the impact of uncertainties, practicing simple stocking policy, prioritizing the internal and external customers, poor coordination among supply chain divisions and the mode of transportation. In strategic and design issues,