An Empirical Study of Operational Performance Parity Following Enterprise System Deployment

This paper presents an empirical investigation into whether the implementation of packaged Enterprise Systems (ES) leads to parity in operational performance. Performance change and parity in operational performance are investigated in three geographically defined operating regions of a single firm. Order lead time, the elapsed time between receipt of an order and shipment to a customer, is used as a measure of operational performance. A single ES installation was deployed across all regions of the subject firm\u27s operations.Findings illustrate parity as an immediate consequence of ES deployment. However, differences in rates of performance improvement following deployment eventually result in significant (albeit smaller than pre-deployment) performance differences. An additional consequence of deployment seems to be an increased synchronization of performance across the formerly independent regions

Inventory Signals

How does operational competence translate into market value, when firms cannot credibly communicate their competence to the market? I consider the example of inventory and fill rates. When the market sees a high-inventory firm, it cannot tell whether the inventory is due to incompetence or a strategy to enhance fill rate. Firms might decide to signal their competence to the market by carrying less inventory. I show conditions for separating and pooling perfect Bayesian equilibria. I also provide empirical evidence for this theory that inventory has a signaling role. The theory could potentially provide a framework that describes one way in which a range of operational competences such as purchasing and outsourcing, translate to market value. Practically, it has implications for firms, such as how to strategically communicate to the market, reward managers, or even whether to go public and be subject to market pressures.Inventory; signaling; operations management; asymmetric information

On the Interface Between Operations and Human Resources Management

Operations management (OM) and human resources management (HRM) have historically been very separate fields. In practice, operations managers and human resource managers interact primarily on administrative issues regarding payroll and other matters. In academia, the two subjects are studied by separate communities of scholars publishing in disjoint sets of journals, drawing on mostly separate disciplinary foundations. Yet, operations and human resources are intimately related at a fundamental level. Operations are the context that often explains or moderates the effects of human resource activities such as pay, training, communications and staffing. Human responses to operations management systems often explain variations or anomalies that would otherwise be treated as randomness or error variance in traditional operations research models. In this paper, we probe the interface between operations and human resources by examining how human considerations affect classical OM results and how operational considerations affect classical HRM results. We then propose a unifying framework for identifying new research opportunities at the intersection of the two fields

Inventory Signals

Among practitioners, inventory is often thought to be the root of all evil in operations management. The stock market hates it, the media abhors it, and managers have come to fear it. But high inventory levels can also be the result of strategic buying and high-availability strategies. The problem is that when the market sees lots of inventory, it cannot tell whether it is because of poor or smart operations. We hypothesize that inventory has a signaling role. In our model, publicly- traded firms use inventory levels to signal their operational competence to the market. There is a separating equilibrium that leads some firms to maintain inventory levels below what their capability could achieve. We offer this as one explanation why, for example, stock-outs are pervasive even among operationally competent firms. We provide empirical evidence for the assumptions behind this inventory signaling hypothesis: (1) the market cannot tell the difference between “good” and “bad” inventory; and (2) the counterfactual: the market punishes firms when it can tell that their inventory is bad, such as when they write off supplies. Consistent with these assumptions, we find that inventory levels do not explain firm value. And on average, stocks suffer an abnormal negative return of 7% in the month of announcing inventory write-offs.Inventory, signaling, operations management, asymmetric information

Laying the Foundation: An Analytical Tool for Assessing Legal and Institutional Readiness for PES

This booklet has been created as an initial resource for public sector officials interested in fostering an environment in which PES transactions can occur. While PES legal and policy readiness is likely to look very different from one country to another -- depending on legal frameworks, as well as historical and current circumstances and pressures -- understanding policy options for getting ready for PES transactions is an important first step towards assessing readiness within a specific national and subnational context.This booklet offers an analytical framework for assessing legal and institutional readiness for PES transactions. It is divided into three sections based on timing and the order of addressing issues, with an eye to what will be most important to investors and buyers in payment for ecosystem services agreements. Specifically, the first level of preparing for PES agreements should be ensuring that fundamental or threshold conditions are in place for buyers to feel that there is sufficient stability in place to consider entering in these business arrangements. The second level of preparedness, while important for well-functioning PES, may be developed adaptively as needs and options become clearer via PES experience on the ground. Finally, level three includes non-urgent aspects that may be important to streamline or scale up PES, depending on the particular circumstances

A discrete time Markov chain model for a periodic inventory system with one-way substitution.

This paper studies the optimal design of an inventory system with “one-way substitution”, in which a high-quality (and hence, more expensive) item fulfills its own demand and simultaneously acts as backup safety stock for the (cheaper) low-quality item. Through the use of a discrete time Markov model we analyze the effect of one-way substitution in a periodic inventory system with an (R,s,S) or (R,S) order policy, assuming backorders, zero replenishment leadtime and correlated demand. In more detail, the optimal inventory control parameters (S and s) are determined in view of minimizing the expected total cost per period (i.e. sum of inventory holding costs, purchasing costs, backorder costs and adjustment costs). Numerical results show that the one-way substitution strategy can outperform both the “no pooling” (only product-specific stock is held, and demand can never be rerouted to stock of a different item) and “full pooling” strategies (implying that demand for a particular product type is always rerouted to the stock of the flexible product, and no product-specific stock is held) − provided the mix of dedicated and flexible inputs is chosen adequately − even when the cost premium for flexibility is significant. Furthermore, we can observe that decreasing the demand correlation results in rerouting more demand to the flexible product and because of the risk-pooling effect reduces the optimal expected total cost.Inventory management; One-way substitution;

Stochastic make-to-stock inventory deployment problem: an endosymbiotic psychoclonal algorithm based approach

Integrated steel manufacturers (ISMs) have no specific product, they just produce finished product from the ore. This enhances the uncertainty prevailing in the ISM regarding the nature of the finished product and significant demand by customers. At present low cost mini-mills are giving firm competition to ISMs in terms of cost, and this has compelled the ISM industry to target customers who want exotic products and faster reliable deliveries. To meet this objective, ISMs are exploring the option of satisfying part of their demand by converting strategically placed products, this helps in increasing the variability of product produced by the ISM in a short lead time. In this paper the authors have proposed a new hybrid evolutionary algorithm named endosymbiotic-psychoclonal (ESPC) to decide what and how much to stock as a semi-product in inventory. In the proposed theory, the ability of previously proposed psychoclonal algorithms to exploit the search space has been increased by making antibodies and antigen more co-operative interacting species. The efficacy of the proposed algorithm has been tested on randomly generated datasets and the results compared with other evolutionary algorithms such as genetic algorithms (GA) and simulated annealing (SA). The comparison of ESPC with GA and SA proves the superiority of the proposed algorithm both in terms of quality of the solution obtained and convergence time required to reach the optimal/near optimal value of the solution

E-Fulfillment and Multi-Channel Distribution – A Review

This review addresses the specific supply chain management issues of Internet fulfillment in a multi-channel environment. It provides a systematic overview of managerial planning tasks and reviews corresponding quantitative models. In this way, we aim to enhance the understanding of multi-channel e-fulfillment and to identify gaps between relevant managerial issues and academic literature, thereby indicating directions for future research. One of the recurrent patterns in today’s e-commerce operations is the combination of ‘bricks-and-clicks’, the integration of e-fulfillment into a portfolio of multiple alternative distribution channels. From a supply chain management perspective, multi-channel distribution provides opportunities for serving different customer segments, creating synergies, and exploiting economies of scale. However, in order to successfully exploit these opportunities companies need to master novel challenges. In particular, the design of a multi-channel distribution system requires a constant trade-off between process integration and separation across multiple channels. In addition, sales and operations decisions are ever more tightly intertwined as delivery and after-sales services are becoming key components of the product offering.Distribution;E-fulfillment;Literature Review;Online Retailing

Supply chain collaboration

In the past, research in operations management focused on single-firm analysis. Its goal was to provide managers in practice with suitable tools to improve the performance of their firm by calculating optimal inventory quantities, among others. Nowadays, business decisions are dominated by the globalization of markets and increased competition among firms. Further, more and more products reach the customer through supply chains that are composed of independent firms. Following these trends, research in operations management has shifted its focus from single-firm analysis to multi-firm analysis, in particular to improving the efficiency and performance of supply chains under decentralized control. The main characteristics of such chains are that the firms in the chain are independent actors who try to optimize their individual objectives, and that the decisions taken by a firm do also affect the performance of the other parties in the supply chain. These interactions among firms’ decisions ask for alignment and coordination of actions. Therefore, game theory, the study of situations of cooperation or conflict among heterogenous actors, is very well suited to deal with these interactions. This has been recognized by researchers in the field, since there are an ever increasing number of papers that applies tools, methods and models from game theory to supply chain problems

Variety Management in Assemble-to-Order Supply Chains

Assemble-to-order refers to a supply chain strategy in which products are not assembled until customer order arrives. It is based on the so-called form postponement that is to hold components at a generic form and to delay the point of product differentiation. The performance of an assem-ble-to-order supply chain depends on two main dimensions, which are responsiveness and achievement level of scale economies. Responsiveness refers to the capability of fulfilling customer requirements in a fast-paced manner, whereas the achievement of scale economies reflects the degree of operations efficiency. Assemble-to-order supply chains induce high product variety, which has adverse effects on performance. We use demand volumes as a proxy for scale economies and lead times as a proxy for responsiveness. A matrix that consists of both dimensions can be defined, in which we distinguish between short/long lead times and low/high demand volumes. This matrix is called performance matrix. On the other hand, the consequence that results from product variety is a high demand variability of end products, which also affects the demand variability of components. An analysis of component demand variability enables one to identify the components with low/high demand variability. These components can further be classified into supplied and in-house made components. Thus, a second matrix (called component matrix) with two dimensions, namely variability (low/high) and supply source (in-house/supplier) can be defined. Due to the supply source dimension in the component matrix, the supply chain perspective is also taken into ac-count. The combination of both matrixes into a single one provides the performance/component matrix for assemble-to-order supply chains. To use the final matrix, it is necessary to compute lead times, demand volumes and demand variability of the supplied and in-house made components. By plotting the components in the matrix, one can determine the problems induced by variety. In order to improve the performance of the assemble-to-order supply chain, the implementation of variety management strategies is necessary. The identified strategies are: commonality, component families, modularity, and platforms. Based on the performance/component matrix, we discuss how these strategies or a combination of them can contribute to derive recommendations that aim to alleviate variety impacts on the as-semble-to-order supply chain.Assemble-to-order; Supply Chain Management; Variety Management