Institutionalizing innovation: Key activities for market leadership

What distinguishes outstanding innovators from others:They understand that consistent innovation is the key to a company's competitive survival.So they are constantly looking for ways to change every aspect of their business - not just marketing - to serve customers better.To generate and sustain innovation all across the business, they consistently focus on these five keys:

• - Creating a corporate work environment that values competitive performance above everything else.

• - Structuring the organization to permit innovative ideas to rise above the demands of running the business.

• - Clearly defining a strategic direction that lets the company channel its innovative efforts in ways that will pay off in their particular marketplace.

• - Knowing where to look for good ideas and how to leverage them once they're found.

• - Going after good ideas at full speed and marshalling the resources needed to ensure lasting success.I believe the following five steps will make your company more dynamic and innovative:

• - Create a mind set that puts constant pressure on the organization to beat your specific competitors through innovation.

• - Structure your organization to promote innovation instead of thwarting it as most do.

• - Develop a realistic strategic focus to channel people's innovative efforts.

• - Know where to look for innovative edges in your particular business.

• - Throw the book at a few good ideas instead of spreading your resources around like butter on bread.Sounds simple because, of course, it is. But: it is a survival issue for most large corporations:

• - Who are being “niched to death” by smaller competitors, and

• - Outfoxed by global competitors who have taught themselves how to manage innovation more effectively.

Just-in-time purchasing: an empirical study of operational practices, supplier development and performance

This paper examines the role of supplier development in establishing and managing efficient buyer–supplier operational links. The paper develops and assesses a measurement instrument for “operational” and “supplier development” just-in-time purchasing practices, followed by an examination of the relationships between the two sets, and an investigation into whether the use of “operational” and “supplier development” practices has a bearing on higher plant performance. A plant-level survey was carried out on a sample of electronics and machinery plants. The study empirically:

• documents the close connection between the buyer–supplier operational link and the buyer’s practices for supplier development;

• demonstrates that recourse to supplier development programs and their nature depend on the kind of vendor–vendee operational connection;

• test whether different plant performance outcomes result from the implementing of different “operational” and “supplier development” practices. It demonstrates that better-performing plants exhibit more advanced design and logistic links with sources, more formalised vendor-rating and ranking procedures, greater use of organisational devices for supplier-organisational integration and place greater importance on supplier assistance and training.

The use of a model in long term planning—A case history

Long term planning in companies can range from the partial simplistic approach to a structured time-consuming ritual. Either extreme can bring planning into disrepute, and both reflect the lack of the proper tools of the trade. Models, by themselves, are no panacea, but properly conceived, constructed, and developed they can transform the planning process. The key to effective and practical models is use, and this can be achieved through direct management involvement on the model's development, and by making sure the model is basically simple and flexible. This article describes how such a model was constructed using a modular approach, and how it was used effectively in long term planning.     

Mechanisms to Induce Buyer Forecasting: Do Suppliers Always Benefit from Better Forecasting?

In this study, we consider a supplier's contract offerings to a buyer who may obtain improved forecasts for her demand over time. We investigate how the supplier can take advantage of the buyer's better forecasts and what kind of contracts he should offer to the buyer in order to maximize his profits. We model a natural forecast evolution where the buyer can obtain a more accurate forecast closer to the selling season. We assume there is information asymmetry between the buyer and the supplier at all times in that the buyer understands her demand better than the supplier. Three types of contracts that the supplier can offer are considered: (1) one where a contract is offered before the buyer has a chance to obtain improved forecasts, (2) one where a contract is offered after the buyer has obtained improved forecasts, and (3) a contingent (dynamic) contract which offers an initial contract to the buyer before she obtains improved forecasts, followed by a later contract (contingent on the initial contract) offered after improved forecasts have been obtained. We consider two scenarios: (1) where the supplier is certain that the buyer can obtain more accurate forecasts over time, and (2) where the supplier is uncertain about the buyer's forecasting capability (or forecasting cost). In the first scenario, we show that among the three types of contracts, the contingent contract is always the most profitable for the supplier. Furthermore, using the contingent contract, the supplier always benefits from higher accuracy of the buyer's demand forecasts. In the second scenario, we explicitly model the supplier's level of certainty about the buyer's capability of obtaining better forecasts, and explore how the supplier can design contracts to induce the buyer to obtain better forecasts when she is capable.     

Local government service systems improvement: exposing the social networks

Using a local government (LG) case study, this paper describes and qualitatively explores the value and use of social network analysis (SNA) in creating a rich environment for service network innovation and development. The case study analysed manages regional planning development applications and involves a complex social network. The study underpins the view that in LG service environments, mechanistic models for systems improvement are on their own inadequate. SNA constitutes an essential complementary development framework underpinning continuous innovation through human and social capital development. Findings presented are profound for local government and multiple service contexts and argue for a greater emphasis on understanding and developing the human and social aspects of service systems as opposed to a predominant technical systems bias.     

Combining Forecasts: Multiple Regression versus a Bayesian Approach*

Simple linear combinations of forecasts have consistently been found to be more accurate than individual forecasts. Several recent studies have found that combination forecasts derived by constrained or unconstrained multiple regression are more accurate than a simple average of individual forecasts. This study uses macroeconomic data to compare the accuracy of combination forecasts derived by a Bayesian methodology with the accuracy of composite forecasts derived by multiple regression. Using the forecasts of four macroeconomic variables from five well-known econometric models, the study finds that the Bayesian combination procedure produces more accurate composite forecasts than does the regression combination procedure, based on a version of Theil's U² statistic.     

Tests of Conditional Predictive Ability

We propose a framework for out‐of‐sample predictive ability testing and forecast selection designed for use in the realistic situation in which the forecasting model is possibly misspecified, due to unmodeled dynamics, unmodeled heterogeneity, incorrect functional form, or any combination of these. Relative to the existing literature (Diebold and Mariano (1995) and West (1996)), we introduce two main innovations: (i) We derive our tests in an environment where the finite sample properties of the estimators on which the forecasts may depend are preserved asymptotically. (ii) We accommodate conditional evaluation objectives (can we predict which forecast will be more accurate at a future date?), which nest unconditional objectives (which forecast was more accurate on average?), that have been the sole focus of previous literature. As a result of (i), our tests have several advantages: they capture the effect of estimation uncertainty on relative forecast performance, they can handle forecasts based on both nested and nonnested models, they allow the forecasts to be produced by general estimation methods, and they are easy to compute. Although both unconditional and conditional approaches are informative, conditioning can help fine‐tune the forecast selection to current economic conditions. To this end, we propose a two‐step decision rule that uses current information to select the best forecast for the future date of interest. We illustrate the usefulness of our approach by comparing forecasts from leading parameter‐reduction methods for macroeconomic forecasting using a large number of predictors.     

BIASES IN MACROECONOMIC FORECASTS: IRRATIONALITY OR ASYMMETRIC LOSS?

Empirical studies using survey data on expectations have frequently observed that forecasts are biased and have concluded that agents are not rational. We establish that existing rationality tests are not robust to even small deviations from symmetric loss and hence have little ability to tell whether the forecaster is irrational or the loss function is asymmetric. We quantify the trade‐off between forecast inefficiency and asymmetric loss leading to identical outcomes of standard rationality tests and explore new and more general methods for testing forecast rationality jointly with flexible families of loss functions that embed squared loss as a special case. Empirical applications to survey data on forecasts of real output growth and inflation suggest that rejections of rationality may largely have been driven by the assumption of squared loss. Moreover, our results suggest that agents are averse to “bad” outcomes such as lower‐than‐expected real output growth and higher‐than‐expected inflation and that they incorporate such loss aversion into their forecasts. (JEL: C22, C53, E37)     

Impact of Bias Magnification on Supply Chain Costs: The Mitigating Role of Forecast Sharing

The impact of forecast error magnification on supply chain cost has been well documented. Unlike past studies that measure forecast error in terms of forecast standard deviation, our study extends research to consider the impact of forecast bias, and the complex interaction between these variables. Simulating a two‐stage supply chain using realistic cost data we test the impact of bias magnification comparing two scenarios: one with forecast sharing between retailer and supplier, and one without. We then corroborate findings via survey data. Results show magnification of forecast bias to have a considerably greater impact on supply chain cost than magnification of forecast standard deviation. Particularly damaging is high bias in the presence of high forecast standard deviation. Forecast sharing is found to mitigate the impact of forecast error, however, primarily at higher levels of forecast standard deviation. At low levels of forecast standard deviation the benefits are not significant suggesting that engaging in such mitigation strategies may be less effective when there is little opportunity for improvement in accuracy. Furthermore, forecast sharing is found to be much less effective against high levels of bias. This is an important finding as managers often deliberately bias their forecasts and underscores the importance of exercising caution even with forecast sharing, particularly for forecasts that have inherently large errors. The findings provide a deeper understanding of the impact of forecast errors, suggest limitations of forecast sharing, and offer implications for research and practice alike.     

Forecast horizon aggregation in integer autoregressive moving average (INARMA) models

This paper addresses aggregation in integer autoregressive moving average (INARMA) models. Although aggregation in continuous-valued time series has been widely discussed, the same is not true for integer-valued time series. Forecast horizon aggregation is addressed in this paper. It is shown that the overlapping forecast horizon aggregation of an INARMA process results in an INARMA process. The conditional expected value of the aggregated process is also derived for use in forecasting. A simulation experiment is conducted to assess the accuracy of the forecasts produced by the aggregation method and to compare it to the accuracy of cumulative h-step ahead forecasts over the forecasting horizon. The results of an empirical analysis are also provided.     

Inter‐organizational Quality Management: The Use of Contractual Incentives and Monitoring Mechanisms with Outsourced Manufacturing

Quality‐related incidents involving contract manufacturers (CMs) are becoming increasingly prevalent. The quality management (QM) literature, however, has focused mostly on QM within a single firm. Thus, the need for data‐driven research on managing quality with outsourced production is evident. We investigate the use and effectiveness of external failure penalties and audits of CMs’ facilities to manage inter‐firm quality. Building on agency theory and extant QM literature, this study addresses two research questions: (i) whether the control mechanisms of quality audits and contractual external quality failure penalties are substitutes or complements in use and (ii) whether they are substitutes or complements in their effectiveness at aligning the quality interests of customers and their CMs. Our analysis uses dyadic data gathered from brand‐owning firms and their CMs representing 95 contract manufacturing relationships in Food and Drug Administration (FDA)‐regulated industries. The results indicate that more severe external failure penalties correspond to a lower use of facility audits (i.e., they are substitutes‐in‐use). We also find that both external failure penalties and facility audits have a unique positive effect on the CM's perception of relative quality importance. Finally, some evidence supports the hypothesis that each mechanism is more effective in the presence of the other (i.e., they are complements‐in‐effectiveness).     

Ethical issues in tracking cellular telephones at an event

Early in 2007, the CSIR conducted an experiment to track the cellular telephones of a small group of people as they moved to and from an event, to test the viability of using such tracking to provide the participants with useful traffic information. This project raised a number of ethical issues, which prompted this paper and which we discuss here. These include:

• the ethics of modelling data, including the treatment of research participants;

• privacy and surveillance issues related to tracking the movement of people;

• the risks inherent in being tracked vs the benefits of being tracked; and

• the ethics related to sending messages to drivers.

We have reviewed the literature on ethics and used the results to assemble a check list of relevant ethical issues, adding a few of our own (i.e. a deontological ethics approach), against which the conduct of this research project was assessed. We also provide an overview of the experiment and the results obtained.     

Corporate social responsibility: the organizational view

Designing strategies for corporate social responsibility (CSR)-practice nowadays has become essential for organizations. Notwithstanding, how organizations appear internally in a socially responsible context toward their employees has been insufficiently investigated. This study aims at ascertaining how Internal CSR can be conceptualized as well as how it affects employees’ commitment. To do so, the manifestations of Internal CSR are discussed based on social identity theory and further literature, while the developed research model is checked for suitability through a survey generating 2081 employee responses from an international pharmaceutical company. As one result, it can be stated that the factors proposed to constitute Internal CSR are confirmed. Further, the findings entail the following conclusions: Internal CSR has a notable influence on employees’ Affective Organizational Commitment whilst relatively moderate impacting Normative Organizational Commitment. Additionally, Affective Organizational Commitment adopts a mediating function regarding Normative Organizational Commitment.     

Combining Judgmental and Statistical Forecasts: An Application to Earnings Forecasts

This study investigated the accuracy of combinations of statistical and judgmental forecasts of annual accounting earnings. Combined forecasts were generated as equally weighted (i.e., simple averages) and unequally weighted combinations of individual forecasts from time-series models of quarterly and annual earnings (statistical forecasts) and security analysts' forecasts of quarterly and annual earnings (judgmental forecasts). The effect of the number of individual forecasts combined on the accuracy of the combined forecasts was also examined. The empirical results indicated that, on the average, combined forecasts were more accurate than individual forecasts. The results also indicated that although analysts' forecasts are based on a wider information set, the accuracy of their forecasts could be improved by combining them with forecasts generated from statistical models. Even if the best individual forecast could be identified in advance, gains in accuracy could be achieved by using combinations of two other forecasting methods. Several of the combined forecasts were superior to the most accurate individual forecast. Forecasts combined by using unequal weights derived from a regression model proved more accurate than equally weighted combinations. Forecasting accuracy improved and the variability of accuracy across different combinations decreased as the number of forecasts in the combination increased.     

Evaluating Probabilistic Forecasts with Bayesian Signal Detection Models

We propose the use of signal detection theory (SDT) to evaluate the performance of both probabilistic forecasting systems and individual forecasters. The main advantage of SDT is that it provides a principled way to distinguish the response from system diagnosticity, which is defined as the ability to distinguish events that occur from those that do not. There are two challenges in applying SDT to probabilistic forecasts. First, the SDT model must handle judged probabilities rather than the conventional binary decisions. Second, the model must be able to operate in the presence of sparse data generated within the context of human forecasting systems. Our approach is to specify a model of how individual forecasts are generated from underlying representations and use Bayesian inference to estimate the underlying latent parameters. Given our estimate of the underlying representations, features of the classic SDT model, such as the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC), follow immediately. We show how our approach allows ROC curves and AUCs to be applied to individuals within a group of forecasters, estimated as a function of time, and extended to measure differences in forecastability across different domains. Among the advantages of this method is that it depends only on the ordinal properties of the probabilistic forecasts. We conclude with a brief discussion of how this approach might facilitate decision making.     

Manipulability of Future‐Independent Tests

The difficulties in properly anticipating key economic variables may encourage decision makers to rely on experts' forecasts. Professional forecasters, however, may not be reliable and so their forecasts must be empirically tested. This may induce experts to forecast strategically in order to pass the test. A test can be ignorantly passed if a false expert, with no knowledge of the data‐generating process, can pass the test. Many tests that are unlikely to reject correct forecasts can be ignorantly passed. Tests that cannot be ignorantly passed do exist, but these tests must make use of predictions contingent on data not yet observed at the time the forecasts are rejected. Such tests cannot be run if forecasters report only the probability of the next period's events on the basis of the actually observed data. This result shows that it is difficult to dismiss false, but strategic, experts who know how theories are tested. This result also shows an important role that can be played by predictions contingent on data not yet observed.