Strategic Sales Process Adaptation: Relationship Orientation of the Sales Process in a Business-to-Business Context

Although previous research has recognised adaptation as a central aspect in relationships, the adaptation of the sales process to the buying process has not been studied. Furthermore, the linking of relationship orientation as mindset with adaptation as a strategy and forming the means has not been elaborated upon in previous research. Adaptation in the context of relationships has mostly been studied in relationship marketing. In sales and sales management research, adaptation has been studied with reference to personal selling. This study focuses on adaptation of the sales process to strategically match it to the buyer’s mindset and buying process. The purpose of this study is to develop a framework for strategic adaptation of the seller’s sales process to match the buyer’s buying process in a business-to-business context to make sales processes more relationship oriented. In order to arrive at a holistic view of adaptation of the sales process during relationship initiation, both the seller and buyer are included in an extensive case analysed in the study. However, the selected perspective is primarily that of the seller, and the level focused on is that of the sales process. The epistemological perspective adopted is constructivism. The study is a qualitative one applying a retrospective case study, where the main sources of information are in-depth semi-structured interviews with key informants representing the counterparts at the seller and the buyer in the software development and telecommunications industries. The main theoretical contributions of this research involve targeting a new area in the crossroads of relationship marketing, sales and sales management, and buying and purchasing by studying adaptation in a business-to-business context from a new perspective. Primarily, this study contributes to research in sales and sales management with reference to relationship orientation and strategic sales process adaptation. This research fills three research gaps. Firstly, linking the relationship orientation mindset with adaptation as strategy. Secondly, extending adaptation in sales from adaptation in selling to strategic adaptation of the sales process. Thirdly, extending adaptation to include facilitation of adaptation. The approach applied in the study, systematic combining, is characterised by continuously moving back and forth between theory and empirical data. The framework that emerges, in which linking mindset with strategy with mindset and means forms a central aspect, includes three layers: purchasing portfolio, seller-buyer relationship orientation, and strategic sales process adaptation. Linking the three layers enables an analysis of where sales process adaptation can make a contribution. Furthermore, implications for managerial use are demonstrated, for example how sellers can avoid the ‘trap’ of ad-hoc adaptation. This includes involving the company, embracing the buyer’s purchasing portfolio, understanding the current position that the seller has in this portfolio, and possibly educating the buyer about advantages of adopting a relationship-oriented approach.

Emerging bioenergy business at Finnish independent industrial sawmills

Finnish forest industry is in the middle of a radical change. Deepening recession and the falling demand of woodworking industry´s traditional products have forced also sawmilling industry to find new and more fertile solutions to improve their operational preconditions. In recent years, the role of bioenergy production has often been highlighted as a part of sawmills´ business repertoire. Sawmilling produces naturally a lot of by-products (e.g. bark, sawdust, chips) which could be exploited more effectively in energy production, and this would bring more incomes or maybe even create new business opportunities for sawmills. Production of bioenergy is also supported by government´s climate and energy policies favouring renewable energy sources, public financial subsidies, and soaring prices of fossil fuels. Also the decreasing production of domestic pulp and paper industry releases a fair amount of sawmills´ by-products for other uses. However, bioenergy production as a part of sawmills´ by-product utilization has been so far researched very little from a managerial point of view. The purpose of this study was to explore the relative significance of the main bioenergy-related processes, resources and factors at Finnish independent industrial sawmills including partnerships, cooperation, customers relationships and investments, and also the future perspectives of bioenergy business at these sawmills with the help of two resource-based approaches (resource-based view, natural-resource-based view). Data of the study comprised of secondary data (e.g. literature), and primary data which was attracted from interviews directed to sawmill managers (or equivalent persons in charge of decisions regarding bioenergy production at sawmill). While a literature review and the Delphi method with two questionnaires were utilized as the methods of the study. According to the results of the study, the most significant processes related to the value chain of bioenergy business are connected to raw material availability and procurement, and customer relationships management. In addition to raw material and services, the most significant resources included factory and machinery, personnel, collaboration, and geographic location. Long-term cooperation deals were clearly valued as the most significant form of collaboration, and especially in processes connected to raw material procurement. Study results also revealed that factors related to demand, subsidies and prices had highest importance in connection with sawmills´ future bioenergy business. However, majority of the respondents required that certain preconditions connected to the above-mentioned factors should be fulfilled before they will continue their bioenergy-related investments. Generally, the answers showed a wide divergence of opinions among the respondents which may refer to sawmills´ different emphases and expectations concerning bioenergy. In other words, bioenergy is still perceived as a quite novel and risky area of business at Finnish independent industrial sawmills. These results indicate that the massive expansion of bioenergy business at private sawmills in Finland is not a self-evident truth. The blocking barriers seem to be connected mainly to demand of bioenergy and money. Respondents´ answers disseminated a growing dissatisfaction towards the policies of authorities, which don´t treat equally sawmill-based bioenergy compared to other forms of bioenergy. This proposition was boiled down in a sawmill manager´s comment: “There is a lot of bioenergy available, if they just want to make use of it.” It seems that the positive effects of government´s policies favouring the renewables are not taking effect at private sawmills. However, as there anyway seems to be a lot of potential connected to emerging bioenergy business at Finnish independent industrial sawmills, there is also a clear need for more profound future studies over this topic.

Effect of uncertainty on helicopter performance predictions

The effect of uncertainties on performance predictions of a helicopter is studied in this article. The aeroelastic parameters such as the air density, blade profile drag coefficient, main rotor angular velocity, main rotor radius, and blade chord are considered as uncertain variables. The propagation of these uncertainties in the performance parameters such as thrust coefficient, figure of merit, induced velocity, and power required are studied using Monte Carlo simulation and the first-order reliability method. The Rankine-Froude momentum theory is used for performance prediction in hover, axial climb, and forward flight. The propagation of uncertainty causes large deviations from the baseline deterministic predictions, which undoubtedly affect both the achievable performance and the safety of the helicopter. The numerical results in this article provide useful bounds on helicopter power requirements.

Composite material and piezoelectric coefficient uncertainty effects on structural health monitoring using feedback control gains as damage indicators

This article addresses uncertainty effect on the health monitoring of a smart structure using control gain shifts as damage indicators. A finite element model of the smart composite plate with surface-bonded piezoelectric sensors and actuators is formulated using first-order shear deformation theory and a matrix crack model is integrated into the finite element model. A constant gain velocity/position feedback control algorithm is used to provide active damping to the structure. Numerical results show that the response of the structure is changed due to matrix cracks and this change can be compensated by actively tuning the feedback controller. This change in control gain can be used as a damage indicator for structural health monitoring. Monte Carlo simulation is conducted to study the effect of material uncertainty on the damage indicator by considering composite material properties and piezoelectric coefficients as independent random variables. It is found that the change in position feedback control gain is a robust damage indicator.

Lacunary Fourier series and a qualitative uncertainty principle for compact Lie groups

We define lacunary Fourier series on a compact connected semisimple Lie group G. If f is an element of L-1 (G) has lacunary Fourier series and f vanishes on a non empty open subset of G, then we prove that f vanishes identically. This result can be viewed as a qualitative uncertainty principle.

Predictive uncertainty of chaotic daily streamflow using ensemble wavelet networks approach

Perfect or even mediocre weather predictions over a long period are almost impossible because of the ultimate growth of a small initial error into a significant one. Even though the sensitivity of initial conditions limits the predictability in chaotic systems, an ensemble of prediction from different possible initial conditions and also a prediction algorithm capable of resolving the fine structure of the chaotic attractor can reduce the prediction uncertainty to some extent. All of the traditional chaotic prediction methods in hydrology are based on single optimum initial condition local models which can model the sudden divergence of the trajectories with different local functions. Conceptually, global models are ineffective in modeling the highly unstable structure of the chaotic attractor. This paper focuses on an ensemble prediction approach by reconstructing the phase space using different combinations of chaotic parameters, i.e., embedding dimension and delay time to quantify the uncertainty in initial conditions. The ensemble approach is implemented through a local learning wavelet network model with a global feed-forward neural network structure for the phase space prediction of chaotic streamflow series. Quantification of uncertainties in future predictions are done by creating an ensemble of predictions with wavelet network using a range of plausible embedding dimensions and delay times. The ensemble approach is proved to be 50% more efficient than the single prediction for both local approximation and wavelet network approaches. The wavelet network approach has proved to be 30%-50% more superior to the local approximation approach. Compared to the traditional local approximation approach with single initial condition, the total predictive uncertainty in the streamflow is reduced when modeled with ensemble wavelet networks for different lead times. Localization property of wavelets, utilizing different dilation and translation parameters, helps in capturing most of the statistical properties of the observed data. The need for taking into account all plausible initial conditions and also bringing together the characteristics of both local and global approaches to model the unstable yet ordered chaotic attractor of a hydrologic series is clearly demonstrated.

GNSS Signal Detection Under Noise Uncertainty

High sensitivity detection techniques are required for indoor navigation using Global Navigation Satellite System (GNSS) receivers, and typically, a combination of coherent and non- coherent integration is used as the test statistic for detection. The coherent integration exploits the deterministic part of the signal and is limited due to the residual frequency error, navigation data bits and user dynamics, which are not known apriori. So, non- coherent integration, which involves squaring of the coherent integration output, is used to improve the detection sensitivity. Due to this squaring, it is robust against the artifacts introduced due to data bits and/or frequency error. However, it is susceptible to uncertainty in the noise variance, and this can lead to fundamental sensitivity limits in detecting weak signals. In this work, the performance of the conventional non-coherent integration-based GNSS signal detection is studied in the presence of noise uncertainty. It is shown that the performance of the current state of the art GNSS receivers is close to the theoretical SNR limit for reliable detection at moderate levels of noise uncertainty. Alternate robust post-coherent detectors are also analyzed, and are shown to alleviate the noise uncertainty problem. Monte-Carlo simulations are used to confirm the theoretical predictions.

Robust Formulations for Handling Uncertainty in Kernel Matrices

We study the problem of uncertainty in the entries of the Kernel matrix, arising in SVM formulation. Using Chance Constraint Programming and a novel large deviation inequality we derive a formulation which is robust to such noise. The resulting formulation applies when the noise is Gaussian, or has finite support. The formulation in general is non-convex, but in several cases of interest it reduces to a convex program. The problem of uncertainty in kernel matrix is motivated from the real world problem of classifying proteins when the structures are provided with some uncertainty. The formulation derived here naturally incorporates such uncertainty in a principled manner leading to significant improvements over the state of the art. 1.

Uncertainty analysis of vibrational frequencies of an incompressible liquid in a rectangular tank with and without a baffle using polynomial chaos expansion

Polynomial chaos expansion (PCE) with Latin hypercube sampling (LHS) is employed for calculating the vibrational frequencies of an inviscid incompressible fluid partially filled in a rectangular tank with and without a baffle. Vibration frequencies of the coupled system are described through their projections on the PCE which uses orthogonal basis functions. PCE coefficients are evaluated using LHS. Convergence on the coefficient of variation is used to find the orthogonal polynomial basis function order which is employed in PCE. It is observed that the dispersion in the eigenvalues is more in the case of a rectangular tank with a baffle. The accuracy of the PCE method is verified with standard MCS results and is found to be more efficient.