The long and winding road: Routine creation and replication in multi-site organizations

Prior research on organizational routines in the ‘capabilities’ literature has either studied how new routines are created during an exploratory process of variation and selection or how existing routines are replicated during a phase of exploitation. Few studies have analyzed the life cycle of new routine creation and replication as an integrated process. In an in-depth case study of England’s Highways Agency, this paper shows that the creation and replication of a new routine across multiple sites involves four sequential steps: envisioning, experimenting, entrenching and enacting. We contribute to the capabilities research in two ways: first, by showing how different organizational levels, capabilities and logics (cognitive and behavioural) shape the development of new routines; and second, by identifying how distinct evolutionary cycles of variation and selective retention occur during each step in the process. In contrast with prior research on replication as an exact copy of a template or existing routine, our study focuses on the replication of an entirely new routine (based on novel principles) that is adapted to fit local operational conditions during its large-scale replication across multiple sites. We draw upon insights from adjacent ‘practice research’ and suggest how capabilities and practice studies may complement each other in future research on the evolution of routines

Tipping the scales: ambidexterity practices on e-HRM projects

Purpose: We examine and conceptualise the ways in which a balance can be achieved between optimising the efficiency and effectiveness of electronic HRM (e-HRM) systems for human resource management (HRM) and enabling innovation to occur during the system implementation. Design/methodology/approach: An intepretive case study of a UK local authority e-HRM system implementation is examined using the notion of ambidexterity as an analytical device. Ambidexterity relates to how an organisation develops the ability to operate efficiently in the now, while at the same time being able to adapt to environmental changes around and ahead of them in order to grow into the future. Findings: As an intra-organisational capability, ambidexterity is found to derive from the simultaneous interplay and balancing of dual capabilities: exploitation and exploration.. E-HRM exploitation concerned the capability to generate new knowledge with innovatory effects, created through the everyday practices performed by practitioners at all levels in the organisation. E-HRM exploration, rather than being a purposeful act, was found to be an accidental consequence of engaging in exploitation to maintain the status quo. Originality/value: There is a lack of detailed investigation of how organisations actually achieve ambidexterity, particularly in three under-researched areas: ambidexterity in the public sector, at HR functional level and e-HRM systems implementation. Bundling these three areas into an integrated examination allows us to both identify how exploitation and exploration play out in the ambidextrous practices of an e-HRM project and also to identify the dimensions of ambidexterity in balancing e-HRM work

Building dynamic capabilities through operations strategy: an empirical example

This paper suggests that the implementation of an effective operations strategy process is one of the necessary antecedents to the development of dynamic capabilities within an organisation and that once established, dynamic capabilities and operations strategy process settle into a symbiotic relationship. Key terms and a model of operations strategy process are proposed from literature as a framework for analysing data from a longitudinal case study with a UK based manufacturer of construction materials

Problem-Solving Behaviours, Organisational Forms and the Complexity of Tasks

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A bi-objective genetic algorithm approach to risk mitigation in project scheduling

A problem of risk mitigation in project scheduling is formulated as a bi-objective optimization problem, where the expected makespan and the expected total cost are both to be minimized. The expected total cost is the sum of four cost components: overhead cost, activity execution cost, cost of reducing risks and penalty cost for tardiness. Risks for activities are predefined. For each risk at an activity, various levels are defined, which correspond to the results of different preventive measures. Only those risks with a probable impact on the duration of the related activity are considered here. Impacts of risks are not only accounted for through the expected makespan but are also translated into cost and thus have an impact on the expected total cost. An MIP model and a heuristic solution approach based on genetic algorithms (GAs) is proposed. The experiments conducted indicate that GAs provide a fast and effective solution approach to the problem. For smaller problems, the results obtained by the GA are very good. For larger problems, there is room for improvement

Combinatorial optimization and metaheuristics

Today, combinatorial optimization is one of the youngest and most active areas of discrete mathematics. It is a branch of optimization in applied mathematics and computer science, related to operational research, algorithm theory and computational complexity theory. It sits at the intersection of several fields, including artificial intelligence, mathematics and software engineering. Its increasing interest arises for the fact that a large number of scientific and industrial problems can be formulated as abstract combinatorial optimization problems, through graphs and/or (integer) linear programs. Some of these problems have polynomial-time (“efficient”) algorithms, while most of them are NP-hard, i.e. it is not proved that they can be solved in polynomial-time. Mainly, it means that it is not possible to guarantee that an exact solution to the problem can be found and one has to settle for an approximate solution with known performance guarantees. Indeed, the goal of approximate methods is to find “quickly” (reasonable run-times), with “high” probability, provable “good” solutions (low error from the real optimal solution). In the last 20 years, a new kind of algorithm commonly called metaheuristics have emerged in this class, which basically try to combine heuristics in high level frameworks aimed at efficiently and effectively exploring the search space. This report briefly outlines the components, concepts, advantages and disadvantages of different metaheuristic approaches from a conceptual point of view, in order to analyze their similarities and differences. The two very significant forces of intensification and diversification, that mainly determine the behavior of a metaheuristic, will be pointed out. The report concludes by exploring the importance of hybridization and integration methods

Ant colony optimisation and local search for bin-packing and cutting stock problems

The Bin Packing Problem and the Cutting Stock Problem are two related classes of NP-hard combinatorial optimization problems. Exact solution methods can only be used for very small instances, so for real-world problems, we have to rely on heuristic methods. In recent years, researchers have started to apply evolutionary approaches to these problems, including Genetic Algorithms and Evolutionary Programming. In the work presented here, we used an ant colony optimization (ACO) approach to solve both Bin Packing and Cutting Stock Problems. We present a pure ACO approach, as well as an ACO approach augmented with a simple but very effective local search algorithm. It is shown that the pure ACO approach can compete with existing evolutionary methods, whereas the hybrid approach can outperform the best-known hybrid evolutionary solution methods for certain problem classes. The hybrid ACO approach is also shown to require different parameter values from the pure ACO approach and to give a more robust performance across different problems with a single set of parameter values. The local search algorithm is also run with random restarts and shown to perform significantly worse than when combined with ACO

Automating defects simulation and fault modeling for SRAMs

The continues improvement in manufacturing process density for very deep sub micron technologies constantly leads to new classes of defects in memory devices. Exploring the effect of fabrication defects in future technologies, and identifying new classes of realistic functional fault models with their corresponding test sequences, is a time consuming task up to now mainly performed by hand. This paper proposes a new approach to automate this procedure. The proposed method exploits the capabilities of evolutionary algorithms to automatically identify faulty behaviors into defective memories and to define the corresponding fault models and relevant test sequences. Target defects are modeled at the electrical level in order to optimize the results to the specific technology and memory architecture

Operational Excellence in Manufacturing, Service and the Oil & Gas: the Sectorial Definitional Constructs and Risk Management Implication

The current global business climate has not been favorable to most firms irrespective of industry affiliation. That condition necessitated companies to adopt operational excellence as a strategy for optimising output with little resources, reducing lead time with the efficient use of assets and employees and avoiding safety and health issues to people and the environment. As a result of the need for operational excellence, many kinds of literature defined the concept based on the context of industry or sector. Industries such as manufacturing, services, oil and gas, mining and so many industries to mention a few, have their unique construct in the definition and therefore causing dilemma on which dimension to hold on to. It is against this backdrop that this paper synthesizes and integrate all the varying dimensions and fuses out similarities, differences and the antecedence of research directions taken on the few mentioned sectors. The paper thus concludes that the unique construct among all the definitions is continuous improvement, cost reduction, quality, time utilization, operational efficiency, staff involvement and output optimisation. However, they varied on risk management, staff health, safety and the concern for the environment, which is unique to oil and gas industry and that can affect the choice of research variables