Advances in digital technologies, amongst others, present process innovation opportunities in manufacturing which if appropriately exploited will increase performance and productivity. Central to successful implementation of process innovation initiatives is adequate preparation during pre-implementation phase and ensuring that the business is ready prior to the deployment of their process innovation initiatives. Akin to digital transformation, key elements of process innovation deployment include people, process, and technology. An understanding of these key elements of process innovation deployment readiness will help towards achieving successful implementation outcome. This paper explores manufacturing process innovation deployment readiness from an extended people, process, and technology framework perspective. The extension adds to the traditional PPT framework the context of the deployment. Attributes of manufacturing process innovation deployment readiness were obtained from the literature and the derived attributes form the basis for discussing the extended PPT view of deployment readiness. It is concluded that failure to either consider or grossly underestimate the role of people, processes, technology, and the context of the deployment will undermine implementations of process innovation in manufacturing.

It is important to attain an appropriate state of deployment readiness when implementing process innovation. That is, ensuring that deployment will run smoothly and relatively problem free. Essentially, deployment readiness is a feature of pre-implementation phase and it represents the state of preparedness for something about to happen. Of increasing interest in the literature is finding ways of achieving the highest degree of process innovation deployment readiness. Whilst methods of assessing deployment readiness are emerging, the influence of flexibility on deployment readiness is yet to be ascertained. This paper focuses on the influence of flexibility on readiness to deploy process innovation in manufacturing. In particular, the emphasis is on how flexibility mediates deployment plans for implementing manufacturing process innovation. Deployment plan is conceptualised as the approach, scope, and execution planned for the deployment of an innovation initiative. The interrelationships of flexibility and deployment plans is studied using simulation of a job shop with routing flexibility and results show that deployment plan is significant to innovation deployment readiness performance and its significance is moderated by flexibility.

Manufacturing companies need to continuously innovate in order to remain competitive. Fostering a successful innovative environment should reflect positively on manufacturing performance, with a premise that companies seek to attain appropriate level of readiness when deploying their innovation. This paper presents an approach to assessing innovation deployment readiness in manufacturing. Deployment is conceptualised as a sequential decision process that involves a deployment plan to be executed sequentially in an uncertain environment. The deployment plan is assessed using simulation to account for risks and uncertainties that may characterise the deployment activities in the target environment and the capabilities put forward in the plan for handling the risks and uncertainties. The approach is illustrated using a simulated manufacturing job shop scenario and the results show that deployment readiness can vary over time. Deployment readiness can be improved by identifying the states in which readiness is weak and taking appropriate actions.

For successful implementation of innovation initiatives it is important to have in place adequate deployment and test plans for the initiatives, pre-implementation. This paper presents an Operational Acceptance Testing (OAT) methodology for use in testing deployment readiness of innovation initiatives. The OAT methodology is centred on scenario-based simulation techniques, using test cases of innovation deployment plans and risks. The focus in this paper is on manufacturing process innovations (MPI). The proposed methodology enables testing of MPI initiatives with the aim of assessing the level of confidence offered by MPI deployment plans and the associated readiness to deploy. Using an illustrative example, it is shown that the OAT methodology helps determine whether there is enough statistical evidence in favour of a belief that a manufacturing process will operate as desired during innovation deployment whilst maintaining the required manufacturing capabilities and service levels.

Manufacturing companies that fail to successfully deliver innovation do so primarily due to ineffectiveness of the pre-implementation phase of their innovation initiatives. In particular, the risks involved in the deployment of innovation initiatives must be appraised and the risks responded to appropriately. This paper presents an approach to optimal selection of innovation deployment risk strategies in manufacturing using a simulation optimisation methodology. The goals to attain are the maximisation of deployment readiness performance and minimization of the cost of implementing risk response strategy, subject principally to budget constraints. The approach is illustrated using an example of deploying process innovations in a simulated job shop. The example demonstrates the usefulness of the proposed method, allowing the job shop to select the most desirable risk response strategies for coping with innovation deployment risk events that will result in optimised deployment readiness performance and satisfactory manufacturing service level.