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As businesses in a wide range of industries increasingly adopt digital ecosystems, the benefits include opening up new and untapped customer segments and channels, enabling access to new and untapped talent pools and expanding previously unexplored modes of innovation. Ecosystems – defined as digitally enabled networks that enhance value propositions by linking business functions, suppliers, distributors, partners, customers and other stakeholders – are now an engine that drives performance. Multiple studies confirm that value creation and competitive advantage are increasingly tied to organizations’ ability to engage partners and stakeholders. IBM Institute of Business Value’s most recent research has found that revenue growth of ecosystem leaders outpaces others by a five-to-one ratio.

Yet despite these notable successes there is also a dark side to digital ecosystem engagement. Large ecosystems have become a central to business strategy while little attention has been paid to the potential for cyber threats. Increased openness of business operations can result in greater risk.

Security must graduate into a central enabler of business transformation. If businesses do not fundamentally rethink their security equation, ecosystems and the trust on which they are built, which often take years and billions of investment dollars to build, can be squandered in minutes by ever-growing security threats.

Large ecosystems have become a central to business strategy while little attention has been paid to the potential for cyber threats.

Mature, risk-mitigated ecosystems are starting to resemble a mature supply chain, where some risks are precluded by design, some partners are favored based on established trust criteria and remaining risks are explicitly managed transactionally as part of the partner relationship.

Over the past year, the IBM Institute for Business Value (IBV) surveyed and interviewed tens of thousands of executives, employees and consumers around the world to learn what high-performing organizations are doing differently, and where executives are making the biggest bets.

After thousands of interviews over several years the researchers identified five trends that executives can explore to help prepare for a future characterized by disruption and change.

Executives are learning how to redefine how humans and technology work together.

Businesses need to reinvent their operations holistically to realize the full benefits of digital transformation.

A comprehensive record of strategic tech and talent trends the leaders can use to prepare for 2022 and beyond.

Downtime is a process parameter that substantially impacts on the operating hours and results in production losses, thus motivating maintenance engineers to control process plants. Notwithstanding, the impacting nature of process equipment failure on the operating hours in bottling plants remains inadequately examined. In this paper, the cause-and-effect analysis was used to establish the root cause of the downtime problem and Pareto analysis employed to justify the greatest opportunities for improvement in reducing downtime and increasing reliability levels. Weibull analysis is then conducted on the industrial setting. Novel aspect ratios are proposed.

Using the Weibull failure function of machines as a principal facilitator to produce failure predictions, the downtime behaviour of a process plant was modelled and tested with practical data from a bottling process plant. This research was conducted in a Nigerian process bottling plant where historical data were examined.

The analysis of the results shows the following principal outcome: First, the machines with the highest and least downtime values are 2 and 5, respectively, with correspondingly mean values of 22.83 and 4.39 h monthly. Second, the total downtime 92.05 and 142.14 h for the observed and target downtime, with a coefficient of determination of 0.5848 was recorded. Third, as month 1 was taken as the base period (target), all the machines, except M5 had accepted performance, indicating proper preventive maintenance plan execution for the bottling process plant. Availability shows a direct relationship between the failure and uptime of the machines and the downtime impacts on production. Two machines had random failure pattern and five machines exhibited a wear-out failure pattern and probably due to old age and wear of components in the machines.

The major contribution of the paper is the Weibull modelling in a unique application to a bottling plant to avoid current practices that use reliability software that is not easily accessible.