Manufacturing firms have been slow to adopt data-driven strategies, but the global pandemic has greatly accelerated the process. As supply chains were disrupted and consumer behaviour changed, businesses realised the need to quickly adapt and make critical decisions based on data insights. While the deployment of data strategy involves a "leap of faith" for many businesses, the benefits of greater transparency, efficiency, and cost savings make it essential for modern manufacturers.
We were joined today by David Green, MD of Handling Concepts, Pam Jackson, MD of Siddal & Hilton Products, Emma Hockley, MD of Big Bear Plastic Products, Mark Nisbett, MD of MTM Products and Roy Taylor, MD of Malthouse Engineering to discuss their findings. The firms all have varying levels of experience and success implementing data strategy but here are the main 3 themes identified from their experiences.
Culture and Change Management:
Implementing a data strategy requires a significant cultural shift and careful change management. Top-down management is common in manufacturing where supervisors and managers put a heavy emphasis on reducing downtime, cutting costs, and maintaining efficient workflows. However, to truly benefit from data insights, businesses need to open up communication channels across teams and levels of management. The shift in culture will require new policies, processes, and technologies aimed at promoting data literacy throughout the organisation. Without this, the companies themselves become data rich, but information poor. Malthouse Engineering particularly over a 15 year period have bought in outside support through 5 knowledge transfer partnerships with local Universities to assist, recognising the solutions weren’t internal. It was agreed by participants that whilst they as Directors may drive implementation, it has to be ‘owned’ by individuals to be successful. Cultural battles seem to have been very ‘hearts and minds’ driven with the fears of loss of job security experienced by those less technologically savvy.
Organisational Coordination:
In the initial stages of data strategy deployment, managing the workload can become top-heavy. The management team must work towards deploying a data governance model that defines the roles, responsibilities, and actions of stakeholders throughout the data lifecycle. Teams must understand their contribution to each stage of the data process, including data acquisition, processing, storage, and analytics. Well-coordinated teams are better positioned to deliver quality outputs, particularly in terms of operational optimization and financial decision-making. Practically though, experiences have been difficult in implementing new software through big investment in ERP, MRP and Office 365 so our manufacturing panellists found that adopting smaller wins such as ‘HR’ holiday booking and payroll information available through their own mobile devices was an easier way of implementing use of data-driven process improvements, once those apps were being used, it was found to be easier to encourage individuals to download different apps for other areas of the business.
Long-term View:
Data-driven strategies require a long-term view of the business landscape. Successful firms invest in technologies, infrastructure, and talent acquisition, to keep the system functioning over the long term. Amidst the digital transformation, businesses must also keep pace with emerging trends like machine learning and artificial intelligence to ensure ongoing competitive advantage. Manufacturers' data strategies must evolve over time to meet the changing demands of customers, anticipate market shifts, and address new risks. Each of our member panellists have well established businesses and long serving staff, in some cases amounting many decades of combined experience so firms need to recognise that experience was not realised instantly and nor can they rush implementation of a data strategy and risk losing the culture of their businesses. In some cases however firms will invariably have employees who are waiting for their retirement after a lifetime of service to the sector and for them it’s not a priority. Finding balance, taking small wins and having a long term view seem to be the best policies.
Conclusion
While the implementation of a data strategy in manufacturing can be challenging, it is worth the commitment. The investment in data-driven insights and decision-making practices can help manufacturers to maintain a competitive edge, reduce costs, and optimize workflows. By embracing the change from the top-down and coordinating stakeholders throughout the data lifecycle, businesses can create efficiencies that impact their bottom line. It is time for firms to recognize the value of data and invest in strategies that enable them to harness its power to drive growth.
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