The National Skills Academy for Rail is part of a network of National Skills Academies—employer-led organizations established by the UK government in 2006 to raise the quality of training provision in their sector and broker better relationships between employers and training providers (Institute for Employment Studies 2011). Part of their remit is to attract significant employer investment in skills, and design and deliver standards, qualifications, and curricula that meet current and future sector needs.
Through workforce data, NSAR helps the rail sector to make informed decisions and target investment in resource planning. It enables the industry to increase its competitiveness by matching skills and workforce demand to training and education supply for both upskilling and apprenticeships, thus enabling rail companies to deliver a more efficient railway.
In support of its work, NSAR has developed its Skills Intelligence Model (SIM), a detailed and innovative skills forecasting tool that provides a comprehensive picture of which skills are needed now and in the future. Assured by the UK’s Department of Transport, it is a statistical tool that can report both at industry and at company level in a way that is easily interpreted by end users. The information collected shows the “gap” between the requirements of the future workforce and the current workplace, allowing industry to plan ahead of time to address any gaps.
The SIM allows users to conduct scenario mapping, business modelling, and cost saving and efficiency projections. The power of the SIM is its ability to deliver intelligence that informs the industry, skills supply chain, and prospective new talent, rather than simply “data”.
The SIM is the central tool used for workforce planning in prominent organizations such as the UK government, Network Rail, the offshore wind industry, and Heathrow Airport. Among infrastructure employers in the UK, workforce planning has become central to creating a learning culture to prepare for Industry 4.0, as well as the accelerated technological change brought about the COVID-19 pandemic.
16.2.1 Development of a Workforce Development Strategy Using the Skills Intelligence Model
In light of the demand for skills created by the planned investments in the UK’s national infrastructure outlined above, and the burgeoning evidence of mismatch between labor supply and demand, in late 2019, NSAR conducted a comprehensive study of supply and demand in the rail supply chain in the UK. All reasonable demand scenarios were considered, and the most likely one extensively modelled. Using the SIM, a large confidential data pack was collated. The modelling showed that the supply chain lacks sufficient capability and capacity to fill the gaps—clearly a market failure. As a result, a long-term workforce training and implementation plan was prepared to resolve this.
The analysis showed that the forecasted skills shortage is certain to lead to further wage inflation especially in the rail and civil engineering supply chain (from 5.6 to 8%). The business plan for the investment expected that the economic value of the associated jobs would be £6.3 billion ($8.3 billion, rising to over £8 billion ($10.8 billion) if a full social mobility approach was adopted. The practical impact of this market failure would be to reduce the economic benefits by 40%, or £2.5 billion ($3.4 billion). This outcome would have risked the whole business case and cost projections. Planned and unplanned disruptions (Britain’s exit from the European Union and the COVID-19 pandemic) exacerbated the situation. Given that the time required to train to the level required by industry (level 3 in the UK) is 3 years, a long-term plan and renewed approach was deemed necessary to address education and learning challenges in these disruptive times. In this case, the SIM planned for a period of 15 years.
16.2.2 A Socially Inclusive Plan to Deliver the Skills Required
Like many governments, the UK government is attracted to investments in less developed areas of the country, expecting that these will create both construction and operational jobs. However, the modelling done for the rail sector showed that on its own, this policy would not have the desired impact. The SIM also showed that it would be possible to grow capacity and capability in the supply chain, but it would need to be a conscious and concerted strategy. As a model, the SIM and its application to workforce planning can be applied much more widely, not just in the UK, and way beyond the rail sector.
The SIM process follows a typical sequence: preparation of a strategic workforce plan, a full statement of anticipated demand for skills and people, and mapping against supply to understand the gaps. Next a recruitment plan is drawn up outlining how many will be recruited, when, and into what roles; and identifying options for how this is to be done, e.g., by promotion, upskilling, but mostly by new recruitment. Once the who, where, and how many need to be recruited is known, a training plan is drawn up. This is in effect a guide to the training supply chain (training providers) to deliver the necessary upskilling, and notably to bring in new skills such as digital and management skills.
Public sector clients increasingly expect social value outcomes, so a social inclusion plan is also prepared—a clear and relatively simple plan to target recruitment for roles and training places from a more diverse range of backgrounds and communities. This social inclusion plan sets out practical steps for employers, such as how to work with local colleges to offer work experience programs in the industry. Procurement contracts for infrastructure projects include clauses such as a minimum 10% (up to 20%) of recruits should come from disadvantaged backgrounds. Colleges are provided with additional funding to enable them to meet demand. The preferred training model is the people-focused apprenticeship, which plans are then integrated into the wider “value for money” strategy—or what is called a “productivity plan”, referring to both quality and efficiency (see Fig. 16.2).
Investments in new technology alone do not realize value if staff and managers are not trained. Research has shown that 50% of the time, returns are not realized because of a lack of skills and/or understanding. A productivity maturity tool (see Table 16.1) helps to create understanding, which incentivizes employers to invest in training, even when they do not see the importance of the wider learning culture and approach. An example of this in the UK composites industry (Lewis 2013) (Table 16.1).
16.2.3 Impact of the Skills Intelligence Model and Subsequent Workforce Development Plan
With an investment pipeline of £600 billion ($810 billion), views were sought on how infrastructure could be delivered without major wage inflation, with strong socioeconomic benefits, and especially with maximum value to the local communities. In order to embed learning society values in industries that had previously shown a reluctance to train, the immediate negative impact of traditional approaches needed to be demonstrated. The SIM showed a number of key choke points in demand and supply. On the assumption that it takes at least 5 years to create a meaningful increase in supply of skilled workers, it was clear that the workforce development plan needed to be implemented immediately.
To facilitate this, a committee called the Strategic Transport Apprenticeship Taskforce (STAT) was established (Strategic Transport Apprenticeship Taskforce 2017) to manage the increase in workers, and a sister committee called Transport Infrastructure Efficiency Task Force looked after the wider plan. An industrial strategy (Rail Supply Group 2016) added further value.
The STAT vision includes the following:
Create a national and regional strategic workforce plans.
Adjust policy and regulation such that procurement can induce greater supply chain forward business confidence (possible target: 3.5 years).
Train 5000 more local people to level 2 or level 3, using pre-apprenticeship provision to increase the proportion from disadvantaged backgrounds.
Assertively link recruitment to these programs, incentivized through procurement.
Orient the skills supply chain to this demand, increasing capacity where required.
Prepare and deliver a development plan for small and medium-sized enterprises (SMEs), and local skills capacity in tier 2 and 3 companies.
Implement a transport policy that leads to government to “levelling out” demand and limiting poaching of skilled workers.
16.2.4 Lessons Learned About Strengthening Investment in Learning and Skills
The first lesson learned is about the power of comprehensive reliable data, presented in an accessible way that resonates with the skills challenges facing policy makers, the private sector, and individuals. Data gathered and presented as intelligence has the power to bring people to the table, instigate dialogue, highlight issues, and make a compelling case for action, even in sectors or organizations that have previously eschewed building a learning culture for the long term, in favor of short-term fixes to human resource problems.
The most effective route to engaging the private sector’s participation in a learning society culture is through the medium of productivity and intelligence related to this. Building on the compelling data already available, and taking this further, a workforce planning approach enables employers to see the costs, benefits, and returns for their investment. This is what NSAR did, and as a result of their analysis, for the first time in recent history, employers became convinced of the value of training and do not need to be compelled to invest in it.
An important finding from a 2-year study (National Skills Academy for Rail 2017) was that change needs a “burning platform”—change needs to become a priority at the Board level. In the more “protected” worlds of regulated infrastructure, the burning platform has often come through regulation. Economic regulation has played an important role in reducing unit costs in regulated infrastructure. Intended as a protection for consumers from monopoly commercial providers, there has been a clear focus on unit costs, with some successes and a body of good practice established. In the UK, this is particularly associated with energy and utility regulator that set the price that producers can charge within fixed time periods known as “RIIO” (revenue = incentive + innovation + outputs) The charging model includes an allowance for the training of new people to address an ageing workforce.
Economic regulation is of necessity sectoral in nature. Issues are complex, technical knowledge requirements are high, and a rich understanding of current business practices are key to effective decision making. However, the businesses that are among the most affected by regulation are often the few, large, tier 1 contractors who work across a number of regulated sectors and have common supply chains. There is a well-documented inefficiency associated with contracting and procurement uncertainty, which is that opportunity arises to “even out” demand, understand the cumulative impact of regulation, and share good practice. This requires an agency, individual, or project to look across the main sectors, and then directly influence decision making.
Where more radical changes in supply chain are required, e.g., to grow a more highly and widely skilled supply chain, other measures may also be necessary. The case of Offshore Wind is instructive. Unit costs of offshore wind power were over £150 ($202.50) per megawatt-hour. Investment was needed to reduce these and increase local labor content and skills. The policy of “contracts for difference” was followed, where long-term contracts with price guarantees were agreed with suppliers, who in turn invested in people and technology. The price dropped to £47 ($63.50), a point where incentives and subsidies are barely needed, and a whole new local infrastructure is in place.
While regulation and procurement may sound technical and divorced from learning, it should be acknowledged that a slew of government initiatives, reports, studies, policies, and even regulations have collectively failed to encourage employer investment in learning, where these measures are succeeding in developing and embedding the culture of a learning society.