This page is designed to answer the following questions:
- 1.3 Importance of accurate forecasting for resource management (Level 5 Diploma in Leadership and Management for Adult Care, Resource Management in Adult Care)
NOTE: This page has been quality assured for 2023 as per our Quality Assurance policy.
To manage resources effectively, accurate forecasting is necessary. This is the process of making predictions about future needs so that resources to meet them can be planned for accordingly.
Your annual strategic plan should address how resources will be used over the coming year to ensure consistent high-quality care.
On this page
How accurate forecasting is informed
Accurate forecasting should be informed by:
- Historical knowledge
- Knowledge of any changes that can reasonably be expected in the future
For example, if your previous year’s figures indicate that you had a budgetary spend of £5,000 on Personal Protective Equipment (PPE), as long as everything remains the same you can expect a similar expense the following year. However, if you are aware that you will be taking on additional clients over the next 12 months then this figure will rise accordingly. Typically, year-on-year things will not be exactly the same – even if there are no changes to operations, inflation should still be taken into account.
Similarly, if staff surveys and reports from your previous year’s operations inform you that:
- Your employees have had to work a lot of overtime to meet the needs of the organisation.
- Your employees report high-stress levels and poor work/life balance.
Then you may wish to look at recruiting additional staff to cover the deficit or using the services of an agency during busy periods. A recruitment drive may also be necessary if you are aware that several members of your existing staff team are planning on retiring over the next twelve so that you can plan their succession with minimal disruption to services.
Quantitative and qualitative data
Both quantitative and qualitative factors should be taken into account when forecasting.
Quantitative information refers to numbers and measurements but does not show the whole picture. Qualitative data is primarily descriptive and can not easily be measured numerically.
For example, referring to the staffing example above, quantitative data would indicate that during the previous year, the organisation has been able to meet the needs of the care services it provides with current staffing levels. However, qualitative information (for example, from staff surveys) would indicate that although needs were met, staff are feeling burned out and will not be able to manage current workloads indefinitely.
Accuracy, reliability and validity
Whenever data is used to inform decision-making, great care must be taken to ensure that it is accurate, reliable and valid.
- Accuracy – how closely does the data align with reality?
- Reliability – how consistent is the data?
- Validity – is the data suitable for its intended purpose?
For example, if you were collecting data about the cost of PPE used over the previous year so that you can budget for the next 12 months, you may decide to ask a sample of employees to estimate how much PPE they use over the course of a year and extrapolate their replies to cover the whole organisation. Because you are using estimations, the data would not be very accurate – some employees may overestimate and others underestimate the true figure. Similarly, because you get vastly different estimations, the data would not be very reliable. Although the data is inaccurate and unreliable, it would still be valid because you can reasonably expect historical data to predict future requirements, especially in the short term. However, a valid method using inaccurate and unreliable data would yield inaccurate and unreliable results.
A more accurate, reliable, and valid method would be to examine the previous year’s complete financial records of spending on PPE (taking into account any anomalies such as the pandemic, or pre-existing stock levels).
Forecasting is uncertain
Although forecasting is important for resource planning and management, it is important to understand that no forecast will be 100% accurate. This is because some of the data used will be subjective and we simply cannot predict the future. For example, at the beginning of 2020, care providers could not have predicted the Covid-19 pandemic and the impact it would have on care services across the country.
Despite this, forecasting is still an essential component of managing resources effectively because it reduces the risk of uncertainty. It ensures that resources are managed optimally when things go right and allow you to have plans in place when things go wrong. The chances of major changes to your operations (such as the Covid-19 pandemic) are very slim but when they do occur, it means adjusting your forecast accordingly – it is much easier to change an existing forecast than start afresh.
Forecasting also gives you the opportunity to notice trends in your data that could provide an early warning to avert disaster (such as stock levels running dangerously low) or alert you to an opportunity (such as identifying services that have an upward trend in demand).