A key challenge for Professional Services organisations is being able to accurately forecast the demand for its main process input: people, or more specifically, people with the appropriate skills to meet customer requirements. Variability in customer demand for services has always a major feature of this industry, with a lot of work traditionally being delivered as one-off project type engagements. While this situation may be gradually changing, matching supply and demand across the talent supply chain is still a significant business challenge.

There is a delicate balance to be struck between having sufficient resource available to meet unpredictable customer demand (ensuring that the business does not miss out on revenues through an inability to deliver), whilst not incurring the costs to the business associated with running a large ‘bench’ of idle staff waiting for such demand to arrive.

It all comes down to an impact on margin either way and this need, to forecast demand against a moving target, is at the heart of where the challenge lies. By its very nature, forecasting always has an element of subjectivity, particularly in the Professional Services industry. Some inputs to the model are easily quantified, but the further out in time a model needs to span, the more there is a need to add a layer of judgement – this is where emerging intelligent technologies can help. Artificial Intelligence and Machine Learning can deliver more accurate views on predicted demand, reducing this layer of subjectivity and delivering more accurate forecasts.

Clare Campbell-Smith

Industry Principal: Information & Professional Services

Balancing the talent supply chain

Interestingly, a lot of the literature around forecasting the demand for resources is very HR focused, looking at elements such as employment trends, replacement needs, productivity, absenteeism and expansion and growth. In an end-to-end Professional Services business model, to me, a lot of this is more focused on the supply side of the equation. For organisations where people are doing the same thing day in and day out, this may be sufficient.

However, for Professional Services, the real demand-side relates to client delivery, and more tangible requirements coming from in-flight projects and pipeline, and therefore is not adequately considered in such models. In this case, businesses need to operate in a much less siloed way. What is key to getting a proper view on the balance in the talent supply chain here is collaboration across the organisation – drawing together inputs across HR, resource management, project delivery and finance.

Components of demand

Just like when running Sales & Operations planning for a product-based business, the focus, inputs and granularity for resource planning vary over different planning horizons. By breaking down the inputs to the forecast into its constituent parts we can make the challenge more manageable.

Backlog

This is the easy bit. Over a short time horizon – and here we are thinking of a 0-12-month scale, demand for skills can be fairly accurately based around actual work that the business has already contracted with customers. This demand can be built bottom-up based on detailed resource scheduling data over the life of the contract. So long as projects are planned accurately, then this information is reliable but the integration between the resource allocation element of a Professional Services automation tool and the forecast engine is the key to this process. Even here, intelligent technologies can be used to further increase the accuracy of the forecast. For example, looking back across a history of similar projects previously delivered, and adjusting demand based on metrics such as project over-runs, and the impact this has had on resource consumption.

Pipeline

Here it gets a bit more complicated. It can safely be assumed that an organisation will win some percentage of the opportunities and bids that its sales team is working on. The challenge is forming an accurate view of what that percentage is likely to be, to be able to weight the total likely demand from the pipeline. Estimation gets more accurate the further down the pipeline an opportunity travels. This again means, that pipeline based inputs to the overall forecast are only of significant value over the short to medium term.

Traditionally, the challenge here is the reliance on the sales force’s assessment of the probability of winning work. As this can only ever be subjective, it will often lead to over or underestimation of resource requirement but this is where intelligent technologies can add real value. For example, using machine learning instead of a salesman’s judgement to predict win probability more accurately based on the organisation’s track record, add to this the ability to do simulations on the forecast: “what if we were to win everything in the pipeline” or “what if we win nothing” and then compare the various impacts on the business, and we have a powerful tool to model different potential business outcomes.

Long term forecast

Here, we are looking much further out into the future (say, over the next 1-5 years), wherein a rapidly changing economy, much more is unknown. Key inputs are the longer-term strategic plan for the business (growth plans, new lines of business or services, etc.), and the supporting detail in terms of a broad view of the type of organisation in terms of headcount and skills that will be required to support than vision. In situations where there is no historical precedent, then Artificial Intelligence and Machine Learning (certainly in the early stages of development that it finds itself in today), is often of less value, as a degree of intuition in decision making is required.

Blended demand model

By combining these various inputs, we come up with a blended demand model. This can easily be reported against the supply side of the equation (people, their skills and availability from the HR systems), and can then form the basis of actionable insights for the resource allocation, HR Learning & Development and all recruitment processes.

As part of the roadmap of innovations around Keytree’s ‘Bridge for Professional Services’ SAP Qualified Partner Packaged Solution, we are currently in the process of developing a product to address exactly this business challenge.

To find out more about Bridge for Professional Services contact Claire Campbell-Smith, Industry Principal: Information & Professional Services – clare.campbellsmith@keytree.co.uk