23 Sep Moving Beyond the Static Report – A Flexible Approach for Uncertain Times

 

Case Study – Industrial Land Supply and Demand Modelling

“The traditional approach to delivering projects relies primarily on static reports that provide fixed results at a specific point in time.”

Your project’s timeframe is many months. A technical report is commissioned. A lot of effort is put into detailed background analysis and customised research. The report is delivered, and the insights provide a great foundation to continue your project. 

As the project progresses to subsequent phases, the insights delivered in the earlier report become outdated. In relation to some aspects, conditions have changed so much that the initial report is obsolete and needs a complete review before you can finalise your project!

The Problem

A traditional approach to running projects typically relies on static reports delivering fixed results at a specific point in time.

The Alternative

Leverage the time, budget and mental effort spent in preparing the report to build a dynamic modelling tool as part of your project deliverables.  A tool that allows the analysis to be updated as new information becomes available, adapts to changing circumstances, or to run scenarios based on certain parameters.    

The Example

Industrial Land Supply and Demand Analysis

Land supply analysis has two key components:

• The stock, or the supply of land at a point in time
• Demand for land over time.

In simple terms, assessing the stock is typically a matter of undertaking an audit of available land and then including any planned future land releases.

Forecasting demand is often more complex as there are a multitude of variables that will determine a possible future state. For this project there were several key variables that went into forecasting demand, each with their own set of unique inputs.  These are summarised as follows:

• Forecasting employment by industry
• Categorising jobs by industry into land use types
• Ascertaining the propensity for jobs in specific industries to locate in designated employment areas
• Modelling the likely land area required to accommodate new jobs for specific industries.

The specifics of this process are considerably more detailed, involving thorough desktop reviews, regional benchmarking, data analytics, and modelling within analytical software applying ensemble modelling techniques and stochastic simulations.

There is a lot of technical work that goes into preparing demand forecasts.

The unique element of this project was that REMPLAN not only delivered a final report, but also developed a customised online tool that the client can use to update analysis as required on an ongoing basis. Behind the online tool are all the same inputs that went into the report.

The video below illustrates how the online tool can be used to model changes in inputs.

The Benefits

With an online tool, such as that developed by REMPLAN in our example above, you are able to update information as circumstances change, or even run various scenarios to plan for various possible outcomes. Such scenarios might include:

• A significant new business has moved into town, creating many new jobs
• A development application has been approved for a large site, thereby removing a large portion of land supply
• Declining activity in an industry sector over time.

A static report cannot account for such changes, so when it comes time to complete your project, deliver the strategy or update your policy, the data that you are basing a decision on may not be current.

By moving beyond a static report, you put yourself in greater control during the course of your project, are able to deliver timely and evidence-based updates to project control groups, and potentially avoid the need to seek additional budget to update your outdated analysis via third party consultants.

Interested in an Industrial or Commercial Land Supply and Demand Analysis for your region?

A bespoke online analytical solution to support your project?

Contact the team at REMPLAN: 1300 737 443

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