When an equipment pooling company needed accurate growth projections
We gave them the answers with granular precision
Industrial Products Case Study
Financial Analytics: Financial Performance Management
Our client is a global leader in the equipment pooling industry, helping manufacturers and growers transport their products to distributors and retailers. Combining superior technology, decades of experience and an unmatched asset base, they handle pallet and container supply chain logistics for customers in the consumer goods, produce, meat, home improvement, beverage, raw materials, petro-chemical and automotive industries.
The company handles more than 3 million equipment movements per day and operates from over 500 service centers serving 500,000 customers. They asked Deloitte to help them with the process of testing their budget and growth figures.
The client previously predicted their budget and growth based on macro measures. Deloitte introduced and demonstrated how our growth model could help them deliver a more informed result. We did this by using other external indicators to identify and predict growth patterns so our client could derive a more granular and reliable forecast.
The challenge faced was to use seven years of data across nine countries, and covering 32 sectors, to create projections for each market sector in only a six-week timeframe.
We needed to flip the company’s entire thinking about the way they used their data. Previously they were basing their budget forecasts on one external macro figure. Our mission was to show the client that while this measure was a good place to start with growth predictions at the portfolio level, it was not necessarily the best indicator to use for all industry sectors. We needed to show the client that other external indicators may affect them more directly.
How we helped
We built and applied a lens across the company’s internal data to provide a view across 32 different industry sectors. By doing so, we were able to show historically whether a sector had grown, declined, or remained stable over time. This insight showed our client that there was growth and decline occurring in various sectors which they had not expected.
We used this growth chart per industry sector to look at how it was affected by external indicators, e.g. GDP, interest rate, foreign exchange, etc. This combination of data for the 32 industry sectors was then used to build a mechanism which recognized if there was any correlation statistically between the internal and external indicators.
The mechanism used was the ‘secret sauce’ to this project. It was able to consider thousands of combinations of internal and external indicators to statistically determine if there was any correlation between them. The technology was able to pick every permutation of hypotheses and test them against internal and external indicators to see which was the strongest.
The main things coming out of the mechanism were:
- There were challenges in the client’s portfolio that were not visible in recent years. Some sectors had
no growth whatsoever.
- When we separated all the sectors into component parts we were able to show that there were better relationships being established with other indicators in the external domain other than the indicator previously used. We could see what each sector was correlated to and show the company that they should be tracking their performance against other more accurate and timely indicators.
- It gave the client another view on how comfortable they would be with putting their findings/predictions forward. Where they had traditionally based their predictions on just one indicator, they now had analysis and information showing where the industry hot and cold spots were and where the client would be most challenged to hit the predictions.
- The results showed the client where they needed to concentrate on attaining new and retaining
existing customers and where they should consider ceasing operations.
We were able to give the client insight into where they should put more resources to save as well as to win customers and improved their future decision-making:
- Providing the business with greater insight and clarity into the challenges across the nine countries.
- Showing the company that some of the existing long-held traditions were statistically not to be relied upon.
- Explaining drop-off in performance, and highlighting possible causes.
- Ensuring that the right information was getting to the correct people in order to make important decisions. There were times when information was not getting to everyone in the organization.
The company is planning to review their growth predictions again, one year into the program.