At Yamaha Motor Finance France, you recently deployed IBM Planning Analytics. What were the objectives and challenges of this project ?

« Yamaha Motor Finance France was created in November 2018, with a team of less than 20 people, with highly-skilled profiles, who had to be able to establish the appropriate organisation in order to meet the requirements of a large group, focused on production, marketing and promoting a network. But also to meet the requirements of the banking sector. Planning Analytics is a tool for planning, budgeting, forecasting and analysis, and is used by Finance teams.

We are a finance company. Our mission is therefore to offer financing solutions relating to products of the Yamaha brand at 2 levels: the wholesale part concerns the financing of dealership stocks and the retail part on the financing of the end customers.

You should know that the banking sector is a sector that is highly regulated. Regulation was strengthened following the 2008 financial crisis. We therefore have a license to be able to carry out our activity. Regulators control and monitor our stability, financial structure and liquidity ratios.

How did you prepare the project ?

I created a data warehouse to render the data transactional, reliable and available in a decision-making format. We were able to feed a BI tool, enabling the data to be made available with a high level of granularity, without any particular analysis. But also to feed our specific tool for regulatory reporting.

What changes has Planning Analytics made ?

Today, if I put on my management controller hat, I know that hours of work is spent in Excel to import data, work on it, make calculations from every angle, try to reconcile the numbers systematically. Once all of these obstacles have been overcome, we submit the information that is no longer relevant, because there have been changes in the assumptions in the meantime. Since Planning Analytics was set up, every morning the financial controller has his updated re-forecast based on the data of the day before. He can therefore make new assumptions, play with different levers, analyze what happened to understand the past and be able to better anticipate the future. The management controller can create scenarios, duplicate them and compare them with each other, to be able to analyze and understand what happened in relation to what was expected to happen and thus have a better projection of the future. Clearly, the tool allows us to be able to rapidly deliver up-to-date, reliable information having undergone a permanence of methods in the way it is built.

To know more about IBM Planning Analytics

You work on 2 perimeters: wholesale and retail. How did you build these two perimeters ?

The approach, modeling and levers of these 2 perimeters are totally different. This therefore necessitated a compartmentalization of data in order to facilitate relevant analyses. Of course the application core model defined, is both structured and structuring and the same for these 2 perimeters. This allows us to consolidate information with the same level of granularity. The financial controller is already looking to integrate additional perimeters, which of course will have to respect this core model application and correspond to the company’s analytical perspectives. We would like to integrate the personnel costs.

The fact that we are a newly-created company and that our tools are all being implemented at the same time, means that we are in a very dynamic context. So we must double up on agility to be able to fulfil yesterday’s needs – not yet met, today’s needs which are concrete, and future needs that we may not yet have thought of but that must already be factored into the application.

IBM Planning Analytics has now been in place for some time. What do you think ?

What I think of the application is not really important. What is important is what the end user thinks of the tool. So if we consider their feedback, for example that of our financial controller who uses the application on a daily basis : he notes that the application is complex, demanding but very adaptable to his needs. He appreciates the different levels of information that a planning tool like Planning Analytics can offer, with different granularities, depending on what he needs to do. Another positive point: the availability not just of financial information, but of business information: statistics, quantities, percentages, ratios, projections.

One of the difficulties he observed with the project was the fact that it was necessary to transcribe the Excel method used thus far into a tool that does not work like Excel.

As for the General Manager who was the sponsor of the project, his takeaway is that it is a powerful tool that totally meets his needs: data integrity, centralization of information, permanence of methods, a different depth of analysis. The way we set it up allows us to play with the various levers, which gives us insight into the management of the company and its strategy. He notes that the implementation of the project was facilitated by upstream anticipation and work on the data, which allowed us to be able to feed the tool. He also notes that internally we were in possession of all of the skills required for the implementation of such a tool, be they business skills or technical skills. The fact that the objective was consistent greatly helped us to define the priorities and how to proceed to achieve this objective.

The CEO, who consults operational kpi for steering on a daily basis, notes quick and easy ownership of the tool. What stands out for him regarding the project is that he is oblivious to technique and technicalities inherent in the project, which for us, the project team, was the best compliment we could have received.

Finally, the Sales Manager, who consults the operational kpi, notes ease of use, data availability and stability in his presentations, since he does not need to redo all of his tables which are automatically updated. What was important to him in this project was a clear definition of the need and to be able to communicate amongst the different professional domains. We do not speak the same language – computer data does not make sense to a salesman. It was important to succeed in translating all of this in order to meet the overall need.

Why do you think the deployment of this tool is a success ?

We had a competent team, who knew the business very well and who knew the technical aspects. We chose a tool that meets our needs, or at least that is capable of meeting our needs, and lastly, and I think that this is perhaps the most important of all, the data was prepared, structured, organized, available and reliable. »

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