3 examples of ROI from analytics for small and medium business
As a small business, how much investment are you willing to put into data and analytics? If one of the most significant returns is time saved in performing your current decision making activities, would that not be enough to justify the effort? From our experience in talking to many small and medium sized business, the awareness of the returns from investment in data and analytics is somewhat low. Fortunately, that is changing today and will continue to improve thanks to the media spotlight on analytics in general.
One of our small business customers was very much taken in by the buzz on analytics, that they wanted to start off with major changes to their current (inefficient) quote management process. Their business is building custom machinery for testing of manufactured components. All their products have a basic mechanism combined with a whole set of options for measurement, settings etc. The cheapest product they build costs tens of thousands of dollars and goes up from there. Clearly their product is quite complex and accordingly it requires several weeks to develop a new quote for their customers because of the need to explore past work orders, current commodity costs, labor costs, engineering costs etc, before they can arrive at a rational and profitable quote. There are obviously several problems with this current state of affairs:
- It is very time consuming
- It is prone to errors
- It is difficult to quantify the likelihood of success of the final product - i.e., whether the quote will turn into an order or not
The one challenge they were focused on is understanding from the past history of successful quotes and using these insights for developing new quotes. Clearly this was a very complex challenge for a company that is just now starting to look at analytics. Our suggestion to them was to first invest in cataloging all their past quotes, work orders and invoices. In other words, to spend time on managing and organizing their existing data before stepping on to the next level. The next level for them clearly is using analytics tool such as basic dashboard to understand their sales history, to see if they can detect patterns around lead times, success rates, types of customers which lead to successful orders, options on successful quotes and so on. From that point onwards, marching towards using advanced models for the quoting process and deploying predictive analytics to improve their ROI becomes a natural progression.
We are very sensitive to the analytics maturity level of small businesses and recommend the following five step process to get the full benefit of investing in analytics.
- Understand which decisions you can make better, cheaper or faster using data
- Develop processes to consolidate data from different silos
- Build appropriate models - descriptive or predictive
- Develop an app to deploy the models and monitor the measures that are relevant (based on 1)
- Regularly feed new data to the model to maintain it
This process is encapsulated in our affordable analytics model shown in the graphic below. With the availability of open source software and crowdsourcing of data science talent, the most expensive step in this very affordable process is probably the first step.
Several of our customers are successfully using this scheme in one or more of the following ways.
Monthly budgeting (predictive): The challenge was to come up with monthly transportation cost forecasts. Our customer is using forecasting models to develop rational and data-driven budget estimates. While the earlier process was simply guess work, now they have a rigorous data driven approach to make high quality estimates.
Production scheduling (predictive): The challenge here was to plan production schedules. Our customer makes multiple product lines, but without having a good idea of what the likely demand would be for each of the major products, planning for equipment and labor was simply too time consuming and involved guesswork. Now, with a dashboard to predict demand for each product, our customer is able to come up with solid schedules for their limited resources.
Manufacturing overhead tracking (descriptive): The challenge for this customer was to keep an eagle eye on spending and costs. In particular their manufacturing overhead costs, which in some weeks could be as high as 90% of their total costs. The CEO and CFO would spend several hours gathering and cleaning data to bring it into a spreadsheet and then run the overhead calculation. With our easy to use manufacturing overhead tracking app, they have a ready made tool which monitors the key metrics in real time and is more reliable, because it is less prone to simple data entry errors. Clearly the executives' time is better spent on more value added work than on such mundane but critical calculations.
Get in touch with us today if you are small or medium business that is looking for affordable solutions for your analytics!