I recently met Tom, an owner of a small manufacturing company who was interested in understanding how analytics could help his business. He has a very small company (<20 employees) and used to have a large local customer that accounted for a majority of his business. This large customer recently went bankrupt, and his company like many other smaller suppliers, are experiencing existential threats. How can analytics help such companies in this situation?
I started out with the suggestion that they focus on their costs. Understand what are the key drivers of cost to improve their bottom line. To which Tom came back and said 35% of their cost is raw materials, 25% is labor and the rest comes from rent, utilities and some variable factors. He is convinced that their operational complexity is so minimal that forecasting their costs could add no more than marginal value. The biggest challenge they face today is to recapture their market share which went away with the loss of one large customer. So can analytics help Tony?
Scenario 1: Small business sells online (or wants to sell online) and wants to grow market share
Start with a quick search of your main keywords using something like Google Keyword tool. This will tell you the latent demand out there for products (or services) such as yours. If the demand is substantial, then a quick thumb rule to estimate potential revenue is 0.01% * demand * average sale price. If this estimate is substantial for your business, analytics can help you grow your market share by helping with customer acquisition. Making an investment in appropriate analytics can help you improve your reach.
Scenario 2: Small business wants forecast demand in order to optimize inventory or production planning
A key question we are answering for one of our customers is the following "Given the fluctuating demand for our product, can we optimize capacity for production so that machines will not remain idle or overdrawn for long?" It is very strange to come across a business that has no fluctuating demand, as Tom stated was his case. If demand is truly constant, there is no need to make any forecasts and hence no need for predictive analytics! Another customer is using predictive analytics to make forecasts for their transportation costs.
Scenario 3: Small business wants to keep their production equipment uptime as high as possible
Another small business customer of ours makes LCD displays for manufacturing facilities. Their customers are automotive manufacturers who manage large production facilities. A key requirement for them is to ensure that their machine tools are performing at their peak efficiency and want to have advance notice of potential failure or breakdown. This is a highly technical problem that predictive analytics can effectively solve. The value of analytics here is quite high, because a few thousand dollars of investment in analytics will ensure that the expensive production machinery is rarely down. Our customers will bundle predictive analytics as a value added feature to their LCD displays which will now indicate potential failure or wear situations in addition to basic equipment usage.
There are many applications of analytics for small businesses. The above provides just a few samples.
One final note: if Tom had actually used analytics, for example to run a Pareto 80-20 analysis, he could have clearly understood the disproportionate impact that the one large customer has on his revenue and profits. In his case, probably 95% of his profits came from 5% of his customers. If he had followed through a strategic analytics process, he would have been forced to improve the 95-5 situation to a more healthy 80-35, for example.
Before you conclude that your case is similar to Tony's, consider the above cases. If your small business meets any one of the following criteria, analytics will be significant value add.
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