Towards data driven company
Nowadays companies face many challenges depending on which industry they operate and consequently how quickly they can adjust to the movements and constant changes in the market. To this end, it would be sensible to raise a question: “How to constantly improve business decisions on a daily basis?” One way to achieve that goal is to estimate future sales, in other words making sales forecast (check out a real case of sales forecast in the following article). But to do that, one has to think about many steps that are hidden in the background. When a company decides for this action, one can influence on production planning to reduce disruptions on the production line, level of stored inventory by minimizing it so that you can avoid high cost of inventory, reduction of out of stock to avoid losing money in case of insufficient inventory, procurement by optimizing the production of stock and optimizing the logistic, and inventory management automatization. With process of sales forecasting one can plan future orders, detect future growth and decline. Forecasting process In order to start with the process of forecasting, you first need to collect the data and prepare it for the further steps. However, some companies already face challenges with collection of the data, since they don’t know how to do that, where to start, what data to collect (internal and external) and which tools to use. And companies that do so in principle, encounter another problem which is poor data quality such as delayed and inaccurate data with missing values and often ambiguous content having more than one possible meaning. For every company it is requested to collect accurate, complete and unambiguous data in a timely manner since they are result of company’s business processes and hence outcome of forecasting model depends on it. In short, the company must manage the data as valuable assets. After collecting and preparing the data, the next step is to analyse the data. Before the analysis, these are only numbers, which don’t tell much, so we need to put them in context. Therefore, the purpose of the analysis is to extract as much information and actionable insights as possible. Through this process one can detect possible trends and events, discover possible seasonality and extract any pattern changes in salesforce activity in order to obtain a better understanding of company’s customers and their behaviour and habits. Based on that, a company can also make more informed business decisions. The third step is to create forecasts based on collected (historical) data by using different statistical models and machine learning algorithms. However, prepared forecasts by itself do not have business value if it is not used in some business process – as for example inventory optimization – which is the final step. Here, for example, we ask ourselves: “How much do we need to have in stock in some future time period in order to reduce or avoid out of stocks and consequently avoid opportunity costs or miss an opportunity for sales, respectively?” To conclude, forecasts are created to anticipate the future, making them important for planning activities. It is essential for example for sales and inventory management, resource allocation, automatization and future growth. It is an important step towards becoming a data & AI driven business.