This page contains a description of some of the demand forecasting solutions we at Kmbara provide. Our forecasting solutions use advanced advanced technology, machine learning and statistical models to help professionals in a variety of industries succeed.
Who wouldn't want to know the future? But obviously, it's extremely hard to predict. Investors want to know which industries and markets will grow, and how much. Business owners want to know which disasters or challenges are coming, so they can prepare. The world is so complex, and our understanding of it so incomplete, that these things are extremely difficult to know with any certainty.
One of the simplest but most crucial things a business owner will want to forecast is demand: how many customers will I have tomorrow, how much labor and inventory will be required to serve them, and how can I prepare now to meet that expected demand. If we under-predict demand, we may run out of inventory and labor to meet customer needs. If we over-predict demand, we'll lose money paying for employees and products that have to remain idle. Every day, business owners need to make this prediction and act on what they predict. But in our complex, ever-changing world, it's a great challenge to predict accurately and prepare well.
We at Kmbara have solid expertise in the most advanced forecasting methods developed by top statisticians, computer scientists, and forecasting experts. We can create forecasting applications, API's, and services to help businesses know what's coming and know exactly how to prepare for it. Below, you can read more details about what we can do and how we do it.
Our solutions use advanced quantitative analysis and custom software tools to provide the forecasts and recommendations that business owners need. Here, we'll present some details about the core aspects of our solutions.
Every forecasting solution is different, and each will require a different set of tools and a different approach. At Kmbara, we have broad expertise. We know about every different kind of forecasting, and we can find and create the right solution for you.
Typically, the first step in the creation of a forecasting solution is data collection. We'll want to collect detailed, historical records of inventory, sales, and demand, going back as far as possible into the past. We'll also want to collect any other data that could be pertinent. Depending on the business, weather historical weather data could be useful if it gives us insight into the relationship between weather conditions and demand. We could also collect event calendars and local news to understand contextual influences on past and future demand. In general, the more data we can collect, the better.
After collecting data, we'll want to create and optimize a statistical forecasting model. Our models usually rely on machine learning methods, like linear regression, random forests, and for advanced needs, neural networks. A simple model could use past demand data to fit a linear regression. Future forecasts would simply be forward extrapolations of our historical demand to future time periods along the curve defined by our regression. Models like ARIMA can rely only on past demand data, while other models can incorporate other data including weather news, and anything else.
Our forecasting models tend to become more accurate as we add more data. We can use tools from other data science domains to properly manage the data. For example, if we have text data, like news headlines, we can use natural language processing (NLP) methods to convert unstructured language data to structured, regularized numerical data. We can even perform "sentiment analysis" on text, where our software can determine whether text is positive, negative, or neutral about a given topic. The latest sentiment of reviews, blog posts, or news stories can provide useful indicators of likely demand changes.
After building our statistical model, we're not yet finished. We'll want to optimize our model to make it as accurate as possible. We use the best practices in machine learning, including training/test splits and cross validation, to make sure we avoid overfitting and underfitting, two common problems in forecasting. Even after deploying a forecasting solution, we'll re-evaluate it periodically to make sure it's performing as well as possible, and adjust it as needed to keep it on target.
Armed with an accurate, optimized statistical model, we'll deploy it, in one of several forms. Deploying a statistical model could be as simple as receiving recent data from a client and sending a single number back (the forecast for tomorrow's demand). A more advanced deployment could use an API for the data transfers. More advanced still, we can create interactive web applications that allow dynamic user input and smart visualizations.
Accurate forecasts can be interesting and very useful, but it's even better when knowledge can lead to real action. We can go a step further than only forecasting and provide recommendations for staffing decisions: how many staff to assign to work on a particular day or time, and how many staff to hire and terminate in particular markets or for particular assignments.
The simplest way to do workforce planning is to base them entirely on raw demand forecasts. If we forecast demand that would seem to require two employees to meet, then we staff two employees, and so on. This simple approach is a reasonable first step, but fails to take into account risks of "overage" and "underage." In other words, we may estimate that efforts from two employees are required, but our forecasts are slightly wrong and actually three employees are required (an example of underage) or only one employee is required (an example of overage).
Two things can allow us to mitigate the risks of underage and overage. The first is a quantification of the likelihood of each. We can obtain these estimates from our original forecasting model, depending on the exact method it uses. For example, a linear regression model will provide estimates of the standard deviation of each forecast point estimate. The second is a quantification of the costs associated with each. Overage costs are typically easy to estimate, since they tend to consist of only the costs of labor which are usually closely accounted by the company. Underage costs are harder to estimate, since they include reputation costs, customer service costs, and other intangibles.
After making these estimates, the most advanced workforce planning efforts will use computer simulations to understand the likelihood of future events and their likely consequences. We create computer simulations based on some techniques developed in academia for "agent-based modeling."These techniques are used by economists and government emergency specialists worldwide, and they allow for careful and meticulous planning of even the most complex scenarios.
After making these estimates, the most advanced workforce planning efforts will use computer simulations to understand the likelihood of future events and their likely consequences. We create computer simulations based on some techniques developed in academia for "agent-based modeling."These techniques are used by economists and government emergency specialists worldwide, and they allow for careful and meticulous planning of even the most complex scenarios. In addition to staffing planning, we'll also make recommendations for hiring and attrition decisions based on demand forecasts for the far future.
Just like we do for forecasts, workforce planning is something that requires constant re-evaluation and optimization. After seeing the performance of our forecasts and workforce plans, we can make constant adjustments to get the best possible outcomes.
Forecasting is hard, but it's very possible! We at Kmbara have the expertise to create software tools and quantitative analyses that make you smarter, that makes your business better, and that makes your life easier. Contact us today to find out more about what we can do for you.
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