How can IT help a manufacturing company more accurately monitor, control, and predict cost associated with factory operations spread around the world?
Traditionally, planning and analyzing factory costs across globally-dispersed operations has been a dark art of sorts – one that involved spreadsheets, deep knowledge of organizational silos, and tremendous amounts of legwork to gather and process all the required data. Which factory spent the most money last year? Where are the bulk of my labor costs? Simple cost planning questions such as these were difficult to answer.
With augmented analytics, IT sets the groundwork for easier planning and greater visibility – starting with a consolidated view of data across silos and timely information augmented by machine learning – so users and managers can make more accurate, timely business planning predictions.
The comparison of historical spend to the prediction helps an analyst understand how closely the real costs come to the predictive algorithm.
A business analyst can view the variance analysis for plan versus forecast for all factories in a single view.
The analysts should now be able to run a quick simulation that increases the planned overhead cost. The adjustment will be instantly reflected in the total planned budget for the organization of the screen and on the variance chart – helping to speed up the planning process dramatically and with greater accuracy.
Whatever offers you develop, you first need to understand your customers before designing appropriate promotions to fit their current and future needs.
You can spend precious marketing resources sifting through data to design promotions that miss the mark or don’t deliver the desired results. Also, manual, spreadsheet-based analysis can be complex, time consuming, and error prone when you’re trying to pull all your customer data together to help identify the right prospects and right offers based on previous buying behavior.
With augmented analytics, you can ask relevant questions. Who are your most profitable customers in a given region for certain product and service offerings? What were the top influencing factors for their past purchases? Why did they choose your services rather than the competition? KPIs to monitor might include the number of existing purchases or online shop visits – or perhaps the average number of calls or text messages sent in a quarter.
In this scenario, machine learning is used behind the scenes to sort through volumes of historical customer data. By detecting patterns in customer profiles and past buying behavior, machine learning algorithms can predict the likelihood of any particular customer or prospect to respond positively to a new offer or campaign.
Augmented analytics changes the game. IT can empower sales and marketing teams with immediate, trusted insight – powered by machine learning – into which customer segments are the best candidates for new promotions. Think of it in terms of investment in your business. Now the business knows which customers are best targets – resulting in higher ROI for campaigns and marketing efforts.
Every company wants to manage their costs efficiently and effectively. That task can be particularly challenging if you have to resort to manual, spreadsheet-based processes. For example, if you’re a business analyst in finance or controlling and you need to forecast travel and entertainment (T&E) expenses for the next six months across the different departments in your company, you would have to aggregate all relevant historical data across departments, which often run in silos and where sharing data doesn’t always come naturally.
In addition, you would also need to combine the historical data with various calendars of events that will require travel. Most businesses have an annual rhythm; likely, you already have a sense of the events your employees will be attending – the annual company meeting, prominent industry conferences, customer visits, and so on. But things do change, as we’ve clearly seen past year, which means you need to be able to aggregate all historical, new, and revised data to assess your situation at any given moment.
Why the close tracking of actuals versus the forecast? Because the forecast is generated using smart predict technology that mixes historical and new data – providing more accurate forecasts and greater confidence that costs won’t run over budget.
By drilling down further into the underlying data, it is possible to perform a detailed deviation analysis, using variances to better understand the relationship between budgets and actuals.
By using machine learning algorithms, patterns can be detected in the historical spend data to establish a baseline model. Then you can incorporate the actuals for what’s happening right now and run simulations based on future-looking data, such as calendars of events.
The role of finance becomes less about gathering data and divining its meaning and more about using smart technologies to generate more timely, accurate forecasts. As a result, you’ll have time to focus on driving business strategy and additional mission-critical activities.
If you’re an HR manager and your company has an aggressive growth strategy, you need to retain your high performing employees and hire additional top talent to drive your strategy forward.
As such, HR needs to keep an eye on attrition rates, identify areas where attrition poses significant risk, evaluate the reasons why, and take measures to rectify the situation.
With augmented analytics, built-in machine learning capabilities help you detect patterns in historical data and easily identify potential attrition risks by various categories (geography, age, salary, and so on). HR planning is more effective because you can easily forecast – or predict – attrition for the next quarter with the help of machine learning algorithms.