5th June 2019
The role of advanced analytics in business planning processes is becoming a necessity, particularly in order to optimise the supply chain. And the key to building efficiency and overhauling logistic practices is twofold;
- Decisions need to be bolstered with advanced analytics
- Those analytics should be placed in the hands of decision makers
Advanced analytics works by analysing real-time data, predicting future scenarios and recommending complex, profitable decisions on the spot. Leveraging the spectrum of advanced analytics is a must for future successes, but understanding how to quickly act on insights from analytics is becoming just as crucial.
Let’s start by taking a look at two key kinds of advanced analytics.
- Predictive analytics uses forecasts and statistical models to judge and provide recommendations about what could happen.
- Prescriptive analytics uses optimisation or embedded decision logic rules to find out what should be done in a certain situation.
The Difference in Value That Both Techniques Bring to an Organisation
Although both methods offer tangible benefits, the results from prescriptive analytics usually far outweigh those from predictive analytics. While this is due in part to the scale of operations, it’s also affected by the types of decisions made as well as the ability of prescriptive analytics to optimise decisions.
Predictive analytics tend to focus on a relatively narrow set of parameters for short-term risk analysis. While this type of analysis can result in huge rewards by limiting risk, it’s unlikely to be in the same order of magnitude as a prescriptive analytics solution. Such a model can identify the most profitable products, pinpoint the best markets and identify optimal strategies for business growth. We can also use prescriptive analytics to explore multiple what-ifs, options and trade-offs without being limited to predetermined scenarios. From what kind of an offer should we make to each customer to which product should we launch and when, this solution will allow companies to answer the burning questions and prepare accordingly. All are undeniably key factors for achieving success and striding ahead in the industry.
The Difference in Technology Requirements
The fast pace of business today makes it imperative that line managers and executives have direct access to these analytical tools. While this doesn’t mean they should be involved in programming and data cleansing, it does imply the provision of end-user tools and dashboards that allow them to interrogate results themselves. This hands-on approach builds confidence in the tools as well as presenting on-the-spot information to support decision-making.
The first step is to clean up and combine the data so it’s usable. Next, various analytical techniques are applied, such as machine learning techniques and neural networks. Prescriptive analytics takes this a step further usually using one of two types of analysis: . Both have their positives, but in order to make the most of their potential, businesses must assess and consider which option would suit their preferred outcomes.
Prescriptive Analytics Guide to Long-term Decision-making
The key difference between predictive and prescriptive analytics is that the former provides short term metrics that help understand what’s happening in the organisation, whereas the latter provides answers to what should be done. While predictive analytics measure metrics in isolation, it won’t evaluate the overall impact. For example, it can measure and predict an organisation’s sales performance but won’t necessarily measure the impact of increased raw material costs on cost of sales and profitability.
Ultimately, prescriptive analytics model businesses while taking into account all inputs, processes and outputs. This means models are calibrated and validated to ensure they accurately reflect business processes. And this kind of advanced analytics will recommend the best way forward with actionable information, helping to maximize overall returns and profitability to support informed decision-making and a more innovative supply chain.