Planning analytics combines elements of financial planning, budgeting, and predictive modeling to help businesses optimize their operations, manage budgets, and plan for the future.
Why is Planning Analytics Important?
Planning analytics helps organizations make decisions based on accurate data, ensuring outcomes are more effective. Without planning analytics, operational decisions would have to rely on estimates and best guesses based on past experiences, resulting in more course corrections that lower productivity. Most businesses have abundant data to assist in decision making, but they lack tools to exploit data assets, leading to losing business to competitors who use analytics effectively.
Applying Planning Analytics
Planning analytics involves the use of various tools and algorithms.
- Spreadsheets are frequently used for planning analytics to organize and manipulate data, create financial models and perform “what-if” scenarios. Forecasts can be built in spreadsheets by using statistical functions and techniques, including moving averages, exponential smoothing, and regression analysis. Additionally, spreadsheets allow for the visualization of data through charts and graphs, facilitating the communication of insights to stakeholders.
- Business Intelligence (BI) tools allow planners to create complex models, forecast trends, and monitor key performance metrics. Interactive dashboards and data visualization help communicate insights, while self-service queries provide users with access to relevant data.
- Predictive Analytics using techniques such as machine learning (ML) can result in more precise forecasts. Inferred outcomes are continually tested and refined to improve the accuracy of future predictions.
Businesses typically align annual planning based on their fiscal year. Business units request operational and capital budgets to meet their planned costs. Cash flow must be planned to meet expected income and expenditures. At the corporate level, each line of business cost must be rolled up, including currency conversions. Human capital planning includes hiring, contractors, and attrition.
New business initiatives must consider financial impacts and anticipated revenues. As most new initiatives have a ramp-up before becoming profitable, breakeven points must be calculated. Plans must include the risk and cost of failure and potential returns from recurring revenue.
The IT function used to be very planning heavy as servers and data centers represent large capital investment items, and upgrades are expensive. Thanks to the mainstream nature of cloud computing, IT planning is less critical as new hardware, upgrades, and additional resources are available on-demand and only impact monthly operational budgets. IT capacity planners today focus on maintaining application performance and selecting appropriate cloud-server classes.
Planning Analytics by Industry
Retailers use planning analytics to predict what products to stock based on customer demand, factoring in seasonal variations. Retail logistics planning includes organizing delivery timeslots, filling trailers to match the order of deliveries, and yard logistics at distribution centers.
Planning analytics in manufacturing optimizes raw materials or parts and raw materials supply to balance production to meet anticipated product demand. Warehouse planning expands into storage of supplies in trailers in the yard and preparing for shipment arrivals. Shipment planning includes planning storage for finished goods, holding incomplete products and distributing products.
In the airline industry, planning analytics plans for a passenger flight two years out to ensure that a plane is full of passengers to maximize profitability. Railways plan timetables and the number of coaches in a train to match the anticipated number of seats required. When planning shipments, trucking companies often use planning transportation management software to plan capacity and optimize routes.
Construction planning is divided into cost and schedule orientations. The cost-orientated side is focused on expected direct and indirect costs to keep to the planned budget and avoid cost overruns. Schedule-oriented plans analyze critical path activities throughout a project to monitor adherence to the projected completion time so businesses can mitigate slips in the timescale.
Benefits of Planning Analytics
- Reduced risk: Making data-based decisions reduces the risk of bad decisions. Major investments justified using data analysis have a smaller chance of negatively impacting your career as sound judgment was used before execution.
- Better outcomes: Planning analytics provides guidance that allows adjustments as projects and programs progress that result in better outcomes.
- Improved collaboration: Sharing a thoughtful plan and results with stakeholders builds consensus, communicates lessons learned, and discourages siloed thinking. Successes and failures lead to learning.
- Increased Efficiency: Good planning leads to less waste and greater profitability. Manufacturing and retail sectors benefit greatly from good planning for replenishment. As parts or stocks get depleted, ordering the right amount at the right time makes a significant impact on operational efficiency.
Actian and Planning Analytics
The Actian Data Platform is easy to deploy and use across multiple clouds. The Actian platform helps ML engineers and data scientists by connecting to operational data sources using predefined connectors, transforming data for ML models, and automating data pipelines. Developers can leverage user-defined functions to deploy ML models in the database engine.
Connectors to BI tools allow planning analytics to run and be visualized in corporate performance dashboards. The Actian warehouse uses a columnar analytic database to accelerate queries that access key performance indicators in real-time.
You can evaluate the capabilities of the Actian Data Platform by signing up for a free trial here.