This is a guest post from our partner IBM. In this post, Michael McGeein, program director and product management leader – IBM Planning Analytics, showcases how organizations use IBM Planning Analytics for Extended Planning and Analysis (xP&A).
Extended Planning and Analysis (xP&A), is not a new concept for IBM clients who use IBM Planning Analytics with Watson, formerly known as Cognos TM1. For the past several years, clients have embraced the need to tie operational decisions to the financial impact from both planning and analysis perspectives. For instance, a Director of Operations may want to increase production for the upcoming selling season, but they must first understand the impact on the business overall.
There are many operational considerations, from labor, staffing and production capacity — such as machinery and warehousing — to ensuring the business has the capital needed. All these factors need to be considered, and fortunately, IBM Planning Analytics with Watson has helped clients do this for years.
Financial and supply planning for a national blood service organization
A national blood service, and long-time Planning Analytics client, has started implementing a financial planning solution to better plan, forecast and analyze the cash flow needs and improve reporting to the leadership team and Board of Directors. Once the team fully understood the capabilities of Planning Analytics, they saw an opportunity to improve salary planning, a key part of the financial planning process.
From that, the HR team expanded the salary plan to include the components of staff planning, including hiring and attrition.
Another way the team used Planning Analytics was to plan for the supplies needed for the collection of blood from donors. They created a planning application that schedules nurses and technicians who collect specimens and accounts for the supplies needed, from orange juice, bottled water, and cookies to medical supplies like tourniquets, blood bags, type testing kits and more.
As this company can attest, extending beyond the core finance function to plan for people, activities, and other areas has been part of Planning Analytics for years.
Financial and HR planning for a television production company
Another great example of Planning Analytics in action is with a television production company that, like many clients, was initially focused on financial planning. After the team had their financial planning and forecasting running well, they turned their focus on how to better run their business. As a ‘job shop,’ where each TV program is a job, one area of focus was cost planning by job. The team created a job planning application, starting with staff planning as one of the largest cost components. Then they extended to include overhead and expense allocations, and eventually created a weekly Show Cost planning module to understand the contribution of each show to the overall production company’s results.
Supply chain planning for a global contract specialty manufacturer
A global contract specialty manufacturer, with deep expertise in manufacturing know-how, supply chain insights, and product design, uses Planning Analytics for nearly every ‘non supply chain’ use case in their organization. From financial analysis and reporting, forecasting, reserves reporting, aged accounts receivables, and treasury cash balance and forecasting to working capital, HQ allocations, local tax adjustments, and income tax in interim periods, all of these Planning Analytics solutions are integrated to ensure changes in one area, like cash forecasting, can be reflected in the overall working capital analysis.
No matter the industry, Planning Analytics is a continuous, integrated business planning solution that helps run some of the best companies in the world. Those who use IBM Planning Analytics with Watson understand the benefits of integrated planning that are not realized when doing ‘connected’ planning in spreadsheets or other traditional tools.
Are you interested in expanding your use of IBM Planning Analytics? Let us know – we can help!
This blog post was originally published on the IBM Journey to AI blog.
