IBM Planning Analytics, or TM1 as it used to be known, has always been a powerful upgrade from spreadsheets for all kinds of planning and reporting use cases, including financial planning and analysis (FP&A), sales & operations planning (S&OP), and many aspects of supply chain planning (SCP). As far back as the 1990s and early 2000s there were companies, like the one discussed in this podcast episode, that took advantage of TM1’s power to support full integration of their financial and supply chain planning processes.

Build planning models to improve supply chain management

The challenge faced by every company is matching supply with demand. In a perfect world you would know precisely how much of your product the market desires, and you would be able to produce and ship exactly that amount to every location where your customers would be waiting, ready to buy.

In lieu of a perfect world, what do you do? You plan. Plans help you explore the consequences of your decisions in advance so you can understand your hedging options: Do I build up inventory here? Do I need to find new suppliers there? Do I have enough cash to fund these investments while also covering day-to-day operations?

You also build planning models to capture relationships and constraints so that you can change your driver assumptions and immediately see the impact on resources and capacity over time. Having the ability to build and use models in this way is fundamental to managing supply chain and financial risk through activities like “what-if scenario planning”, as explained in this blog post. Time matters too: your models must be quick to run, so analysis can be done before the assumptions are out-of-date. As such, planning becomes a continuous rolling activity as the lines between “plan”, “budget” and “forecast” are blurred.

Since there are clear cross-functional business correlations between demand and sales, supply costs and Cost of Goods Sold, it’s not hard to argue for supply chain and financial planning models to be integrated across the Extended Planning and Analysis (xP&A) cycle. However, the reality of this is complicated by several factors including:

• Differing time horizons and cadences: Days/Weeks vs Months/Quarters
• Differing levels of detail: SKUs/ Products vs Product Groups/ Lines of Business
• The need to collaborate, share data and agree on definitions across organizational boundaries and systems

Choosing the right technology to support xP&A for your strategic goals

A growing number of forward-looking companies are successfully navigating these complexities using IBM Planning Analytics, a technology capable of supporting secure collaboration, fast automated data acquisition, driver-based and AI-powered predictive modeling, and, unique in the market, the handling of large amounts of detail at scale without sacrificing performance.

With the right technology-foundation in place, it becomes easier to tackle the business alignment questions, starting with designing an end-to-end integrated business planning process that will lead efficiently to a consensus forecast (or plan).

The first step is always the unconstrained demand plan.

Even when supply constraints seem overwhelming, it’s still important to have this view, so you can take action to overcome the constraints in the future. Depending on the patterns of your business, predictive models can play a significant role in improving the accuracy of your demand plan, while also saving time through automation, as experienced by Arthrex, a global medical device company.

The next step is to start layering on constraints.

In a manufacturing, distribution or retail context, this is the supply plan. The supply plan is typically anchored in capacity and can combine manufacturing capacity, supply capacity and labor capacity.

Then, everything comes together.

With everything in the IBM Planning Analytics dashboard, it’s now possible to see where and when capacity shortfalls (or excesses) are imminent and explore options for mitigating situations in accordance with strategic goals.

IBM Planning Analytics can help your teams modify assumptions such as production capacity and labor allocation across a variety of scenarios in real-time, and immediately see the impact on all related metrics including constrained demand, inventory, sales, costs, and cash. QueBIT’s webinar includes a demonstration with IBM Planning Analytics of the interplay between all these components, beginning with the demand plan and ending with the impact on financial statements. You can also find a more nuanced explanation of the relationship between supply chain decisions and financial KPIs here.

I also encourage you to join the IBM Business Analytics live stream event on October 25^th, to hear more case studies on how businesses have used Planning Analytics to accelerate data-driven business decision making.