Credit: I’m working closely on this topic with Dominik Fischer. Together we are leading a new effort at SAP to showcase Extended Planning and Analysis best practices in pre-built Business Content.
This is the second part of this series intended to help introduce Enterprise Planning to those completely new to this topic. If you missed Part 1, well then you have not had to endure the suspense of how we are going to solve my teenage challenges on our road trip. Go check out my first post in this series and then come back here to see how the story ends.
For those who have already read Part 1, the wait is over 🙂 Continue reading below to learn what a planning model is and how we perform a basic scenario comparison in order to inform better decisions and achieve our desired outcome.
Please note that the intention of this series is to answer the question, “What is Enterprise Planning?” While Part 2 includes some screenshots from an Enterprise Planning solution, SAP Analytics Cloud for Planning, we will not go through a click-by-click tutorial here. If after reading this series, you are interested in answering “How” questions, I highly recommend the free OpenSAP course, Planning with SAP Analytics Cloud and its subsequent Advanced Topics course, both of which contain plenty of hands-on tutorials.
In Part 1, we set out on a 3-day-long road trip to the beach:
- We defined a central goal – Creating happy family memories.
- We identified key cost drivers – Fuel Cost, Lodging Cost, Food Cost
- We created a cost-optimized plan – Fuel Cost + Lodging Cost + Food Cost = $867
And we hit the road. While our actual expenditures remained within our budget, we unfortunately did not achieve our goal of creating happy family memories as it seems 3 days in the car was just too long for my teenage son.
Life, like business, is a non-stop journey of experimenting, failing, adjusting, succeeding, and starting the process all over again. It is this process that allows us to learn basic skills for fundamental survival all the way to the most advanced skills we pursue for our career or sport. What did we learn from our experience in Part 1? My teenage son was not fond of a 3-day-long road trip. Let’s explore some options to optimize the plan for more likely success achieving our central goal of creating happy family memories.
Option 1: The Life of Luxury
We all like to treat ourselves to luxurious vacations every now and then. In this scenario, we will explore a revised plan with increased expenditures for fancier hotels and better food.
Option 2: Misery Loves Company
Maybe we don’t want to spoil the family too much with extravagant accommodations and food. As an alternative, we will explore in this scenario the addition of one of my son’s friends coming along with us. Companionship often makes us forget about the things that bring us down and enjoy the moments we share with each other.
The Planning Model:
The most important step you can take to improve likelihood of achieving a goal you have planned, is to write down or record the plan. But what is it you will write down? What details will you include? The structure of your plan that instructs what details to include is exactly what we refer to with the term, “Planning Model”. In fact, defining such a model is the first step for any Enterprise Planning exercise. This model defines the measurable variables that we will plan and monitor along with the dimensions that describe the scenario being planned. Dimensions could be hierarchical in nature or just a flat attribute. Common hierarchical examples include time (for example, year -> quarter -> month) and location (for example, region -> country -> state/province -> city). Non-hierarchical examples could be attributes such as color, size, etc. Often times the dimensions will be maintained as “master data” and are immutable from the perspective of planners, though the management of such master data is beyond the scope of this article.
For our road trip, I have created a basic planning model with 3 dimensions and 3 measures. The 3 measures correspond to our cost drivers: fuel, lodging, and food. However rather than modeling the direct cost for each of these as measures, I have modeled the drivers that impact the cost: fuel quantity (gallons), lodging number of nights, and food quantity (number of meals).
The three dimensions are Version, Date, and Cost_Type. Version is included in every Planning exercise I pursue and provides the basis upon which I can compare different scenarios, collaborate on different perspectives with colleagues, and track progress of actuals to plan. Date is my standard time dimension. In our example, we want to plan our key costs for each of the 3 days that we will be driving to the beach. Thus I have narrowed the Date dimension down to just three days:
If you use SAP Analytics Cloud for Planning like I do, these two dimensions, Version and Time, are included by default in every new planning model.
Cost_Type is a bit more interesting. One benefit of this Cost_Type dimension is it allows us to define different types of restaurants, each with a different cost impact to our budget. In addition, we can use this dimension together with the measures, or cost drivers, in order to calculate our various costs for the trip.
At a minimum, the members you populate into a dimension (the master data) must have an identifier field. It is common practice to also include a description field that is more user-friendly. In my Cost_Type dimension, I have included a property called “Avg Unit Cost” to centrally define our cost assumptions established in Part 1 of this blog series. For example, you can see the average fuel cost modeled here as $3.40 per gallon. In addition to the unit costs, I have also maintained a hierarchy to help categorize these costs for presentation purposes later during our planning exercise. The parent-child hierarchy relationships are maintained in the “Cost Hierarchy” field, and we can easily visualize the results to validate we have defined the hierarchy properly.
With this model in place, we are now ready to dive into our planning activities.
Before we explore our two possible plan modifications, let us take a look at how our original plan (Part 1) comes together based on the planning model we have now defined.
In this view above, we have broken out a separate table for each of our cost drivers. We then entered the values for the drivers in each of the tables. This allows the tool to extrapolate the total cost for each driver based on the average unit costs maintained in our Cost Type dimension together with the quantities we entered (total number of gallons, total number of nights, and number of meals for each meal category) on the fly. Here we can easily see the Total Fuel Cost = $323, Total Lodging Cost = $250, and Total Food Cost = $294.
In an additional view, I have displayed a simplified summary consisting of the Cost Type hierarchy and Total Cost measure. This allows us to see subtotals within the various levels of the hierarchy (for example, total lunch costs) and roll-up the grand total of all costs combined which comes to $867 as detailed in Part 1.
Option 1 Plan:
Now that we have reviewed our baseline let’s begin adjusting our plan for our first scenario, “The Life of Luxury”. First, we need to extend our model to accommodate fancier lodging.
Within our Cost Type dimension, we create two specific lodging types, one for 4-star lodging and one for 5-star lodging. We move the $125 average cost per night from the original “lodging” row down to the 4-star entry, and we maintain a $250 average cost per night for 5-star lodging. We also maintain the hierarchy field for these two new lodging types to have them both roll-up within the lodging category in the hierarchy.
Next, we will plan our new lodging costs for the “Life of Luxury” based on the 5-star lodging option. In the Lodging Costs table, we ensure the 4-star entry is empty, and log our 2 nights of lodging on the 5-star row. We can see immediately the increased lodging cost of $500 for our trip.
Zooming out to review all three cost drivers, our fuel cost does not change. However, food costs increase quite a bit as we add breakfast buffet each morning, change lunch to “Full Service” lunch, and continue indulging with “Fine Dining” dinner as well. With these changes, the total food cost is now $787.50. If you are wondering why 3.5 meals are planned for each mealtime each day, recall from Part 1 that my teenager eats enough food for 1.5 people!
Option 2 Plan:
Perhaps you noticed in the screenshot in Option 1 above, there was a new widget to select the desired Version to review. Now you can see the power of the Version dimension which we quickly glossed over when setting up our planning model. In this example, we are using Version to capture a couple different options, or scenarios, for our plan. Changing that selector to “Misery Loves Company”, we now maintain and review the plan for Option 2 in which my teenage son brings a friend along on our trip. Once again, fuel cost does not change from our original baseline plan. Our friend will sleep in the same hotel room with us, so the lodging cost doesn’t change either. However, food cost will increase as I have another teenager to feed. Similar to my son, our friend is also quite voracious and thus we add another 1.5 meals to each of our food entries from our original plan. This results in a revised food cost of $420 for our “Misery Loves Company” scenario.
We have captured our plans for each of the cost drivers in the two different scenarios. As a final step, we want to aggregate the costs within each scenario and then compare these totals to each other. For this step, I have extended the summary table we used previously by adding the Version dimension to our columns. In addition, I created a quick bar chart to show the total cost associated with each of the 3 versions: Baseline (or “Plan” as depicted here), Life of Luxury, and Misery Loves Company.
We can easily see that “Misery Loves Company” is significantly cheaper than “Life of Luxury”. However, we learned our lesson from Part 1 about making decisions based purely on a cost factor without taking into account other factors or metrics. Thus, before making any final decisions, I will discuss the various options with my son and my wife prior to our next trip and make a decision together as a family about which scenario makes the most sense to achieve our goal of Creating happy family memories.
Lesson: Do not optimize too early. When you do optimize, make sure you are optimizing the most appropriate variable(s) for the goal(s) you are trying to achieve.
I hope this two-part blog series helped break through the confusion and intimidation in order to understand what Enterprise Planning is all about. As summarized in part 1:
Set a goal, make a plan, and then measure your progress regularly so that you can adjust your execution to achieve the desired result.
In this second part of the series, we performed scenario-based planning in order to compare a few options and use the results to inform a better decision. You may also recall a comment from Part 1 in which we acknowledged that fuel costs could be quite volatile. Scenario-based planning could also be applied here by extending the planning model to account for variable average unit costs, and then planning multiple scenarios on a range of costs. By considering various possibilities in advance, you are then better prepared for volatility and able to react with more agility during execution.
While the examples described in this series are basic, the same principles apply in the business landscape. For the best-run companies in the world, the Planning process never ends but rather is a cycle.
The process becomes quite interesting when we consider that each department in the enterprise has their own cycle they are driving in order to contribute to the overall enterprise-wide goal. Workforce Planning. Supply Chain Planning. Financial Planning. Etc. These departmental cycles are all interconnected, dependent upon one another, and susceptible to external disruptions. Watch for an upcoming article from my colleague, Dominik Fischer, about how we can facilitate and orchestrate these cycles with a holistic framework we call “Extended Planning and Analysis”.