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Using the Scenario Planning Feature in Silico to Compare the Impact of Decisions and External Factors on Processes
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Using the Scenario Planning Feature in Silico to Compare the Impact of Decisions and External Factors on Processes

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  1. This is it.

In this blog, we will explore Silico’s Scenarios feature, which allows you to quickly compare the impact of different decisions and external factors on essential outcomes in your process. This makes decision-making and scenario planning for businesses increasingly detailed in forecasting. 

For example, you can use the Scenarios feature to compare your company’s financial statements under a high and low inflation scenario. Perhaps you could use it to compare how backlogs in a process (such as an order-to-cash process) might change if you allocate more FTEs to that process. Alternatively, you could test out a combination of different decisions (i.e. changing variables over which your company has control) with different external factors (i.e. changing values of variables such as inflation, over which you don’t exert any real control in the real world). You can see an example of this analysis in this video.

If you prefer following along with our simulation model building in video format, head to our YouTube channel below to watch us implement the Scenarios feature in our model.

For this article, we return to our example of the soft drinks company. We finished the last blog with a simple model that showed how price changes affect demand using the Lookup Table:

Simulation model showing distributors buying a product in Silico Scenarios

As we concluded, there is likely to be a sweet-spot price to be found. If it is too high a price, we’ll get high revenue per unit but a low volume of unit sales. And if we charge too low a price, we’ll get a high volume of sales but low revenue per unit. The sweet spot price will be high enough to generate a decent revenue per unit, whilst maintaining a decent volume of unit sales too. We can use the Scenarios feature to help find out where that sweet spot price is. 

Before doing that, we need to develop our simulation model a bit further so that it represents how our soft drinks company generates revenue. 

Updating the Process Model

As a soft drinks manufacturer, our revenue isn’t generated from consumers buying our products in a retailer. Our revenue comes from distributors buying our products from us. Distributors then sell products with a markup to retailers, who then sell on to consumers. This can be represented in the simulation model:

Complex beverage process simulation model

Next, we can create a new variable called Revenue, which is calculated by multiplying the unit sales by the revenue per unit, i.e. our price to distributors. In the same way the retail price doesn’t directly lead to our revenue; the drinks sold at retailers per week also don’t directly affect our revenues. Instead, the units we sell to distributors impact our revenue directly. 

We could build this out as a supply chain simulation model, where we model how retailers’ and distributors' inventories get depleted and consequently make orders back along the supply chain to replenish their stocks accordingly. But for now, let's keep it simple and say that the drinks that distributors buy from us will be a function of the drinks that retailers sell to consumers, delayed by a particular time - i.e. the time it takes for the retailer to realise their stock is depleted, make an order with the distributor, the distributor delivers that order and depletes their own stock, which triggers them to make an order with us, the manufacturer. 

Let’s say the delay is three weeks. To prevent confusion, we can rename the Total drinks sold per week variable to ‘Drinks sold at retailers per week’. This highlights how Business Process modelling in Silico is often an iterative process: as we build our model, we constantly change and adapt it to new variables and dynamics. If we keep expanding our model, we can create an accurate Digital Twin of the Enterprise.

Here’s how our updated structure looks:

Creating a Digital Twin of the Enterprise with Silico scenarios

Creating a Scenario

Now that our model is set up to calculate Revenues, we can begin using the Scenarios feature to find the best price we can set for the distributor. This assumes that our goal is to achieve the highest Revenues.

All data already inserted into the model is stored as the Base Case scenario, which is by default set to Blue. You can see this in the bottom left corner of the screen. To create a new Scenario with different inputs (i.e. different values or formulas for certain variables), click the “+” button in the top right corner of that section. 

You’ll see “New Scenario“ appear just below the Base Case, with the writing in red (that’s the default colour of a second scenario, but it can be changed). When we see the name of this scenario highlighted in its colour, it means that this Scenario is now selected. Any changes to variables’ formulae will be saved as part of the selected Scenario. In a Scenario other than the Base Case, the name of that Scenario is visible in the top left corner of the screen. 

You’ll also notice that you won’t be able to add any variables while in any Scenario other than the Base Case. This has been set up because the Scenarios feature should be used to compare the same model run under different values/formulae. The ultimate structure of the simulation model (i.e. the variables in the model, and the connections between them) should, therefore, remain the same as you make these comparisons. 

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Finding Sweet Spot Pricing with Simulation

Our goal is to test out the impact of different prices on revenues. So for this first new scenario, let’s change the price that we charge our distributor from €1 per can (in the Base Case) to €1.1 (in this “New Scenario”). Make sure that this change is done when that Scenario is selected. 

And to remember what this Scenario represents, let’s rename it from “New Scenario” to “10% Price Increase”. When selecting the Base Case, you’ll notice that the changed variable value reverts back to its original value. Here’s how the whole process so far looks in Silico:

Price reduction simulation process model

You’ll notice that all variables in the model now have a second time series, shown as a red line. This allows us to compare how a change in a specific Scenario has impacted all other variables along the causal chain affected by that change in a variable. Note that if that change doesn’t impact some variables, then the red time series will be the same as the blue time series for such variables. For example, in this case, the number of stores that carry our drinks is not affected by the changes we’ve made in the “10% Price Increase” Scenario, so we won’t see any difference between the red line and blue line for those variables. 

As we can see, in this Scenario, our Revenues variable is actually reduced due to the lower volume sales and despite the higher per unit revenue. This is likely due to the dynamics captured by the Lookup Table.

So, let’s test out a lower price. We can create another new Scenario. The default colour this time will be green. We can once again change the ‘Our Price to Distributor’ variable, only this time we will set it to 0.9, to represent a reduction of 10% in price (which is what we will name our Scenario). 

As we can see by the green line for the Revenues variable, this price results in higher overall Revenues, due to the higher volume sales and despite the lower per unit revenue. That leads us to ask, what if we reduce the price even further? We can create another Scenario called “20% Price Reduction” where we set the price to our distributors as €0.80 per can. 

Here we see even higher revenues. So what if we go even lower again? Let’s test out a 30% Price Reduction Scenario:

As we can see by the orange line, with this Scenario, we see revenues decline slightly.  So, the sweet spot seems to be the price of €0.80 set to our distributor. This results in a retail price of €1.60, driving high volume sales without cutting our revenue per unit too much! 

As you can see, the visual aspect of the Scenarios feature allows us to easily compare the outcomes of different decisions/external factors on our business. However, when many Scenarios are shown simultaneously, the graphs can start to look a bit cluttered. We can easily toggle the visibility of these Scenarios on and off by clicking on the eye symbol beside each Scenario. This allows us to easily compare between a subset of Scenarios without showing too much clutter on the diagrams. Note that if you have a specific Scenario active, it will still appear on graphs even if its visibility is toggled off. 

Conclusion

That concludes the Scenarios blog; we hope that you now feel comfortable using the scenario planning feature in Silico. Scenarios are a hugely valuable tool for comparing how different decisions and external factors will impact different outcomes represented in the model.