What-if analyses allows users to evaluate alternative strategies, policies, and tactics to maximize their revenue, profit, and working capital performance while delivering on service level commitments and properly considering risk and supply chain constraints. Over the past year, we’ve discussed this topic with current and potential customers and partners. We’ve found companies tend to be fairly clear in articulating their business needs. Surprisingly though, S&OP managers have more difficulty articulating the technology capabilities required to deliver on the business need.

This post is the first in a series intended to provide a set of guiding principles which allow S&OP managers and their partners to better understand best practices possible through what-if analyses. The key underlying requirement for maximizing value capture is the support for multi-dimensional analysis on a forward-looking basis. This cross-functional analysis capability allows managers and executives to examine the plan from multiple business angles (e.g., demand, product, supply, etc.), including upside and risk scenarios, to quantify the key trade-offs, and to reach consensus on the best combination of actions and policies that optimizes future performance. In the series of posts I’m providing, I will discuss a particular type of what-if analysis.

Demand Shaping and What-if Analysis

Demand shaping includes analyses of pricing, promotions, and specific customer deals. The objective is to identify the impact of different demand shaping strategies on the business, trying to maximize a combination of revenues, profit, and volume. A best practice analysis sequence would include:

1. Start by entering the what-if analysis in the system. Typical data would include the expected demand uplift, pricing, and any costs associated; example, executing a trade promotion campaign. A similar example would be a special deal for a customer, which typically includes a certain volume and pricing. This is typically done by editing a demand plan or through a scenario wizard that allows users to enter only the necessary variables.
2. Once the scenario is entered, users re-create the plan considering all the constraints in the system. By re-optimizing the supply plan (to maximum profit or minimum cost) in context of the potential additional demand, users can analyze and compare multiple scenarios under “the best possible outcome.” This not only allows more realistic, apples to apples comparison, but it also saves significant time vs. simulation-based strategies.
3. The re-optimized plan establishes the impact on overall financial performance, product, and campaign profitability. Users should be able to see P&Ls by business unit and product, as well as the impact of the campaign vs. a base plan.
4. Typically, there will be deeper questions as to why the impact is what it is. Root-cause analyses help determine the key drivers of performance. For example, capacity availability may limit the ability to fulfill additional demand or force the use of overtime or build-ahead, resulting in higher cost and lower campaign profitability. Additionally, users may find their policies (such as working capital or inventory) could potentially drive higher out of stock situations.
5. If the scenarios are complex, users may allow the system to select from multiple campaigns (full or partial) based on total profit impact; therefore, reducing workload and leading to the best answer quickly. Sometimes it is possible to fulfill a campaign up to the point where there are step changes in costs (e.g., overtime, outsourcing), thus maximizing profitability.
6. Finally, users should have access to opportunity values (i.e., the net system-wide impact of selling an additional unit of product or adding a unit of capacity). Opportunity values provide unique insights to help users identify further opportunity while significantly reducing the workload.

Ideally, integrated business planners that evaluate demand shaping scenarios will also have access to a trade promotion optimization solution. This will increase the planning synergies; first, by creating a set of campaigns that is closer to optimal. Additionally, it should be able to consume unit cost and unit profitability forecasts produced in the S&OP process (see blog post on embedding financials into S&OP).

Demand shaping analyses embedded as part of the S&OP process represent a very large upside, especially for CPG and electronics manufacturers that spend significant money on trade promotions focused largely on revenue and volume without knowing the true impact of these campaigns. By embedding the decisions as part of S&OP, campaign planners will be able to understand the likely revenue and profit impact and make necessary adjustments to maximize the ROI of their investments.