What-if Analysis: 3 Applications in the S&OP Process

This compilation of three blog posts on what-if analysis is intended to provide a set of guiding principles which allow S&OP managers and their partners to 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. Let's dive in.

1. 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 consist of 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 generally is 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 action 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 are closer to optimal. Additionally, it should be able to consume unit cost and unit profitability forecasts produced in the S&OP process.

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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.

2. Product Mix and What-if Analysis

As stated earlier, the key underlying requirement for maximizing value capture is the support for multi-dimensional analysis on a forward-looking basis. This multi-dimensional analysis capability allows decision makers to examine a business plan from multiple angles. Given this, I’ll explain how multi-dimensional analyses can be applied to Product Mix, increasing decision options for senior managers.

Product mix what-ifs include evaluations of different product mix strategies including assortment and SKU portfolio optimization. Product mix analyses evaluate different go-to-market strategies to identify the optimal product mix at the customer, regional, or national levels, often extending the scenarios to new product introduction and SKU sun-setting decisions. Users try to create realistic plans that maximize the financial impact of their strategies while meeting other business objectives (e.g., customer service, being the category captain, etc.). A best practice analysis sequence would include:

1. Start by defining the what-if analysis. Assortment analyses might simply include evaluating different product mix strategies for different retail channels in different store types or regions, with the resulting volume and price expectations associated with any given combination. A more complex scenario might also include new product introductions – which require deeper forecasting especially at the manufacturing and supply chain levels (e.g., BOMs, process rates, cost of new ingredients, etc.) – and/or SKU sunsets at the national level that force adjustments to lower-level assortment scenarios. Scenarios are typically defined by editing a demand plan or through a scenario wizard that allows users to only enter the necessary variables.
2. Once the scenario is entered, users re-create the plan considering all the constraints in the system. Since the scenario involves [potentially] changing the product mix at the regional or national levels, it is essential to re-optimize the supply plan (to maximum profit or minimum cost) to properly account for supply chain and business constraints and for users to analyze and compare multiple scenarios under “the best possible outcome.” This not only allows a 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. Users should be able to see P&Ls by business unit, product, and major customer/store type and compare the impact of the scenario vs. the base plan. Also, users should be able to see the impact of discrete moves such as new product introductions or product rationalization.
4. Typically, there will be deeper questions as users will want to know why scenarios report a given outcome – remember that your system as a whole behaves in a non-linear way and this will often result in counter-intuitive findings. Root-cause analyses help determine the key drivers of performance.
   • For example, users may want to see why a certain product yields a lower profit than expected. To do this, they may need to dive into detailed (ABC-like) product profitability forecasts that show revenue and cost by region. A product may have a higher cost in one region due to higher input costs because it is using an older, less efficient line or maybe because it is using over-time labor. These costs may also vary across time.
   • Another example could result in higher or lower profitability by customer. Here the root-cause analysis may reveal different cost-to-serve a given customer if the logistics requirements are sufficiently different (for example under a rapid replenishment situation).
5. Under complex scenarios – and limited time, users may allow the system to select the optimal product mix (by customer/region/other) based on total profit impact. It is even possible to configure these scenarios in a way that some products are “must-haves” (in other words, the decisions to carry the products are forced) while others are open to choice by the system. This type of analysis is often very revealing and leads to the definition and analysis of multiple additional scenarios.
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) by product and perhaps even by product/by customer. Opportunity values provide unique insights to help users identify further opportunity while significantly reducing the workload.

Ideally, integrated business planners that evaluate product mix scenarios will also have access to a next-generation assortment planning & optimization capability. This type of solution takes into account cross-elasticities between related SKUs to find the optimal combination for a given customer, store type, and region. It is highly synergistic with S&OP (or Integrated Business Planning) as it will provide a better set of incoming scenarios while it can consume the output of S&OP in the form of volume constraints and unit costs.

Product mix analyses embedded as part of the S&OP process represent a significant opportunity to improve performance. By embedding the decisions as part of S&OP, product mix decisions can be made in context of a much more realistic revenue and profit impact, allowing S&OP and category managers to make necessary adjustments that maximize the performance of their product portfolios.

3. Supply Planning and What-if Analysis

Supply-side what-ifs include evaluation of different supply planning strategies, including manufacturing, inventory, procurement, and logistics. Users alter constraints (e.g., safety stock policy, manufacturing run lengths), introduce new possibilities (e.g., additional straight-time capacity, outsourcing) and manipulate objective functions to create realistic supply scenarios to identify supply options that maximize revenue, profit, working capital, and customer service performance. A best practice analysis sequence would include:

1. Start by understanding the base plan – which ideally includes an integrated supply and financial plan. The base plan should highlight the key constraints impacting the supply plan. It should go beyond the operational view to quantify the financial impact of these constraints, in the form of lost revenues and lost profits. Besides, it should calculate the opportunity value (the net system-wide marginal impact) of removing a unit or bottleneck (e.g., an hour, a shift, a policy constraint, etc.). This analysis will lead supply planners to zero-in on the key opportunities for improving the supply plan.
2. Define the what-if analysis. Scenarios are typically defined by editing min-max constraints or through a scenario wizard that allows users to only enter the necessary variables. Typical analyses are targeted beyond the frozen period (typically 3-6 months out, but this varies by industry and company), and they include strategy, policy, and planning level analyses.
• Strategic and policy analyses guide the planning and short-term operational decisions. These include manufacturing strategy (e.g., sourcing, MTS/MTO), inventory policy (e.g., safety stock, cycle stock), procurement decisions (e.g., single/multi-sourcing), and logistics policy (e.g., distribution, customer service). In addition, some companies will include capital expense allocation scenarios as part of their strategic S&OP analyses.
• At the planning level, users evaluate the impact of altering more tactical constraints. These include build-ahead, use of over-time, manufacturing run length, changes to the maintenance schedule, special deals on procurement, and many other levels that can impact the business.
3. Once the scenario is entered, users re-create the plan considering all the constraints in the system. Since the scenario involves changes to the business representation, it is essential to re-optimize the supply plan (to maximum profit or minimum cost) to get a realistic picture of the impact and to analyze and compare multiple scenarios under “the best possible outcome.” This allows a more realistic, apples to apples comparison as well as saving significant time vs. simulation-based strategies (as in those available in Business Intelligence/BI systems).
4. The re-optimized plan will establish the impact on operational and financial performance. Users should be able to see P&Ls by business unit, product, facility/asset type and function. In addition, users should be able to compare the impact of the scenario vs. the base plan at the same level of detail.
5. Typically, there will be deeper questions as users will want to know why scenarios report a given outcome – remember that your system as a whole behaves in a non-linear way and this will often result in counter-intuitive findings. Root-cause analyses help determine the key drivers of performance. Users may want to see why a certain strategy or policy change yields a different outcome than expected. To do this, they may need to dive into operational plan reports that tie with detailed cost forecasts (akin to Activity Based Costing but on a forward-looking basis). Accessing detailed reports that combine operational and financial information is critical to understanding the impact of a given what-if scenario.
6. A final evaluation of a scenario must include opportunity values (i.e., the net system-wide impact of selling an additional unit of product or adding a unit of capacity) by product and by asset. Opportunity values point the way towards additional upside potential by quantifying the value of removing the new constraints, thus helping users identify new what-if scenarios to evaluate.

Closing Remarks on the S&OP Process

As outlined in the three sections above, demand shaping, product mix, supply, and financial what-if analyses can provide significant upside to the S&OP process. The key requirement for capturing this upside is the ability to conduct these multi-dimensional what-if analyses on a forward-looking basis. Executives need the ability to evaluate the effect various strategies, policies, and tactics have on business plans. Technological advances have enabled companies to eliminate departmental/business unit planning silos to evaluate financial, operational, and service-level trade-offs better.

The S&OP process has evolved into true Integrated Business Planning with revenue goals and budgets that are validated against a bottom-up operating plan, and that operating plan is consistent with financial goals. Decision makers can make changes to tactical and strategic plans that optimize the balance between financial performance, customer service, and risk. Integrated Business Planning provides the optimal outlook for an enterprise.