Product Mix Analysis: Strengthening Sales and Operations Planning with What-if Analysis
This article is intended to provide guiding principles for Sales and Operations Planning S&OP managers and their partners to help them better understand which best practices are possible through what-if analyses.
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At River Logic, Our mission is to establish a new generation of planning and performance management, powered by prescriptive analytics and a next generation cloud-based user experience that excels at what-if scenario analysis.
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As stated earlier in the first post of the series, Strengthening the S&OP Process with What-if Analyses – Demand Shaping, the key underlying requirement for maximizing value capture is the support for multi-dimensional analysis on a forward-looking basis.
This multidimensional 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 Analysis and What-ifs
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:
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.
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.
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. In addition, users should be able to see the impact of discrete moves such as new product introductions or product rationalization.
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).
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 definition and analysis of multiple additional scenarios.
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 IBP) 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 the 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.
About the Author
As President, Carlos Centurion oversees design, development, marketing and delivery of all River Logic solutions. He works closely with prospects, customers, partners and industry thought-leaders to continually improve the value delivered to global organizations. Earlier in his tenure at River Logic, his thought leadership drove solution development, integrated financials into company planning and decision support processes.
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Enterprise Optimization™ requires a different paradigm. It needs a closer tie to corporate and strategy planning, deep participation from finance and stronger collaboration between the commercial and supply departments to optimize the use of demand generation and supply resources. That being said, Enterprise Optimization™ also requires a new generation of technology.
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