In the first post of the series, Strengthening the S&OP Process with What-if Analyses – Demand Shaping, I discussed that 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.
Within the second post of the series, Strengthening the S&OP Process with What-if Analyses – Product Mix, I discussed how embedding Product Mix analyses as part of the S&OP process represents a significant opportunity to improve performance.
In S&OP Process with What-if Analyses – Supply Planning, the third post of the series, I discussed ways to enhance strategy planning that will allow decision-makers to identify, run, and evaluate integrated analyses that tie into financial outcome.
In each post, I’ve discussed a particular type of what-if analysis. I’ll do that here, too, in this final post of the series and narrow my focus on financials and risk management.
What-if Analysis for Financial and Risk Management
Financial and risk management includes analyses of input costs such as raw material, fuel, and outsource labor; demand; unexpected supply chain interruptions; interest rates; currency; and other factors that might affect performance. The objective is to identify the impact of potential unplanned events and use these to test the resiliency of current strategies and plans under different situations. Potential outcomes include refinement of current strategies and development of contingency plans should some events occur. In addition, leading indicators can be used to flag potential situations sooner and alert the organization to increase response readiness.
A best practice analysis sequence would include:
- Start by evaluating the Base Integrated Supply & Financial Plan, including the assumptions that went into creating the plan. Evaluate trends for key assumptions (e.g., raw material input costs per pound, fuel cost, machine availability, demand, inventory holding cost) and compare them against history. Prepare a list of the top variables that might create the highest impact and/or present the most risk. Remember it is a requirement that financials be embedded at a granular level as this will not be possible in a typical supply planning system where you enter unit cost.
- Use the system to run sensitivity analyses. The system should allow you to automate this analysis, for example by configuring plus/minus percentage points vs. the Base Plan through scenario wizards or easy to access datasets. If you have a lot of variables, it may take a few minutes to configure all the sensitivity analyses.
- Once the analysis series is entered, the system will re-create the plan multiple times, each time considering all the constraints in the system. By re-optimizing the supply plan (to maximum profit or minimum cost) in context of the potential changes in cost, demand, or supply chain capacity, 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 (for example, if you configured sensitivity analyses for three variables each with plus/minus 20%, you have six “re-optimized” scenarios to evaluate… if you used simulation you would have to make many assumptions on what to do about stranded volumes, how to reallocate lower demand, where to buy product, how much inventory to build, etc., resulting in tens of thousands of runs vs. six)
- Each run presents a re-optimized plan that establishes the impact on overall financial performance, product profitability and operational plans. Users should be able to compare the impact of each run side by side, easily identifying the variable that changed and its financial and operational impact. If the analyses has been configured properly (and sometimes the unexpected requires luck), users should be able to see very quickly where the biggest risks lie.
- Sometimes though, 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, the system may choose to move production of a product to another facility to take advantage of raw material or transportation cost differentials. These cases can form the basis for contingency planning.
- At this point, users from different functions get together and review the impact of the financial and risk sensitivity analyses. Often times there is no need for additional evaluation, but in some situations the users will want to evaluate refinements to the current plan or go deeper into drawing contingency plans. For example, a typical evaluation might include querying the system to evaluate whether it would store additional inventory if the working capital policy were relaxed, understanding that inventory can be an effective buffer against unplanned supply chain downtime or even raw material cost increases. Of course, there is always the “upside” risk where sales runs a trade promotion campaign that is wildly successful from a revenue perspective but puts the organization in a bind to supply the additional demand and in the process incurs additional cost. These and many more “what-if” analyses should be available for review.
- Once they are done, users can flag leading and lagging indicators to identify when deviations from plan are meaningful enough. These can come from the sensitivity analyses but also from Opportunity Values™, which alert users as to the marginal profitability of products and capacity time. Contingency plans can be stored in the system attached to the indicators in case the organization needs to react.
Ideally, integrated business planners that evaluate financial and risk scenarios will be able to integrate their system with budgeting and FP&A. This will increase the planning synergies; first, by providing profitability numbers that are significantly more accurate; second, by highlighting the top risk drivers; finally, by directly communicating Gross/net revenues, COGS and logistics budget P&Ls under different scenarios, thus reducing inefficiencies of translating from one system to the next (see blog post on embedding financials into S&OP). Most importantly, the organization will know when and how to react to unplanned events, leading to stronger performance and organizational agility.