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The Constraint ~ Bridging Operations Management, Corporate Strategy, and Financial Plannin  Revenue forecasting. It’s the hinge of any budgeting process. All budgeting for direct and supporting activities is then based on these projected revenues. But how often are the numbers, representative of activities and supporting assets, considered as more than just the numbers. Revenue forecasts and budgets, in general, carry the assumption that there exists the operational capacity to deliver those revenues because the forecasts too often are just seen as numbers.
Even when we relate revenues to COGS and OpEx through some method of regression or simply ratios we still miss the fact that a good portion of those costs do not represent the actual productive capability of a company’s processes; company specific resources and capabilities. For many finance professionals and strategy consultants, the closest they get to considering operational activity and capacity is during discussions on capital investment and the development of capital budgeting models. The actual operations and discussions on cycle time, throughput, and costs per unit time are left to operations managers and production supervisors, thus leaving a disconnect between the ‘theoretical’ (on paper numbers) and the ‘applied’ (real world activity).
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 Some Top Issues Facing Closes Over-Arching:: Manual Processes and Data
First: Integrable Capabilities of Systems and Applications
--Manual Processes and Errors Not every company has fully integrated ERP systems such as SAP, Oracle Fusion, Oracle NetSuite, MS Dynamics 365, Acumatica or some equivalent. Some of these systems offer a full suite of integrable packages and modules covering all areas of business operations, while other smaller ERPs offer modules for a few functional areas and are expected to integrate with other applications. An example of a full suite, Oracle NetSuite, has it’s core finance and accounting suite, NetSuite Financial Management, but it also has modules for CRM, Inventory & Order Management, Supply Chain Management, Commerce & Ecommerce, HR & Project Management. Of course, all the big ones have a similar setup. Not playing favorites, all systems have good points and bad points. It is quite common to see businesses attempt an amalgamated setup of operational applications, often with a partial ERP package and several other applications. A recent setup I saw had an ERP system that acted as the primary Accounting, CRM and POS tool and they used separate applications for SCM, Logistics, Fixed Assets, Payroll, HR, Tax and so on. This wouldn’t have to be an issue, but the reason I said ‘attempt an amalgamated’ setup, is that the integrable capabilities between applications is one key to smoother financial closes that are often not utilized.  In my opinion, within FP&A, the understanding of operations management and analysis is far undervalued and mostly nonexistent. CAPEX and capacity planning requires an understanding of resource scarcity and constraints on operations systems. Utilizing linear programming is a great way to get an understanding of how these constraints impact costs and investment. While I used Solver here, due its easy availability, it is a very basic computational application and not suitable for many more granular operational applications. That said, from a higher-level planning view for budgeting and CAPEX it is suitable for test runs and analysis. Hopefully, you will find that this also furthers communication between finance and operations for better analytics.
 Utilizing BI tools, such as Power BI, can add several levels to a company’s internal reporting and data insights. Interactive and dynamic dashboards can be composed relative to organizational departments such as supply chain, finance and accounting, operations, and marketing; but it can also be tailored to the level of the audience, from the managers “in-the-weeds” to the executives. However, in order to get the most out of the visualizations and analysis for the intended user, dynamics, scalability, minimal maintenance and integrity in calculations, a good deal of planning should be implemented to optimize your time in actual development.
This article will flow through some thoughts on the layout and planning of a dashboard, inclusive of dynamics and functional components such as a Drill Through on four-levels of products, a Bookmark, and several Measure calculations (afterall, if I was going to piece this together I also wanted to test some ideas). If you don’t use Power BI, that is okay, many of the planning and data organizational steps will apply to any BI or model development.  Statistical Methods : This is a quick example of an ARIMA model on a QoQ Revenues dataset that I chose because it is particularly ugly. While QoQ revenues is not typically the period over which forecasting would be done for revenues from operations, it was a readily available set. Statistical ARIMA forecasting is particularly useful when there aren't easily available and associated variables to use as predictors like you might use in multivariate regression models.
We are going to look at time-series data for forecasting. Time-series simply refers to data that has been collected at regular intervals over a time period. Because it is collected regularly, this data has both its value and the time it was collected as characteristics. Examples are closing stock prices (end of day - daily), monthly revenue (monthly), and hourly electricity usage (hourly). Using time-series data requires an understanding of Stationarity. The most important thing to understand about forecasts is that they rely on historical data and the composition of that data. This fact is an influential assumption to any forecast since you are utilizing the historical relationships that produced your time-series values and then pushing these same relationships into the future to produce forecasted values. You can certainly adjust these relationships, but then you may have to decompose your values by identifying those relationships before producing forecasts … not always an easy task. This is why nearly all forecasts begin with the assumption that any influence that creates past values will continue in the same fashion into the future.  Let's just get right to the point. The vast majority of your "analysts" are not anything more than process maintainers. This is the reason you can't get any actual models constructed from scratch or are stuck with models nicknamed "the beast" comprising sheets and sheets of formulas that are no longer used, but no one understands how to clean it out. This is also the reason all your budgets come from Three Statement financials and all your variance "analysis" is little more than the difference between budget and actuals. As we move forward this will only get worse given the usage of AI as the crutch for lack of aptitude, knowledge and general ability. As I predicted, there will be a reliance on professional certifications and proctored exams proving the passage and competence of knowledge for hiring in the very very near future. Data Analytics
Analysis is the ability to reason and make conclusions from events and data. This analysis can be descriptive, the most basic of data analysis which is the best most companies can get out of their "analysts". Descriptive analysis is simply telling you what a set of data indicates about a given topic. In the case of a business, the data is most often financial. You may get a set of P&Ls or Balance Sheets, from which the basics are told to you of rising or tightening margins, increased expenses, or changing asset and liability positions. You have sat through these meetings covering financial accounting ratios going up or down. Well ... You could read this yourself. If you know a ratio is supposed to go up to be considered positive and its not (or vice versa) then something needs to be done.
 Company: Machine-tool manufacturer of highly specialized, custom lasers machines and coating machines. Executive Summary
 Executive Summary
 Before I get into the example I would like to comment. Companies often have issue with field services and planning due to the mobility aspect, but if there is a segmentation, the planning becomes clear. Think of each vehicle as a brick and mortar location … that moves.
First, set up the planning as if it was a location. There are the lease payments, equipment investment, inventory investment (parts), maintenance, and all the admin that would accompany an actual store such as general liability insurance and licensing and permitting. You will eventually forecast revenues and have your AR and AP that is directly attributable to the field service, but don't think of it as mobile yet. Think of it more like a service station that has a certain number of available hours for the day for appointments, only these appointments will have more idle time between them due to the drive time. If you are working out a plan for field services for the first time, or considering changes, you should probably work with different sizes of vehicles. The reason is that you must understand the investment in the equipment and inventory load out that you would like to have given a certain size of vehicle. As you begin with a large, ideal vehicle, you can then choose another smaller, more economical vehicle and perform the same the loadout. Of course, the smaller vehicle will force a selection of equipment and inventory that you believe is absolutely essential given less capacity for the equipment. This planning will also have you questioning what types of service will be most common since this will determine equipment and inventory carry and have you working out how other services could be completed if they are beyond the current equipment and inventory. These are all good questions because they get you to consider data collection and future analytics.  This field services case study is that of a transportation NPV (net present value) model for bus purchases. There are initially two options, that of a purchasing of six, 32-Passenger buses or four, 52-Passenger buses, but I have included the use of solver to maximize the NPV if we were to select a mix of the two types using the same budget constraints. I always try to include additional analytics into the options, but I will walk through the analysis and the details. The buses will be used on a full day schedule of 480 miles, a 16 hour day, of which is included a 4 hour rush hour segment that the buses will operate at full capacity. Below are the specifications collected for each of the buses.  The available information suggests that the budget is at least $720,000 given that we are able to purchase six 32-Passenger buses at $120,000 each. This will provide the budget constraint for the maximization of NPV when running the solver for mixed purchases.
We will use a straight line depreciation over 8 years with the respective salvage values just for modeling. The wages per hour are assumed to be the same for a full-time driver and a part-time driver and the operational expenses are per bus per annum. |