As businesses increasingly look to optimize their overall operations, CPAs and accountants in general should consider tackling supply chain management with their clients.
One of the top ways to address tax issues within a supply chain and increase overall efficiency is through Quantitative Supply Chain Optimization (QSCO). To learn more about what QSCO entails and how it can help your clients, read part one of this article. In part two, we’ll discuss how your clients should go about implementing QSCO to unlock greater efficiency and cost savings within their supply chain.
The Four Phases of Implementing QSCO
The implementation process for QSCO is heavily inspired by software engineering R&D and data science best practices. The methodology is highly iterative, with low emphasis given to prior specification, and a high emphasis on agility and the capacity to recover from unexpected issues and/or unexpected results.
Following the proper QSCO implementation process will allow your clients to better understand the current state of their supply chain, as well allow them to effectively evaluate multiple “what-if” scenarios for changing their supply chain process. In addition, with clear documentation of their current supply chain, you will be better positioned to offer advice on how to optimize tax considerations for various components.
The end goal of proper QSCO implementation is to establish key metrics, from which plans and goals for improvement can be made and executed. The following are the 4 key phases of implementing QSCO:
Phase One: Scoping. This defines which supply chain decisions are intended to be covered by the initiative. This phase is also used to better understand the expected complexity involved in the decision-making process and the relevant data. Historical data on order volume, margins, year-over-year growth and other variables should be compiled and added as input data to be analyzed.
Business owners and key stakeholders should begin by outlining key constraints, including minimum order quantities, full containers, maximum warehouse capacity, max pace of order growth and current shipping vendors. Next, economic drivers should be considered. These include carrying costs, cost of stock-outs, gross-margin and tax considerations, which can be provided by an accountant or finance professional.
Supply chain scoping also includes outlining the various supply chain components and identifying who manages each component. Once your client’s data needs are fully documented, you should also conduct your technology vendor evaluation and selection efforts at the end of this phase.
Phase Two: Data Preparation. This consists of establishing a software solution and process that transfers all relevant data from the company’s current systems to an improved, analytics-focused platform. This phase includes preparing data for quantitative analysis by cleaning up the data and formatting data to be uniform, including metrics, formatting or terminology.
Gaining access to data and making sense of data is nearly always an underestimated challenge, but challenges and time commitment should be expected at this phase. Historically, standard operational systems such as enterprise resource planning, manufacturing resource planning, warehouse manufacturing and customer relationship management were not designed to work together, and are often implemented at different time periods, frequently leveraging different metrics such as pounds versus ounces or zip codes versus state locations.
Upon testing the data in the next phase, business owners should expect several iterations of this phase. From returning to improve data inputs or data formatting, or discovering that data quality is poor, many businesses go between the data preparation and pilot phase several times before they are able to fully implement QSCO.
However, once automated, QSCO is well worth the effort. Because the data will be used to make important company decisions on efficiency or scale, key stakeholders will realize there is ROI on quality data collection and accurate data.
Phase Three: Pilot. This phase consists of implementing an initial decision-making logic that generates decisions. An example of this would be generating a recommendation for suggested purchase quantities based on data inputs and data analysis.
Test recommendations should be evaluated against previous forecasts and processes. Data inputs, analysis and outputs (recommendations or forecasts) should be fully automated. As mentioned above, companies will likely experience numerous iterations of this process as errors are found and data is improved.
Traditional supply chain systems require a lot of manual corrections to even operate, including new products, promotions, stock-outs and more. QSCO establishes new rules for supply chain management in that there are no manual entries and all factors should be built into the algorithm’s logic.
The supply chain coordinator will gather all factors, workflows and specificities that should be integrated into the decision-making logic. Then, the supply chain scientist implements the first batch of KPIs associated with the decisions.
Those KPIs are introduced in order to avoid black-box effects that tend to come about when advanced numerical methods are being used. It is important to note that the KPIs are devised together with the supply chain leader who will ensure that the measurements are aligned with the company's strategy.
Phase Four: Production. This phase brings the initiative to cruising speed. Performance is monitored and maintained and consensus is achieved on the desirable degree of refinement for the supply chain models.
The decisions generated by the logic are being actively used and their associated results are closely monitored. It typically takes a few weeks to a few months to assess the impact of any given supply chain decision due to the required lead times.
The initiative's change of pace in the production phase should be approached accordingly to ensure reliable assessments of the performance of the automated decisions. Tax professionals should work closely with the business’ leaders to find the right balance of possible refinements to the optimization process and the corresponding complexity or tax implications of those refinements.
The supply chain coordinator will be able to focus on the strategic insights proposed by the new supply chain management process. With a slower pace of change, it becomes possible to incrementally revise existing processes, in order to immediately unlock performance improvements, providing incremental ROI on QSCO early on.
The supply chain scientist will continuously fine-tune the QSCO system by putting an ever- increasing emphasis on the KPIs and data quality. This role is also responsible for revising the logic since subtle flaws or subtle limitations, typically relating to infrequent situations, become uncovered over time.
Then, as the processes change, the decision-making logic is revised, too, in order to remain fully aligned with the workflows and the strategy. The supply chain scientist will also ensure that the decision-making logic of the QSCO system remains up-to-date with the evolving advancements of general IT, business and technology capabilities.
Note: A special thanks to Lokad for enabling Avalara to repurpose parts of its original article on supply chain optimization.