Role of Analytics in Product Portfolio Optimisation
In this post, I’d like to demonstrate how portfolio managers can optimise their product portfolios in an educated fashion by incorporating the right tools and information.
Who hasn’t ever been amazed at the number of products some businesses offer and wondered “Why so many”? In this post, I’d like to demonstrate how portfolio managers can optimise their product portfolios in an educated fashion by incorporating the right tools and information. Expanding the product catalogue can be in itself a double-edged sword. Through my client engagements, I have often noticed that increased turnover via larger product portfolios does not always translate into increased margins but may lead to operational complexities, lack of core focus and profitability leakage. Few businesses have visibility into their true profitability and costs at a product, customer and transaction level, leaving portfolio managers effectively stabbing in the dark.
To achieve such visibility, quality data across business units must be collected over a time period and level of granularity that avoids the use of averages, eliminates randomness (e.g. warehouse relocation, change of provider, etc.) and at the same time captures variations in demand.
Customer transaction, logistics and administrative overhead data is necessary to allocate all business-related costs. By tying back all data sets at a transactional level, cost factors, patterns and problems can be identified. This can be achieved via a Cost-To-Serve analysis, an exigent exercise.
Profitability can be impacted by combination of causes. For example, material and finished good prices, customer ordering behaviours and logistics costs are examples that can further be linked to product design, operational inefficiencies, business policies and deficient contracts with providers.
The Pareto Principle, the rule that 80% of outputs are driven by 20% of inputs, can be applied to a range of areas in business. In terms of a product portfolio, this means that 20% of products account for 80% of profits. Consequently, businesses must dive into large detailed data sets to find margin increase opportunities for the remaining 80% of products.
By slicing and dicing data at a product, customer and transactional levels, portfolio managers have the tools and visibility to make educated decisions by considering the trade-off and balance between:
- Reducing costs through optimising operations to increase existing SKU profit margins;
- Eliminating unprofitable products with minimal profit margin;
- Investing in specific products to improve their margins;
- Managing marketing value to keep the portfolio relevant to customers.
If blindly followed, quantitative data without qualitative knowledge and expertise may not lead to the right answer. Portfolio managers would above all consider each product’s lifecycle and may see opportunities that are not reflected in the data. For example, a decision might be to retain unprofitable products to attract new customers or secure future sales.
Initiative leaders should also create dialogue with both internal and external stakeholders. While a strategic shift would require extensive communication, smaller-scale initiatives must be supported by targeted communication to stakeholders that are the most impacted. Collaboration with other business units, especially Logistics and Sales, is necessary to fully evaluate the value and feasibility of each improvement initiative.
Overall, effective product portfolio optimisation fosters focus on value adding initiatives and profitability, not just revenue. An optimised product portfolio, managed in an informed manner with the support of analytical tools and smart collaborators will lead to margin increase, better utilised working capital and higher customer satisfaction. Less can indeed lead to more.
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