As shifts in procurement and sourcing costs result in increased supply chain expenditure, CPG leaders world over have had to re-evaluate their long-held unit-based pricing strategy. Now, with 47% of consumers switching to click-and-collect services to buy consumer packaged goods, and brand loyalty taking a hit due to frequent supply chain disruptions, supply chain cost implications are set to evolve even further.
After being focused on achieving efficiency for the longest time, supply chains will now incentivize towards building resiliency, and this means adopting a fresh approach to account for supply chain spending, integrating automation and intelligent capabilities in the following key areas of opportunity:
Traditionally, CPG businesses have undertaken the unit price approach to reduce incremental costs in their supply chain. While unit pricing is a more defined approach, using the unit cost as the baseline for all costs, it overlooks several indirect costs related to the supply chain, such as import duties, freight charges, warehousing expenses, and plant downtimes. Furthermore, decisions that have a direct impact on these costs, including supplier evaluation and baseline cost negotiations, often go unnoticed under this approach.
Although unit pricing does allow for easier identification of cost-saving opportunities, incremental cost cuts can no longer meet the demands of today's business environments. With an imperative to rethink their go-to-market strategy owing to continued at-home consumption, a sharper focus on sustainability, and evolving digital advancement, CPG organisations must make significant changes to their supply chain cost base to stay competitive and accelerate growth.
In March 2021, producer prices increased by 4.2% over the previous year, the most since 2011. Under a unit price approach, such an increase would cause decision-makers to make blanket cuts, setting unrealistic cost-saving targets. According to Gartner, 9 out of 10 businesses made blanket cuts to their budgets in 2020, with only 43% attaining the savings goals they set against these cuts. Here, applying a total cost view can allow for greater visibility into supply chain costs and make end-to-end cost optimization possible.
A total cost approach accounts for not only the purchase price of an asset but also the cost of operation. By assessing the total cost of ownership, insights about a product’s intrinsic value and its value over time become clear. In a supply chain context, this assessment can help quantify the cost-to-serve, or the value that the supply chain contributes to consumers and the brand’s product portfolio.
Putting this approach into practice, however, depends on a business’ level of supply chain complexity, spending category, and buyer-supplier engagement. Considering how extensive the purview of costs it encompasses is, the way to flexibly apply this approach at scale is through optimum utilization of supply chain data linked to sourcing, labour, packaging, equipment, inventory, and even downtime. A total cost approach paired with intelligent capabilities, such as predictive maintenance, route optimization, and machine vision, can collect and analyse data from multiple sources and uncover patterns between varied data to provide accurate cost-performance insights. To achieve this, AI-enabled real-time cost-surveillance engines can enable CPG organizations to better understand, optimize, and control expenses.
Optimum data utilization results in enhanced stakeholder performance across the value chain. Today, CPG manufacturers can map insights on procure-to-pay and source-to-settle cycle times, retailer projections, demand patterns, pricing strategies, and product alternatives among others, to determine the causal variables that underlie demand variations for their brands and goods across all retailing locations.
Here are the ways in which AI can help CPG organizations achieve strategic cost optimization:
Leveraging data mining and ML algorithms can help organisations benchmark and discover cost optimization opportunities. By examining correlation between risks, profits, expenses, and viability of various cost control initiatives, and evaluating the impact of cost plans across key areas, AI can help generate a cost baseline consistent with different cost structures, comparatives, and future steps on a priority-first basis. Further, it assists decision-makers in comparing their organization’s spend to competitors, measuring the efficiency of business-critical processes, and putting improvement plans in place. This enables the development of a sustainable, enterprise-level cost optimization blueprint to help CPG businesses mitigate economic disruptions, commuting costs while supporting growth and innovation.
Back-office operations such as data entry, purchase order issuing, and customer service constitute an integral part of the supply chain. Having a cost-focused analytics tool can allow CPG leaders to take an informed approach on internal budgeting decisions related to resources, processes, and innovation. AI-driven tools help analyse a variety of data related to personnel, acquisition, and operating costs, unlocking cost-saving whitespaces, and enabling a shared framework for cross-functional cost management. This allows organizations to develop an effective cost optimization communication strategy with different internal stakeholders and recalibrate their cost-to-function, ultimately enhancing resource utilization, phasing out excessive spending, and identifying areas for technology application to automate routine tasks.
AI-driven analytics can help make sense of cost data unique to potential suppliers, such as warehousing, transportation, and packaging to accurately identify business-specific needs, evaluate and suggest potential stakeholders, and streamline external budgets. For CPG organizations, this helps in creating purpose-aligned contracts, reducing buying cycle risks and complexity, and minimizing costs, to ensure low-risk business continuity.
Cost of Innovation Defines Cost of Ownership
The total cost view introduces a new outlook to supply chain cost management, focusing on interoperability across all touchpoints. But to successfully apply this approach, CPG organizations will also need to build a strategic technology portfolio. This would include evaluating potential supply chain innovations, assessing their own automation needs, and identifying technologies that best align with their cost goals, helping extract predictive insights from data to drive decision-making.
This will not only enable them to optimize cost of ownership, but also allow for a better understanding of their spending portfolio. By reworking their cost baseline and fact base aligned with their future cost goals, CPG businesses can acquire capabilities to unlock the next level of cost performance, delivering superior value at an optimized expenditure level. With strategic implementation of analytics and automation, organizations can make their processes time- and cost-efficient, identifying cost-saving opportunities at various levels, and reinvesting savings into processes that generate significantly higher value in the supply chain.