Industry Stories

Product Cost Optimization: Balancing Legacy and Innovation in Manufacturing

A decade of unparalleled technological innovation, sustained economic growth, and unprecedented globalization has financially pressured manufacturing and engineering firms from every direction. They must find the right balance in their product portfolios, navigating a careful strategy of investing in legacy products and introducing new ones. Companies worry about leaving money on the table in the next quarter, while still trying to prepare for a potentially tectonic business shift in a year or two.

To protect their bottom lines, companies often find that it is just as important to increase the margins of legacy products as it is to generate revenue growth through innovation, new products, and accelerated time to market. The only effective way to shore up this foundation is through stringent controls while still retaining market share. However, identifying these savings without choking innovation is easier said than done. Businesses need better ways to measure their variables, particularly when their physical costs dictate profitability far more than associated services do.

The challenges of tradition

Manufacturers often find that traditional mindsets are the biggest barriers when trying to control recurring production costs. The product definition for functions and features is usually engineered without a sufficiently detailed analysis of the cost of materials, shapes, and processes. The industrial planning team typically provides a feasibility assessment and a general direction for production. The procurement team then takes over and often struggles to manage availability and to meet schedules. Inevitably, cost becomes a secondary objective.

In some sectors, these traditional processes exacerbate the problem. Aerospace, automotive, high tech, discrete manufacturing, and similar industries often assume they must provide end users with as many options and upgrades as possible. Iterative design has become the norm. However, dynamically changing production volumes — even quarter to quarter — is commonplace in order to avoid inventory and logistical challenges.

Whether because of commodities, labor, or the cost of capital, markets today move at a frantic pace. But most manufacturers struggle to define and plan for costs with enough precision and definition to make suitable adjustments. As a result, costs fluctuate significantly enough to affect pricing margins conceived in preproduction stages.

There are two basic questions that companies need to ask when confronting these problems. How do we factor in costs at every stage as we build product portfolios and production systems? And how do we build an enterprise mindset to retain the competitive edge and realize the growth we must achieve?

Product cost optimization

Although tradition can be a hindrance, companies have tools available that allow them to look beyond past practices. A product cost optimization (PCO) strategy offers a glimpse of how organizations can navigate growing financial demands. PCO is a comprehensive set of services and solutions designed to estimate and analyze costs and then optimize accordingly. The underlying philosophy is to look at every aspect of the product development, production, and service life cycle from a cost perspective.

PCO solutions arrive at the “should cost” of an engineered product by analyzing cost trends against build attributes. This identifies optimization potential and lays out a road map to savings. It can be likened to how the driver, car, fuel, and crew of a Formula One race team all work together to reach the finish line.

Traditional cost solutions need help

Manufacturers have long understood the need to improve margins while growing their customer base. Cost estimation, value analysis, value engineering, and product benchmarking have sought to manage these issues. However, cost engineers and analysts have always been dependent on tribal knowledge and hand calculations, applied mostly to legacy two-dimensional engineering. Gaps in these efforts can now be filled by accelerating technology, including automation, data analytics frameworks, and more powerful computing infrastructure. Companies now have a greater ability to generate accurate cost estimates in near-real time and to make decisions based on accurate data.

Figure 1. Manufacturers struggle to accurately estimate, store, and repurpose costs

Source: Infosys

New life cycle design

In industries such as aerospace and discrete manufacturing, product design determines nearly 80% of the manufacturing cost. Form, fit, and functionality requirements drive variability over relatively small production volumes.

With these dynamics, it is important that engineering teams understand cost trade-offs in the research and development stage. The previous “design to cost” approach can now be aided by the rapid evaluation of multiple engineering alternatives from a cost perspective. All this can be done without needing to fabricate a prototype.

Even at the development stage, designers can calculate geometrical features, such as tolerances or the composition of alloys and the cost impacts of these choices. The changes then can be tracked across revisions. For legacy products, physical teardown and analysis for the potential value engineering of components can be replaced with estimated configurations compared to cost baselines. These can be simulated for function and performance and ultimately can reduce recurring costs.

Manufacturing and operations phase

Industrial planners, manufacturing leaders, and operations managers need to consider many moving parts for serial production. They determine how to allocate manufacturing capacity for legacy and new products, plan for demand fluctuation, and optimize throughput. All the while, they face pressure to be lean and to invest in the right capabilities for future needs.

To make better-informed decisions, manufacturers need to visualize cost alternatives, such as selecting processes based on volume, equipment availability, and tooling and consumable needs. For example, when deciding between a 3-axis mill and 5-axis mill, a company would need to consider balancing the recurring cost efficiency of manufacturing and the investment and maintenance costs. Organizations need to perform these analyses at scale for every relevant component. And the calculations need to be geography-specific to take into account varying labor and machine rates.

Supply chain complexity

Supply chain leaders have a particularly complex set of tasks. They must optimize the procurement spend without significant control over the engineering and manufacturing costs. They complement make-versus-buy decisions; negotiate contracts for cost and volume flexibility; reduce supply chain complexity; and ensure on-time and on-dock availability of components, products, materials, and spares. At the same time, these leaders need to watch commodity and labor pricing trends to avoid short- and long-term price changes that eat into revenue.

To manage some of this complexity, companies often analyze the most expensive components to assess the supplier’s pricing strategy. However, a thorough, bottom-up estimation of all components provides more useful data. With common parameters to compare, supply chain leaders can easily identify outliers and understand sources of a pricing squeeze.

With a comprehensive understanding of all parameters, businesses are better positioned to organize their global sourcing strategy. Firms can compare suppliers and bids on a level ground and analyze past performance on cost, quality, and schedule. Sourcing can be apportioned and routed to the appropriate contractors. All those inputs provide more accurate financial projections.

The visibility into capacity, capabilities, quality, and cost performance help manufacturers retain an edge in negotiations. Also, most suppliers benefit from understanding details like process times, material wastage, and machinery options. A comprehensive approach to sourcing — based on quality data — can simplify supply chain spread, eliminate blind spots like tail spend, improve quality issues, and reduce scrap.

Figure 2. Connected cost estimation and management across functions

Source: Infosys

Product marketing benefits

The benefits of PCO go well beyond the manufacturing and supply chain. Marketers can also use the data to compare their products to those of the competition and understand when and where to introduce new features, functionalities, or products.

A data-driven approach allows marketers to understand the associated costs of features and functions and then determine what a particular market will bear. This sort of analysis upfront lets companies pass on less-popular options and ensure that must-have features are included. For example, competitive benchmarking when using a should-cost model would help eliminate drag in production and pricing needs and also provide priced-in flexibility.

Technology as a differentiator

A cost optimization strategy is tremendously valuable, but it is not without challenges. Companies encounter a number of hurdles when starting or implementing these strategies. Those include:

• Product specificity versus black-box commercial, off-the-shelf solutions.
• Scalability to an enterprise level.
• Variety of product families.
• Teams — engineering, sourcing, manufacturing — spread across multiple work centers.
• Ways to preserve and augment cost engineering data over time.
• Effort-intensive execution and data validation.
• Absence of a single source of truth for cost engineering.

Enterprises need a technology framework that provides speed and accuracy at an industrial scale and allows executives to make intelligent decisions about global sourcing and product rationalization.

Automation toolkits, such as computer-aided design extractors, help engineers use existing data to estimate design iteration costs. Parts communization with functional and cost attributes improves cost and product reliability by reusing proven components. Template-based algorithms help create and modify cost models based on end-user requirements, providing agility despite a variety of components. Constituent cost databanks can help derive parametric calculations and refine form-fit-function-cost ontology models. Statistical tools can then be assembled for derivative cost estimation to scale estimates accurately across component families.

Cognitive models that are built using existing data can process engineering attributes, manufacturing complexity, and supply chain options. At the same time, they can significantly accelerate turnaround times. Procurement and manufacturing functions can arrive — accurately and almost instantaneously —at should-cost estimates at assembly levels and also explore the impact of changing order volumes, equipment, tooling, and locations. The estimates can establish the authenticity of purchase order pricing trends, helping identify items that need a relook and a step-down. And any changes in global commodity rates can be propagated to models through centralized databases.

The needed optimization solutions, however, vary by sector. In the aerospace industry, parts complexity is high and design decisions are made years ahead of full-scale manufacturing. There, an application framework that works across component categories helps scale costs from one component to entire product families. The automotive industry faces different challenges: multiple variants and high volume. So, enterprisewide cost and analytics platforms can have a tremendous impact on precisely managing scale and complexity without adding lead times for insights and direction.

Figure 3. Cost analytics at scale

Source: Infosys

Cost optimization in aerospace

Spirit Aerosystems, one of the world’s largest suppliers of aerostructures, greatly increased production in the past decade as worldwide demand for single-aisle and twin-aisle aircraft rose. On the 737 program, Boeing raised production from 30 to 52 planes per month. Spirit supplies 70% of that aircraft’s structure.

The billions of dollars spent on hundreds of thousands of components required executives to rethink their supply chain sourcing and procurement strategy. Switching from a relatively fragmented approach posed obvious risks and opportunities — both short and long term.

Infosys collaborated with Spirit and others to create a datacentric approach to more efficiently and effectively manage its complex supply chain. The elements included visibility, transfer of work, and in-house capability expansion.

The clean sheet evaluation approach essentially reverse-engineered parts and broke down the material, labor, and processing constituents in order to compare them to the prices. The should-cost figures showed potential savings of millions of dollars annually. The data points generated simulated sourcing scenarios and helped devise the right strategy. The wealth of data also led to more than 300 value engineering opportunities and enabled simulations of trade-off studies in half the turnaround time.

As the aviation market recovers, the new, adaptable supply chain is expected to help Spirit meet a projected increase in demand for aircraft and manage through the near-term market uncertainty.

“This blend of services and technology has helped us in deconstructing product costs the way we wanted to — with as much detail and precision as possible,” said John Pilla, Spirit’s chief technology officer at the time, who helped support this initiative. “As we traverse the changing demand environment, the insights from the data will help us define the feasibility and viability of the way we operate and help us remain competitive in these challenging times.”

Implementing a new approach

Manufacturers have a greater ability to rethink their entire value chain, but they often need help. That can lead to localization so that products are developed and customized for each geography. Legacy product support now is often farmed out to engineering services firms. The presumption is that these companies can drive value and save costs. Also, they can provide complementary skill sets, such as the implementation of industrial internet of things, artificial intelligence, machine learning, supply chain digitization, additive manufacturing, and other elements of Industry 4.0.

These types of partnerships also help chief technology officers in their efforts to increase innovation, enhance customer experience, solve stakeholder pain points, and maintain segment leadership. An effective alliance in a collaborative enterprise can extend the life of core products through modernization and testing as well as enhanced features, functionalities, and performance. The technical knowledge gained can also feed new product development.

Strategic advantage

Cost leadership is particularly critical now with the global economy’s vast uncertainty. Companies must deal with variables that stress the bottom line — material rates and labor costs rising with inflation, thus shrinking margins. At the same time, technology evolves faster than business. Competition lurks in every product category today, fueled by globalized innovation and markets.

A lean, value-conscious approach puts industrial organizations in a stronger place to deal with rivals, demand fluctuations, and business volatility. When they are looking at profits, market share, or customer base, transformational momentum is a necessity for all executives, no matter how they calculate success.