Executive Briefings

Can Analytics Help With Introductions of New Products?

If you're a football fan, you've probably seen a quarterback fumble the handoff to his halfback after the start of a play. This almost always leads to a turnover or a big loss. The same can happen with new product introductions. Too often, there is a disconnect between the R&D and marketing professionals responsible for bringing new products to market and the supply chain experts who take the "handoff" and must make the new product launch a success.

In the adrenaline-fueled world of new product introductions (NPIs), companies sometimes manage based on gut instincts, flood the market with excess product and lose focus on key strategic goals. The result? New products become unsuccessful not-so-new products as process inefficiencies cost companies millions of dollars in lost profits.

 

Using the Analytic Process

While innovation is vital in today's market, it doesn't require breaking key rules of supply chain management. In fact, some parts of the NPI process can be effectively managed and optimized with analytic tools typically applied in day-to-day operations. The key to success is establishing a process that allows analytics to be used-and, if need be, overruled-to produce "smart" flexibility during the many precarious decision points in the product lifecycle, including the following:

• Prototypes and R&D: In the early design stages of an NPI, specification changes can occur frequently, planned introduction dates may change and market potential is still in the discovery phase.

• Pilot and initial production: Initial volumes are planned out around estimates. Capital investment is growing. Demand is uncertain. Profitability is not the primary concern, while market growth, saturation and other metrics are.

• Ramp-up: Market size is still being discovered while production needs and capabilities are continually expanded. Demand is growing and supply/demand uncertainty is still high. Investment is still less of a concern, as is the need for service.

• Sustained production, maturity: As demand peaks, production capacity is potentially still growing, depending on the length of the entire product lifecycle. Demand and demand uncertainty have leveled off; production uncertainty has decreased. Service levels are in line with the firm's overall service commitments. Profitability is key.

• Ramp-down; end of life: As the product goes into its ramp-down period, production capacity for that line declines rapidly. The firm may announce very limited availability of the product, implying lower service levels. Product volume decreases while uncertainty

increases in comparison to the maturity phase. Product should not be considered for new designs.

Among the key product management challenges is the so-called "bullwhip effect," which is exacerbated during NPIs. (The bullwhip effect describes the phenomenon where in periods of rising demand, downstream participants increase their orders, and in periods of falling demand orders fall or stop in order to reduce inventory. These variations are amplified-the bullwhip effect-the farther you get from the end consumer.)

When the supply chain is forced to ramp up rapidly, there can be inadequate inventory at the beginning of the lifecycle and excess inventory at the end. When the new product is introduced or shortly thereafter, production typically cannot meet initial projected demand, resulting in shortages. Many times, distributors and resellers over-order to hedge against continuing shortages. This produces what is sometimes called phantom demand since, as supply begins to match demand, excess orders are canceled or merchandise is returned. Due to misalignments in financial and production planning, production may continue while product inventory accumulates. As demand decreases, all parties try to get rid of excess inventory, frequently with costly write-offs and liquidations.

 

Other Challenges

There are several other challenges associated with NPIs, including the following:

• Launch strategy often lacks product availability understanding or service level goals between business functions;

• Product plan assumes constant service level while promotional plan reflects demand changes throughout the product lifecycle;

• Excessive inventory at product launch (due to "channel stuffing") and at transition to the end-of-life phase;

• Uneven distribution of early adopter demand; and

• Challenge of allocating both new and old product inventory for required channel coverage.

All of these challenges are indicators that companies should use better product lifecycle management tools and the systems to support them-especially if NPIs are a key corporate strategy to gain market advantage.

Different expectations at different phases of the product lifecycle cause stress on the supply chain. At the core of the issue is the level of uncertainty associated with unknown markets. Uncertainty can cause havoc in many supply chains, as supply misalignment is amplified by spikes in demand and/or spikes in required lead times.

 

Multi-Echelon Inventory Optimization

Understanding the impact of lead times and uncertainties within the supply chain-and driving the underlying APS/ERP systems to appropriate targets that account for these uncertainties-is the key to sound financial management during a new product introduction. Some companies use lean initiatives and product design initiatives to reduce lead times and minimize some of the impact of uncertainties. However, such initiatives take time and can be controversial if all they do is push that uncertainty downstream.

A new breed of tools can survey the supply chain and buffer uncertainty in the NPI process. These multi-echelon inventory optimization (MEIO) tools examine the process end to end, across procurement, manufacturing, assembly, transportation and distribution layers. They can then find the lowest cost inventory positioning by taking advantage of postponement, pooling and similar dynamics. Since MEIO tools can optimize inventory levels across time, they are an excellent tool for NPIs. The effect is a true end-to-end examination of the process, integrating geography and product lifecycle phases to look at optimal inventory levels given "best guess" uncertainties.

For example, as a product enters the ramp-up stage, average demand is rising while demand uncertainty is high. Service levels are likewise high, yet the sourcing capabilities will be somewhat limited at this stage. Planning systems must execute to have higher levels of safety stock on hand and must plan for these levels in advance of the ramp-up stage.

As a product enters maturity, sales volumes may be at their highest levels, but safety stock targets will be lower in proportion to average demand because of improved forecasting. Also, service level goals may still be high, but not as high as during the market saturation phase. Finally, increases in yield capabilities and better capacity availability further drives down uncertainty on the supply side, requiring proportionally lower safety stock for buffering.

 

Applying MEIO to NPI

To see how MEIO tools work in the NPI process, consider an example from the fashion industry. A company is introducing two new sportswear jerseys to the market; the entire season of jerseys is expected to be about 13 weeks. The jersey provider uses an NPI process involving a cross-functional team of designers, marketers and supply chain professionals. The company goes through rigorous analysis on both new products, enabled by decision-support systems such as the MEIO tools mentioned above. These systems allow users to quickly estimate three to five different sales scenarios based on projected demand and the projected impact of growing capacity and lead times. The company is able to create best-case inventory plans, which are put into motion.

A week or two later, the process is reviewed. The company finds that one product is moving well in key test areas while the other is not. By entering their new estimates into the analytic tools, the team can optimize lead times, evaluate expedited shipping decisions and mobilize supply chain capabilities on the hot item, while re-evaluating their position on the slower moving item.

In this example, the process enables the NPI team to minimize inventory buildup on the slow item, while maximizing profits on the fast moving item. While the team is still using "best guess" estimates to make decisions, these decisions are linked in an analytic framework that drives operational efficiencies.

Driving Better Alignment

A cross-functional process supported by tools such as MEIO can help manage NPI challenges. It can dampen the bullwhip effect by enabling an understanding of key cost drivers. More importantly, analytic targets can be used to drive underlying ERP/APS systems. As a result, new products can more easily transition into established products since similar target-setting methodologies are used in both product sets.

With a better understanding of normal product lifecycles, companies can establish business processes to support better alignment of R&D, marketing and supply chain functions; improve capability; reclaim investment; and empower flexibility in the notoriously unstable NPI process. Worst-, average- and best-case scenarios can be quickly simulated for tradeoff analysis. And, with systems in place, companies can quickly review market conditions, make decisions and activate supply chains at critical times-just when such flexibility is needed most.

If you're a football fan, you've probably seen a quarterback fumble the handoff to his halfback after the start of a play. This almost always leads to a turnover or a big loss. The same can happen with new product introductions. Too often, there is a disconnect between the R&D and marketing professionals responsible for bringing new products to market and the supply chain experts who take the "handoff" and must make the new product launch a success.

In the adrenaline-fueled world of new product introductions (NPIs), companies sometimes manage based on gut instincts, flood the market with excess product and lose focus on key strategic goals. The result? New products become unsuccessful not-so-new products as process inefficiencies cost companies millions of dollars in lost profits.

 

Using the Analytic Process

While innovation is vital in today's market, it doesn't require breaking key rules of supply chain management. In fact, some parts of the NPI process can be effectively managed and optimized with analytic tools typically applied in day-to-day operations. The key to success is establishing a process that allows analytics to be used-and, if need be, overruled-to produce "smart" flexibility during the many precarious decision points in the product lifecycle, including the following:

• Prototypes and R&D: In the early design stages of an NPI, specification changes can occur frequently, planned introduction dates may change and market potential is still in the discovery phase.

• Pilot and initial production: Initial volumes are planned out around estimates. Capital investment is growing. Demand is uncertain. Profitability is not the primary concern, while market growth, saturation and other metrics are.

• Ramp-up: Market size is still being discovered while production needs and capabilities are continually expanded. Demand is growing and supply/demand uncertainty is still high. Investment is still less of a concern, as is the need for service.

• Sustained production, maturity: As demand peaks, production capacity is potentially still growing, depending on the length of the entire product lifecycle. Demand and demand uncertainty have leveled off; production uncertainty has decreased. Service levels are in line with the firm's overall service commitments. Profitability is key.

• Ramp-down; end of life: As the product goes into its ramp-down period, production capacity for that line declines rapidly. The firm may announce very limited availability of the product, implying lower service levels. Product volume decreases while uncertainty

increases in comparison to the maturity phase. Product should not be considered for new designs.

Among the key product management challenges is the so-called "bullwhip effect," which is exacerbated during NPIs. (The bullwhip effect describes the phenomenon where in periods of rising demand, downstream participants increase their orders, and in periods of falling demand orders fall or stop in order to reduce inventory. These variations are amplified-the bullwhip effect-the farther you get from the end consumer.)

When the supply chain is forced to ramp up rapidly, there can be inadequate inventory at the beginning of the lifecycle and excess inventory at the end. When the new product is introduced or shortly thereafter, production typically cannot meet initial projected demand, resulting in shortages. Many times, distributors and resellers over-order to hedge against continuing shortages. This produces what is sometimes called phantom demand since, as supply begins to match demand, excess orders are canceled or merchandise is returned. Due to misalignments in financial and production planning, production may continue while product inventory accumulates. As demand decreases, all parties try to get rid of excess inventory, frequently with costly write-offs and liquidations.

 

Other Challenges

There are several other challenges associated with NPIs, including the following:

• Launch strategy often lacks product availability understanding or service level goals between business functions;

• Product plan assumes constant service level while promotional plan reflects demand changes throughout the product lifecycle;

• Excessive inventory at product launch (due to "channel stuffing") and at transition to the end-of-life phase;

• Uneven distribution of early adopter demand; and

• Challenge of allocating both new and old product inventory for required channel coverage.

All of these challenges are indicators that companies should use better product lifecycle management tools and the systems to support them-especially if NPIs are a key corporate strategy to gain market advantage.

Different expectations at different phases of the product lifecycle cause stress on the supply chain. At the core of the issue is the level of uncertainty associated with unknown markets. Uncertainty can cause havoc in many supply chains, as supply misalignment is amplified by spikes in demand and/or spikes in required lead times.

 

Multi-Echelon Inventory Optimization

Understanding the impact of lead times and uncertainties within the supply chain-and driving the underlying APS/ERP systems to appropriate targets that account for these uncertainties-is the key to sound financial management during a new product introduction. Some companies use lean initiatives and product design initiatives to reduce lead times and minimize some of the impact of uncertainties. However, such initiatives take time and can be controversial if all they do is push that uncertainty downstream.

A new breed of tools can survey the supply chain and buffer uncertainty in the NPI process. These multi-echelon inventory optimization (MEIO) tools examine the process end to end, across procurement, manufacturing, assembly, transportation and distribution layers. They can then find the lowest cost inventory positioning by taking advantage of postponement, pooling and similar dynamics. Since MEIO tools can optimize inventory levels across time, they are an excellent tool for NPIs. The effect is a true end-to-end examination of the process, integrating geography and product lifecycle phases to look at optimal inventory levels given "best guess" uncertainties.

For example, as a product enters the ramp-up stage, average demand is rising while demand uncertainty is high. Service levels are likewise high, yet the sourcing capabilities will be somewhat limited at this stage. Planning systems must execute to have higher levels of safety stock on hand and must plan for these levels in advance of the ramp-up stage.

As a product enters maturity, sales volumes may be at their highest levels, but safety stock targets will be lower in proportion to average demand because of improved forecasting. Also, service level goals may still be high, but not as high as during the market saturation phase. Finally, increases in yield capabilities and better capacity availability further drives down uncertainty on the supply side, requiring proportionally lower safety stock for buffering.

 

Applying MEIO to NPI

To see how MEIO tools work in the NPI process, consider an example from the fashion industry. A company is introducing two new sportswear jerseys to the market; the entire season of jerseys is expected to be about 13 weeks. The jersey provider uses an NPI process involving a cross-functional team of designers, marketers and supply chain professionals. The company goes through rigorous analysis on both new products, enabled by decision-support systems such as the MEIO tools mentioned above. These systems allow users to quickly estimate three to five different sales scenarios based on projected demand and the projected impact of growing capacity and lead times. The company is able to create best-case inventory plans, which are put into motion.

A week or two later, the process is reviewed. The company finds that one product is moving well in key test areas while the other is not. By entering their new estimates into the analytic tools, the team can optimize lead times, evaluate expedited shipping decisions and mobilize supply chain capabilities on the hot item, while re-evaluating their position on the slower moving item.

In this example, the process enables the NPI team to minimize inventory buildup on the slow item, while maximizing profits on the fast moving item. While the team is still using "best guess" estimates to make decisions, these decisions are linked in an analytic framework that drives operational efficiencies.

Driving Better Alignment

A cross-functional process supported by tools such as MEIO can help manage NPI challenges. It can dampen the bullwhip effect by enabling an understanding of key cost drivers. More importantly, analytic targets can be used to drive underlying ERP/APS systems. As a result, new products can more easily transition into established products since similar target-setting methodologies are used in both product sets.

With a better understanding of normal product lifecycles, companies can establish business processes to support better alignment of R&D, marketing and supply chain functions; improve capability; reclaim investment; and empower flexibility in the notoriously unstable NPI process. Worst-, average- and best-case scenarios can be quickly simulated for tradeoff analysis. And, with systems in place, companies can quickly review market conditions, make decisions and activate supply chains at critical times-just when such flexibility is needed most.