To perform effective root-cause diagnostics, companies need to leverage all relevant supply chain data, not just what is in a departmental data mart.
The Issue: Toyota recalls millions of vehicles. The recalls spread beyond the United States to Asia and Europe. Toyota suspends the sale and production of eight popular models. The company faces federal scrutiny.
The impact? Billions of dollars in lost sales and warranty costs, potential loss of entire markets and most importantly, a loss of reputation. For a company that built its reputation on quality and dependability, these recalls amount to existential uncertainty.
Market watchers and industry analysts can argue about what caused this crisis. Was it management's excessive focus on cost cutting? A change in sourcing strategies from dependable suppliers to unfamiliar, new ones? Or was it the desire for a rapid expansion to become the No. 1 automaker in the world? The reality is that the once-revered company is facing the biggest crisis since its inception. While there may be many causes leading to the crises, the deeper underlying issue is the disconnect between the company's strategy and execution which was caused by a dissonance between the performance metrics at the operational level and at the strategic level.
Toyota's story is symptomatic of the issues facing most large enterprises today. Companies with complex, global supply chains have limited visibility across their supply chain, which makes performance monitoring and management difficult. Often, a decision taken by one business unit can have a significant impact on the performance of the entire supply chain and the company overall. The situation is even more challenging because it is usually difficult to get to the root of the problem due to multiple disconnects at department and geographic levels.
Most companies have done a good job of establishing performance metrics at the operational and departmental level. However, they have done so in isolation with a view to improve the performance of individual processes. They continue to struggle with building and maintaining alignment between cross-departmental metrics and mapping those operational metrics to enterprise KPIs. This prevalence of conflicting metrics and KPIs across the supply chain causes sub-optimal and, at times, counterproductive decisions.
The problem is difficult but not unsolvable. Companies can go a long way toward weeding out inefficiencies and improving overall performance across the entire supply chain by focusing on three primary factors:
Measuring What Really Matters
A number of companies get caught in measuring just for the sake of measurement. Performance of each objective can be measured in multiple ways: by time dimension, by cost or by effectiveness. The result is too many meaningless and conflicting metrics that don't directly relate to the end objective.
The key to choosing appropriate metrics lies in understanding which metrics really matter. For example, if the end goal is to improve customer service, a company should focus on order fulfillment rate and fulfillment lead time rather than forecast error.
Aligning operational metrics with strategic enterprise-level KPIs. To ensure overall improvement in supply chain performance, it is important to balance departmental and geographic goals with strategic enterprise-wide goals. However, it is easier said than done.
When it comes to supply chains, most managers aspire to achieve too many objectives simultaneously without accounting for inherent trade-offs. For example, the goal of reducing supply costs may have a negative impact on desired product quality, lead time or the proximity of the supply base. Unless organizations do a good job of aligning these cross -departmental goals and ensuring that operational KPIs map to the enterprise's strategic KPIs, they will continue to measure and reward against conflicting metrics, resulting in inefficiencies and counter-productive decisions, including those that lead to recalls.
Leveraging Supply Chain Data
Companies often ignore the most compelling asset they have: The data residing in supply chain management systems. It's data that has been assiduously collected over the years, but has been historically underleveraged. When managing performance, companies get so caught up in measuring and monitoring KPIs associated with various processes that they forget these KPIs are not the end of the analysis; they are instead a starting point for identifying the root cause of problems so that corrective actions can be taken. And, to perform effective root-cause diagnostics, companies need to leverage all relevant supply chain data, not just what is in a departmental data mart.
The bottom line is that the performance of the entire supply chain can only be improved when organizations take a holistic view of the entire supply chain. With so much data across systems, the only way an organization can make timely, forward-looking decisions is by extracting and sharing meaningful information from the data regardless of where it resides.
Futurist Thornton May captured the essence of this dynamic recently, "This is a new age economy. Going forward, the companies and individuals that will succeed in this new age economy will be the ones that master the art of collecting, organizing, analyzing and acting upon the masses of data available to them."
Ritu Jain is the Industry Marketing Manager, Manufacturing and Supply Chain for SAS, a provider of business analytics software and services. http://www.sas.com
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