13.1. Diagnosing the Problem

The team began by tracking a series of decisions (related to matters ranging from strategy and operations to talent management and pricing) as they made their way through the organization. All the while, the team recorded the duration and ultimate result of each participant's involvement in these strategic decisions. The team then built decision-process maps based on the findings. Figure 13.1 depicts the kinds of information gained from this process.

These efforts revealed troubling news. For one thing, decisions at Cedarwood tended to involve too many people—perhaps a legacy from the company's start-up days, when everyone had a hand in all decisions. In addition, many decisions (minor and major alike) escalated to the highest levels in the organization. Finally, decisions were revisited frequently, suggesting that although participants may have thought they had reached agreement, they in fact had not.

The examples were disturbing. One capital-expenditure decision that originated during a conversation among four directors wasn't resolved for five months. The decision embarked on a long journey that re-involved the four directors at several points—and consumed the time of two lower-level managers, a director in yet another department, two executives, and analysts who felt compelled to run and rerun numbers several times. What was the ultimate result of this investment of time? The approval of a decision that had been amended only slightly from its original version.

Figure 13.1. MAPPING THE DECISION STAGES

This kind of inefficiency permeated decisions ranging from pricing and hiring to promotion and even trivial travel approvals. On minor approvals, the extensive collaboration racked up staggering costs. As just one example, a decision regarding a $39,000 purchase ate up $17,000 in labor costs (participant time consumed) over two months. Another decision that dragged on for months involved 25 people in one month alone and incurred labor costs of more than $60,000.

But labor costs were just part of the damage wrought by Cedarwood's flawed decision processes. Opportunity costs were also unacceptably high. In the pharmaceutical industry, even a one-day delay in introducing a new product to market can cost $1 million in lost revenues. And when managers are spending most of their time grappling with trivial or routine decisions—not on moving products through the pipeline—the risk of product delays soars.

The team theorized that decision-making inefficiencies may have stemmed in part from Cedarwood's rapid growth. As the company quickly added new hires to support its growth, many of them came from larger organizations that had more-formalized decision protocols. Newcomers unfamiliar with Cedarwood's culture wondered, "How do they do things here?" Unable to find any clear documentation about how decisions should be made, employees lobbed questions not only to peers around them but also to leaders above them—escalating decisions to higher and higher ranks. Furthermore, there were no processes in place for enabling new hires to get to know veterans in the company. Thus recent recruits didn't know whom to go to for advice.

With these problems in mind, the team set out to conduct an organizational network analysis (ONA), which could reveal how networks might be reshaped to improve decision-making efficiency. While many organizations' troubles stem from insufficient connectivity in their information, advice, and decision-making networks, the team's findings so far suggested that the solution to Cedarwood's problems might lie in reducing connectivity.