How Technology Drives Business Excellence

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How Technology Drives Business Excellence

Business Essentials · Chapter 18: Technology and Ethics in BusinessSection 18.2 · lesson 82 of 10513 min · free with a free account

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In this lesson

Technology makes a better process possible; people have to design it.

  • Explain how technology enables improvement by function
  • Distinguish incremental improvement from fundamental redesign
  • Decide which tasks are worth automating
  • Assess an improvement initiative with the right measures

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Technology and Ethics in Business

How Technology Drives Business Excellence

Businesses spend heavily on technology, expecting it to make them better. This section is about why that expectation so often disappoints, and what actually works. We'll start with a practical rule. Technology enables improvement, but it doesn't produce it. We'll trace that rule through six business functions, each with its own mechanism and measure. We'll compare incremental improvement with fundamental redesign, including redesign's honest record. Then a bank that could automate its approvals, or redesign them, with very different results. We'll cover what's worth automating, how to measure an initiative honestly, and why gains fade. And we'll end with a warehouse that bought scanners, and got faster paper.

The Practical Rule

Technology Enables; It Doesn't Produce

Chapter seventeen established that a system creates advantage only when an organization is organized to exploit it. This section turns that idea into a practical rule. Technology enables improvement. It doesn't produce it. A system makes a better process possible. It doesn't, by itself, make the process better. The improvement has to be designed. Then the technology is chosen to support the design that results. The alternative is adopting a system first, in the hope that improvement follows automatically. A business that reverses the order, choosing a system and then asking what it might improve, routinely gets less. Less than a business that starts from the improvement it wants, and chooses technology to serve it. Keep that order in mind. Every example in this section is a variation on it.

Table 18.2.1 (1 of 2)

Enablement, Mechanism, and Measure

Apply the rule function by function, and it takes concrete form. Each row has an enablement, a mechanism, and a measure. In operations, technology enables scheduling, tracking, and quality data. The mechanism is seeing where a process is falling behind, before it becomes a crisis. You'd know it worked from the on-time completion rate, and the defect rate. In supply chain management, technology gives visibility into where materials and shipments actually are. It coordinates parties who previously worked from incomplete information. The evidence would be shorter delivery lead times, and fewer stockouts. In marketing, technology enables targeting an offer to the customers most likely to want it. And it measures whether that offer actually worked. The measures here are response rate, and cost per acquisition. Each row tells the same story. Technology supplies a capability. Whether it improves anything depends on use.

Table 18.2.1 (2 of 2)

Enablement, Mechanism, and Measure

In sales and service, technology gives a full view of a customer's history. So anyone dealing with that customer has the context needed to help them well. The measures are first-contact resolution, and the repeat-contact rate. In finance, technology enables faster, more accurate reporting, and the control processes behind it. That catches a variance sooner, following the control cycle from chapter five. Watch the time to close the books, and report errors. In human resources, technology handles routine administration, and supplies workforce data for decisions. It frees staff time for higher-value work. Measure processing time per request, and staff time reallocated. Now look at the last column, across all six rows. The measure is what turns a claimed improvement into a demonstrated one. An initiative in any of these functions should be able to name its own row before it's approved, not only afterward.

Process Redesign

Improve the Process, or Start Over

Process redesign is the deliberate reworking of how a business process operates. It comes in two forms. Incremental improvement, which we met with total quality management, makes a series of smaller changes to an existing process. It keeps the basic shape, and removes waste and friction from it. Fundamental redesign starts over. It asks what the process should look like, given what's now possible. Not what small changes could be made to the process that already exists. So when is starting over the right call? When the existing process encodes assumptions that no longer hold. Picture a five-step paper approval chain, built when information traveled only as fast as a document carried from desk to desk. That assumption no longer describes how information moves in most businesses today.

The Honest Record

Why Fundamental Redesign Often Fails

Fundamental redesign has an honest record, and it includes a documented history of high failure rates. Redesign projects have often failed to deliver the improvement they promised. Sometimes the redesign itself was poorly conceived. Other times, a well-conceived redesign was poorly implemented. It met resistance, or lacked the organizational change needed to make it stick. That's the territory of change management, from chapter five. Fundamental redesign has also been closely associated with workforce reduction. A process rebuilt around what's newly possible frequently needs fewer people to run it. But that association is a documented pattern, not a necessary feature of every redesign. Some redesigns redeploy staff to higher-value work, rather than reducing headcount. Which happens depends on the business's own choices, not on the redesign method itself.

Figure 18.2.1

Automate the Process, or Redesign It?

Here's the difference between automating and redesigning, in one example. The figures are illustrative, constructed for the exercise. A regional bank approves increases to customers' credit limits. The process needs five separate sign-offs on paper, passed from desk to desk, and takes nine days on average. In the first path, the bank installs software, so the same five approvals happen electronically. It's faster, because no document has to be carried between desks. But every one of the five approvals still happens. The average time falls only modestly, from nine days to seven. In the second path, the bank asks why five approvals are needed at all. It finds the two most cautious steps were set up years ago, for a much larger range of amounts than it now typically approves. So it redesigns the process. Requests below a stated threshold are decided automatically, against clear rules. Only requests above the threshold, or that fail one of the rules, reach a person. For most requests, the average time falls to under a day. Both paths use similar technology. The results are very different. Only the second path changed what the process actually required.

What to Automate

Rule-Based, High-Volume, Stable

Not every task is a good candidate for automation. The strongest share three characteristics. They're rule-based. The correct outcome can be stated as a clear rule, rather than depending on interpretation. They're high-volume. The same decision recurs often enough that automating it saves meaningful time. And they're stable. The rule doesn't change so often that maintaining the automation costs more than it saves. The boundary is reached where judgment is required. That means a decision that weighs unusual or conflicting considerations. Or one that carries a serious consequence for an individual, or that no rule could anticipate in advance. Those are poor candidates for full automation, however routine they look on the surface. The bank's redesign drew exactly this boundary, and drew it explicitly. Routine requests go to the rule. The exceptions go to a person.

Measuring Both Sides

Efficiency Inside, Frustration Outside

A common and avoidable failure is technology that improves internal efficiency while worsening the customer's experience. Think of a phone system that routes callers through many automated menus before they reach a person. It may cut staffing cost. It also raises the time and frustration a customer goes through before getting help. Why is that avoidable? Because it usually comes from measuring only the internal side, like cost per call. Nobody measured the customer's side, like time to resolution, or the customer's own rating of the experience. So the change looks like a clear success internally, while quietly damaging the relationship. Credible measurement needs three elements. The baseline is performance measured before the change. Without it, no later figure can show that anything improved. The measure is the specific indicator the initiative should move, stated precisely enough that everyone agrees what counts. And the balancing measure is a second indicator, tracked alongside, that would reveal harm appearing somewhere else. In the phone example, a customer experience measure would have played that role. Report one improved number, with no baseline and no balancing measure, and you haven't shown improvement on balance.

The Capability Question

Specify, Implement, Use

An organization needs people who can do three things with a new technology. First, specify what it should do, in enough detail that a vendor or developer can build or configure it correctly. Second, implement it: install and configure the system, and migrate the data it needs. Third, use it, applying it correctly to the daily work it was meant to support. The absence of any one of these is the most common reason a technology investment underdelivers. Suppose a business can specify and implement, but never trained the staff who use the system daily. The system gets installed correctly, and used poorly. Now suppose the users are capable, but nobody could specify the system correctly in the first place. Then it gets built to solve the wrong problem.

Making It Last

Sustaining a Gain, and Judging One

Improvement initiatives decay when the attention that drove them moves on. The discipline that produced the gain tends to relax once the project is declared complete. Regular review of the measure and the balancing measure quietly stops. And performance drifts back toward where it was. Sustaining a gain needs structures that hold it in place, without continuous special attention. Build the measure into routine reporting, not just tracking during the project. Write the new way of working into a standard procedure, rather than leaving it as an informal habit. And name an owner, responsible for the process after the project team moves on. An initiative with no such structure should be expected to fade, whatever its initial success. To assess any described initiative, ask four questions in sequence. What problem was it meant to solve? What mechanism does the technology actually change to solve it? What measure shows it worked, including a balancing measure for likely side effects? And what capability, to specify, implement, and use the system, did the organization actually have? If an initiative can't answer all four, it hasn't yet earned the credit it often gets, for the technology alone.

Case Vignette

Faster Paper

Here's a composite case, built for instruction. Devraj manages operations at a regional home-goods distributor. Warehouse staff record inventory counts on paper forms, and an office clerk later types them into a spreadsheet. The process is prone to delay, and to transcription error. Devraj leads the purchase of handheld scanners, expecting them to solve both problems at once. Counts are recorded faster, and the transcription step disappears. That's a real improvement. But the approval chain behind a stock adjustment hasn't changed. A discrepancy is still reviewed by a supervisor, then by the inventory manager. Then it's reconciled by the same office clerk, who now checks the scanner output instead of typing. The time from a count to a corrected record falls only slightly. Four of the five original steps are still there. Devraj faces a trade-off. He could declare an easy win, or ask whether the approval chain still makes sense. Those reviews were set up for reasons that may, or may not, still apply. His reasoning is to examine why each step exists, just as the bank examined its five approvals. The scanners automated a process nobody had redesigned. And a small gain is the signature of exactly that.

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FAQ

Questions students ask us

About How Technology Drives Business Excellence

Does technology improve business performance on its own?
No, technology enables improvement but does not produce it by itself. The improvement has to be designed first, and the technology chosen to support that design. A business that buys a system and then asks what it might improve routinely gets less. Handheld scanners that replace paper inventory counts, for example, remove a typing step, but if the approval chain behind each stock adjustment stays the same, the overall time barely falls.
What is the difference between incremental improvement and process redesign?
Incremental improvement makes a series of small changes to an existing process, keeping its basic shape while removing waste, as in total quality management. Fundamental redesign, often called business process reengineering, starts over and asks what the process should look like given what is now possible. Redesign fits when a process encodes assumptions that no longer hold, but it has a documented history of high failure rates, from poor design or poor implementation.
Which business tasks are best suited for automation?
The best candidates for automation are rule-based, high-volume, and stable tasks. Rule-based means the correct outcome can be stated as a clear rule; high-volume means the decision recurs often enough to save meaningful time; stable means the rule rarely changes, so upkeep does not outweigh savings. Decisions that require judgment, weigh unusual factors, or seriously affect an individual are poor candidates, so routine cases can go to the rule and exceptions to a person.
How do you measure whether a technology initiative worked?
Credible measurement needs a baseline taken before the change, a specific measure the initiative should move, and a balancing measure that would reveal harm elsewhere. An automated phone menu, for instance, may cut cost per call while raising customers' time to resolution, which only a customer-side balancing measure would catch. To keep the gain, build the measure into routine reporting, write the new method into standard procedure, and name a process owner.

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