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Management Information Systems: Using Data for Operational Control
Business Essentials · Chapter 8: Operations Management and Information SystemsSection 8.5 · lesson 46 of 10513 min · free with a free account
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In this lesson
The data an operation needs, and how it turns into control.
- Describe the systems that support operations
- Explain how operational data supports control
- Judge metrics, cadence, and data quality
- Identify the data a described operation needs
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Everything said in the video, section by section (1,842 words). Read along, or search it.
Operations Management and Information Systems
Management Information Systems: Using Data for Operational Control
Every operation runs on data. Orders, scans, stock counts, schedules. This section closes the chapter by asking what that data is for. We'll separate the operational role of information systems from the analytical roles covered later. We'll look at six systems that run an operation, and what goes wrong when their data are bad. Then the control loop, and where the system fits inside it. Then metrics, dashboards, and exception reports. And the dimensions of data quality. We'll finish with integration, timing, automation, and a lesson about installing new systems. Keep one question in mind. Does this data arrive in time, and in a form someone can act on?
Information Systems
The Nervous System of an Operation
An information system is an organized combination of people, hardware, software, data, and procedures. It collects, stores, processes, and distributes information. Information systems serve an organization in three distinct roles. In the operational role, they record the events of the business as they happen. They supply the data that lets the operation run, and be controlled, from hour to hour. In the decision-support role, they help managers analyze data and choose among alternatives. In the strategic role, they're used to gain competitive advantage. This section deals with the first role only. Chapter seventeen covers the other two, including business intelligence. Keeping the roles apart clarifies what an operational system is for. It isn't primarily a tool for analysis. It's the nervous system that tells the operation what's happening, while it happens.
Table 8.5.1
6 Systems That Run an Operation
Several systems run an operation, and each records a different kind of event. A transaction processing system captures routine events as they occur. Sales, receipts, payments, and movements of goods. It's the foundation the other operational systems rest on. An inventory and warehouse system records what's held, where it is, and when it moves. It supports reordering, put-away, and picking. A scheduling system records which work is assigned to which resource, and when. It supports sequence, staffing, and loading. A point-of-sale system records each sale as it's made. The item, the quantity, and the price. It supports decisions about replenishment, and staffing at the counter. An order management system follows customer orders from receipt through delivery. It supports promising dates, allocating stock, and handling changes. And an enterprise system integrates these with purchasing, finance, and human resources, in one common database. So an event recorded in one area is visible to all the others.
Table 8.5.1, Continued
When the Data Are Wrong
Each system also fails in its own characteristic way when its data are wrong. Bad transaction records make every report built on them wrong. The errors pile up unnoticed, until an audit or a customer complaint. Bad inventory data cause stockouts while the system shows stock, or overstock from needless reorders. Pickers get sent to empty locations. Bad scheduling data leave some resources idle and others overloaded, with promised dates that can't be met. Point-of-sale errors charge wrong prices, misjudge stock, and credit sales to the wrong item. Order management errors mean wrong or late deliveries, and customers given inconsistent answers. And in an enterprise system, departments act on different versions of the facts, so mistakes spread across functions. So bad data don't do general damage. The damage is specific to the decision the system feeds. That's why data quality checks should be aimed at the records where a mistake is most costly.
The Transaction
The Atomic Unit of Operational Information
A transaction is the smallest complete record of a business event. A sale, a receipt of goods, a movement from one location to another, or an adjustment. It's the atomic unit of operational information. Every figure in every report is built by adding up transactions. That has a big consequence. The quality of every downstream report depends on whether each underlying record was accurate when it was made. A miscounted receipt, a sale scanned twice, or an adjustment never entered doesn't stay a small error. It's carried into every total, every average, and every dashboard that draws on that record. So where should bad data be stopped? At the point of capture. That's where a scan, a keystroke, or an automatic sensor creates the record. Correcting figures in a report after the fact only treats the symptom.
Figure 8.5.1
The Control Loop in Order Fulfillment
Operational control was introduced in section five point two, as the fourth management function. It needs four elements. A standard defines what should happen. A measurement records what did happen. A comparison sets the measurement against the standard, to reveal any gap. And an action corrects the gap, or changes the process. In an order-fulfillment operation, the information system supplies two of these, the measurement and the comparison. It doesn't supply the standard. That's a human judgment about what's acceptable. And in most cases it doesn't supply the action either. That's a decision about what to do. The system's value is that it measures and compares continuously, at a scale no person could match. That reserves human attention for the actions that need judgment. But if measurement or comparison is missing, slow, or wrong, the loop breaks. Then the supervisor can't act on what's really happening, however capable that supervisor is.
Metrics and Exceptions
Run on a Handful of Measures
An operation can't be run on hundreds of measures. It runs on a small set, perhaps a handful. For a fulfillment center, that might be orders shipped on time, orders awaiting picking, inventory accuracy, and equipment downtime. A dashboard presents those key measures compactly, so a manager can grasp the state of the operation at a glance. Each measure needs two things beyond its definition. The first is a cadence, how often it's reviewed. That should match how quickly a problem in it can grow. Downtime on a bottleneck machine matters by the hour. Scrap rate may be reviewed daily, or weekly. The second is an escalation trigger. A stated condition that sends the measure to a higher level for action. An order waiting beyond a set number of hours, for instance. Without a trigger, a measure is only observed. With one, it's managed. Exception reporting goes further. It reports only the items that fall outside the standard. A report listing every order, every stock line, and every machine is a report nobody reads. The few that need action get lost. An exception report points straight at the late orders, the low stock lines, and the machines that are down. It depends on a clear standard, because an exception is defined as a departure from one.
Data Quality
Fit for the Use It Is Put To
Data quality is the degree to which data are fit for the use they're put to. Everything so far depends on it. It has several dimensions. Accuracy is whether the data correctly describe the real event. Does the count on hand match what's on the shelf? Completeness is whether all the required records and fields are present. Timeliness is whether the data arrive soon enough to be useful. And consistency is whether the same fact is recorded the same way, everywhere it appears. A fifth problem deserves special mention. It's the definition problem. It arises when two departments count the same thing differently. Suppose sales counts an order as complete when it's placed. The warehouse counts it complete when it's shipped. The two will report different figures for the same week, and each will be correct, by its own definition. So agreeing on definitions matters just as much as agreeing on accuracy.
Integration
A Single Source of Truth
When the same data are held in several systems, they tend to drift apart. A customer's address gets updated in one system, but not another. Or a stock quantity is held in both the warehouse system and the finance system. Sooner or later the copies disagree, and no one can say which figure to trust. The answer is the principle of a single source of truth. Each item of data is recorded once, in one authoritative place, and shared from there to everything that needs it. Enterprise systems pursue this by keeping one common database. The principle is easy to state. It's hard to achieve. Many organizations already have legacy systems, spreadsheets kept by individuals, and separate departmental databases. Each of those is another copy, waiting to disagree.
Real Time or Periodic?
As Fresh as the Decision Cycle
Real-time information is available the moment the event occurs. Periodic information is collected and reported at intervals, hourly, daily, or weekly. It's tempting to believe real time is always better. But the choice should follow the decision. Data need to be only as fresh as the decision cycle they serve. A manager who reviews staffing once a shift gains nothing from a figure that updates every second. And real-time data cost more, in sensors, communications, and systems capacity. They can also overload managers with movements that carry no meaning. So where an event can only be corrected within minutes, like a fault on a fast production line, real time is worth the cost. Where the operation can only act at the end of the day, a daily report is enough.
Automation and Its Limits
Rules, Boundaries, and Redesign
Some operational decisions can be made by the system itself, within limits set by management. For example, a rule can generate a purchase order when an item's stock falls below its reorder point. Provided the order is under a stated value, and goes to an approved supplier. Automation removes delay and routine effort, and applies the same rule every time. The boundary is set by the limits. An unusually large order, an unapproved supplier, or implausible data should go back to a person. A system allowed to act without limits will repeat an error at the speed of a computer. There's a persistent implementation lesson too. A system laid over a process that hasn't been redesigned produces the old process, at greater cost. Unnecessary approvals, duplicate data entry, and workarounds all get carried out faithfully, now with an expensive license behind them. So redesign comes first, eliminating steps that need not exist, as section eight point two discussed. Finally, operational systems record what happened, and support routine control. Questions like which markets to enter, or how customer behavior is changing, belong to decision support, in chapter seventeen.
Case Vignette (Composite)
The Monday Report
Here's a composite case. The organization and the person are fictional. Dalia is an operations manager at Sorrel Parcel Services. Each Monday she gets a report of last week's late deliveries. By Monday, the late parcels are days old, and the customers have already complained. The report can only explain the past. Her first instinct is to add twelve more measures to the weekly report, hoping more detail will reveal the causes. She could add the measures, accept the delay, or change how often the information arrives. The system already records each parcel's scans, and its promised delivery time. The trade-off is more information per report, against more frequent information in a shorter report. The problem isn't what's measured. It's when it arrives. The data to spot a likely late parcel exist on the day it's scanned at the depot. So Dalia has the system produce a short exception list each morning, of parcels that will miss their promised times unless someone acts. Any parcel listed two days running is escalated to the depot supervisor. The cadence moves from weekly to daily, and the report shrinks to the exceptions, without a single new measure.
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