The management environment is teeming with problems. In fact, managers face complex systems of problems. Fortunately, problem solving can be an orderly process in which a core problem is identified, and then relevant data is gathered leading to an understanding of the problem and its potential solutions. Finally, a manager can apply his or her experience and judgment to recommend a response. While many people once considered data analysis to be the most complex part of this process, the processing power of today’s calculators and computers now makes this relatively easy. Now, many managers find it most challenging to correctly identify the problem.
A problem can be defined as a difference between where we are and where we want to be. In order to recognize that we have a problem, we need to know:
- Where are we?
- Where do we want to be?
Once we have answered these questions, we will begin to discover potential underlying problems. For example, if our sales goal for the current month is $50,000 in revenue, and we have only generated $10,000 during the first half of the month, our problem is clearly evident, but the reasons may not be.
We must dig into the problem. Why did it occur? What are its component parts? What can we do to get back on track? We must clearly identify the nature and extent of the problem. As an example, I am going to use a problem that occurs in every organization at one time or another.
At this stage, we have identified the general nature of our problem and can state the overall objective. We need to do something to stem our revenue decline and get the firm into a growth mode. This is our overall objective, and it will serve us better if we state it in measurable terms, e.g. bring revenue back up to the $200,000 level by July and grow revenues by $5,000 a month through December.
Our “Golden Rule” for analyzing such problems is MISS or, “Make It Simple and Straightforward.” MISS is difficult to do. In fact, we are much better at making things complicated and convoluted (MICC). In most management problems, there are many possible parts and, following our MISS rule (make it simple and straightforward), we can often break the problem into simpler parts.
One way to do this is to look at the background of the problem. The better we know the nature of our organization and business, the easier it will be to understand the background of our problem. We should always start this investigation by asking, “What is, or isn’t, associated with our problem?”
Let us go back to our revenue problem. We discovered that the problem began in February, continued in March and April, and appears to be present during the first two weeks of May. The decline is not due to internal factors, such as a failure to meet production schedules, since we are operating at only 65 percent capacity. And nothing has changed concerning quality control.
Further investigation shows that we reduced our advertising budget in January. In looking at the external environment, a scan of macro-economic statistics shows almost no change during the period of interest. Our major competitor has redesigned a competing product line and made it available in pastel colors. A brief analysis of his first quarter SEC filing indicates that his monthly sales are up $18,000 for the first quarter. We have not been able to identify any relevant other factors that have changed.
By going through this process, we have come up with two possible factors that may account for our drop in revenues: A reduction in advertising, and a competitor introducing a competing product. Note that the first factor is internal. We can elect to increase our advertising. The second factor, however, is external and cannot be counteracted unless we create an environmental change. There may also be other drivers we have not identified.
There are several ways in which to broaden our knowledge of other possible drivers. We can ask for relevant information from our management information system, we can do searches of libraries and online databases, we can do market research, we can bring in outside experts, and we can ask our resident experts.
We often find it useful to look at what other people have experienced in dealing with similar problems. A good way to do this is to review the literature and databases available online. In situations where the focus of the problem seems to be on the external environment, we can conduct market research.
Going back to our problem of declining revenue, we could ask a sample of people who bought our competitor’s improved product why they bought it and not ours. This will almost always lead to new insights into the problem and its possible solutions.
We should also survey others who may have encountered similar problems. This may involve management, employees from different parts of the organization, consultants specializing in our area of concern, or people in other organizations with whom we have a “networking” arrangement.
Resident experts are another source of background information. If resident experts are available, two pitfalls must be avoided: 1) Jumping to solutions and, 2) “We tried that before and it didn’t work.” One of the hidden perils of jumping to a solution is that it will cloud the perception of the very facts that are truly germane to the problem. While many people believe that “Seeing is believing,” it also is quite true that “Believing is seeing.” When we jump to solutions, we run the risk of coming up with a fine solution that has nothing to do with the problem at hand. To do a good job at background analysis, we must put a hold on all solutions until we have determined what the problem is, when it started, and how serious it is.
The second pitfall usually appears later in the analysis process. Once a possible solution to the problem has been identified, you may hear from your expert that “We tried the solution before, and it didn’t work.” Or, so-and-so tried it and it didn’t work. Don’t accept such comments without further investigation. First, we all have very imperfect memories and tend to remember things selectively. Second, the conditions may well be different than they were previously.
Most frequently, we will eventually identify more than one factor, or variable, as a possible solution. Here we have a choice: we can combine them and do everything we can think of that might make the problem go away; or we can tease them out and try one “fix” at a time. In many management situations we want to get the problem behind us and move on. We are not interested in what relative weights different factors have on our problem. We just want to get done with it and move on.
If we want to find out which variables do what to resolve our problem, each variable must be handled separately as a unique proximal objective. Each proximal objective represents one of the simple elements for reaching our overall objective.
Let us return to the revenue problem. We believe that an increase in advertising might be a solution to our problem.
Proximal Objective One – “We want to verify that our revenues will rise after an increase in advertising.”
Our market research showed that customers who bought our competitor’s product reported that they did so because they could color coordinate the item with their room decor at no additional cost compared to our product. This suggested that price might be another factor impacting revenues.
Proximal Objective Two – “We want to verify that our revenues will rise after we lower the price of our product by 10%.”
You will notice that neither of these proximal objectives looks at profit. If we increase our advertising, we will increase costs. If we lower price, we are decreasing contribution margin as well as revenue per unit sold. We have to be careful that we do not switch our problem focus in the middle of the process. Beware. This is easy to do, so be careful. Our problem focus here is revenue, not something else.
I want to examine the proximal objective in more detail because, if it is not stated properly, we may not be able to chose between different decisions later in the process or, even worse, we may focus our efforts on the wrong problem. First of all, the proximal objective must focus on our overall problem — declining revenues. Second, we need to state what we are comparing to what. In the example above, we state that we are going to compare revenue before and after the advertising campaign, as well as before and after a 10 percent price decrease. Any proximal objective that does not clearly cover these two points will pose major problems later in the analysis process.
Looking at each of the proximal objectives stated above, we should understand that, if we elect to do both at the same time, we would never be able to determine how much, if anything, each contributed to any rise in revenues. So we may want to vary each independent variable separately in order to determine their relative weights. This is a decision point. If we elect to do both at once, we would have a new proximal objective: “To find out if our revenues go up after we launch a new advertising campaign and reduce prices by 10 percent.” If we really would like to compare the relative impact of each proposed change we must retain both of our proximal objectives and deal with each one separately.
Let us look at one more example of the process in this step of problem analysis. Suppose that our analysis of a problem indicates that a competitor has gotten a real advantage over us in product quality and that is why our revenues are down. We really have two problems here. The first involves regaining a competitive advantage in product quality, which means changing manufacturing procedures.
Therefore, our first proximal objective is: “Verify that product quality meets our standards when new procedures are implemented.” We need to verify that what we did was successful in improving quality. Then we can proceed with the second part of the problem: “Verify that revenues have gone up since we introduced the new procedures.” If we do not do the first, we really have not confirmed that our solution worked. Given that quality has indeed improved, we can now see how it impacted sales.
When we are at a choice point and cannot decide which path to take, we need to revisit our objectives, and we may need to add another proximal objective. This is an iterative process that may have to be done repeatedly, especially when we are dealing with a complex nest of problems.
A problem is defined by our not being where we would like to be. The focus of the problem is always contained in the statement of the overall and the proximal objectives. In the world of statistics, this focus of the problem is called the dependent variable (DV). It is in the nature of dependent variables that we cannot grab them and move them to where we want them to be. Our challenge is to find something in our control that will change our DV to a satisfactory level. This something is called an Independent Variable (IV). In other words, we identify an IV upon which we believe our DV depends.
A variable can be anything that takes on different values. For example, we each represent thousands of variables including age, sex, height, net worth, marital status, serum cholesterol level, and political party. Some are “natural” DV’s and IV’s. For example, early in life, your height depends on your age rather than vice versa. Revenues depend on price, competition, demand, etc.
When we have made a clear and clean statement of our Proximal Objectives, we have really identified the Dependent Variable(s) in which we are interested and the Independent Variable(s) on which we think they might depend. We are now free to move on to other phases of the Problem Analysis process.