By Dr. Benoit Mario Papillon, Professor, Dept. of Finance and Economics, Business School, University of Quebec

Growth and insolvency: a surprising correlation

As the saying goes, you have to have something to sell in order to live. In many sectors of activity, this is an ongoing challenge. The first test for success of any start-up is finding takers for your product. For an existing enterprise facing competition from others who have what its clients want, the saying is a reminder of what it must do in order to survive. This explains the importance attached to sales growth as an indicator of its success and the quality of its management. The perception that globalization brings intensified competition reinforces that interpretation.

With the long-term reduction in customs tariffs and communication and transportation costs, barriers to trade have diminished. There is less protection for the relationship between an enterprise and its customers, which makes it all the more difficult to expand one’s clientele. Another factor is the creation of new businesses. Entrepreneurship and policies to support start-ups stimulate new business creation. For existing enterprises, this is a new source of competition although, as is true for globalization, it can also generate new opportunities.

One aspect of the challenge to succeed relates to the circumstances in which value is created. There is a widening range of products that facilitate substitution. Perhaps your suitcase is not large enough for the long journeys in your future. Instead of a new one, you can opt for lighter clothes that are more compact and versatile; thus the suitcase maker is indirectly in competition with the producer of high-tech clothing. Every enterprise has a specific basket of resources; the growing diversity of products suggests that its set of product characteristics and locations for value creation is less extensive. It means that, for example, reorganizations of conglomerates that are active in many sectors – GE comes to mind – will seek to narrow the field of activity.

Given the challenge of selling product in order to succeed in business, growth is a very strong indicator of sound management. Yet many businesses now dealing with insolvency have, paradoxically, experienced significant growth in the recent past. Resolving this paradox offers a more accurate reading of the causes of insolvency. Accurate diagnosis is as essential for insolvency professionals as it is for physicians, if an informed choice of treatment is to be made.

Forecasting errors and financial difficulties

Enterprises form one category of organization among many, such as social clubs. An enterprise manufactures or provides goods and services in response to client requirements. Its revenue provides an indication of the value of the needs it satisfies, and its costs indicate whether its revenue allows it to be financially viable. Revenue and costs are measured using accounting data. Accounting is the language of management, although prior to decision-making, sound management takes factors into consideration that are less easily measured: employee motivation, supplier reliability, lender patience, client expectations and so on.

The choice of means to generate growth involves predicting their effect on revenue and costs. Attractive in its simplicity, the concept of the break-even point is a popular forecasting tool. With accounting data for the recent past, average variable costs and average selling prices can be calculated. Adding fixed costs and multiplying by the number of units sold, indicated on the horizontal axis of the graph, the evolution of total costs and revenue can be shown by two lines. Moving across the graph, the line for revenue crosses the “total cost” at some point, thereby indicating the number of units at which the enterprise reaches the threshold of profitability. The reality, however, is complex, and this kind of forecasting is subject to error.

Some prices are determined by regulation, or by an agency like the Chicago Board of Trade. In that case, the line referred to above is sufficient for predicting revenue. Generally speaking, however, product pricing is an important decision-making variable. Taking buyer behaviour into account, revenue follows a curve that resembles an inverted letter “U”, which is maximized at a given volume of sales, and then falls. A frequent cause of insolvency is operating beyond that maximum. The enterprise is doubly threatened by revenue lower than it could be, and higher total costs, because it is producing and selling too many units – and fails to realize that fact. In practical terms, and in relation to the average price on which calculation of the break-even point is based, it will be obliged in many cases to offer discounts in order to achieve its growth objective. There will be other expenses in such areas as the sales force, advertising, logistics and so on. If production gets ahead of sales, inventory will pile up; the enterprise will then be encouraged to offer easier credit conditions, and recovery of accounts receivable will suffer accordingly.

In predicting costs, the constant average variable cost referred to above assumes that variable inputs have a constant output – in other words, the addition of, say, 100 hours of work always generates the same quantity of product. This was true of the cottage industries that were typical of the economies that preceded the industrial revolution of the 19th century, and remains valid in a few sectors like hairdressing salons in which each hairdresser operates autonomously, as an independent tradesperson. In general, however, there is an interaction between fixed and variable inputs. First of all, increased production supports greater specialization of tasks, thereby increasing productivity, but eventually, it has a negative effect due to physical congestion and a cognitive overload at the management level. In other words, diminishing returns are characteristic of contemporary technology. Since productivity is a determinant of average variable cost, that cost is not constant.

The frequent errors made by insolvent enterprises referred to above will be all the more disastrous for its profitability in that it is not only producing too much, but is doing so at a total cost that exceeds the predicted level. It will also be unable to identify the main cause of its losses. Management will then cast around for an explanation in such areas as employee motivation, making more decisions that will increase losses and further reduce asset value. The ability of an insolvency professional to diagnose the situation quickly and accurately can save an enterprise from liquidation, and support a return to profitability.

An enterprise experiencing growth will eventually confront overuse of its facilities. It will conclude that it is operating at 110%, and must therefore increase its production capacity. Here again the decision – in this case, an investment decision – is based on predicted results, and again, an analysis limited to historical data will overlook another important aspect of technology: economies of scale. The decision will then lean towards adding a production line, rather than a new plant, because the analysis will not weigh the transitional costs of adding a production line against the benefits of a new plant, including lower production costs. With the slightest intensification in competition, such a decision will compromise profitability.

From historical data to analytics

At the management level, specialization and international trade have led to a proliferation of coordination and control tasks. Technological innovations, in conjunction with accounting concepts, are partially responsible for contemporary economic realities, since it would be difficult to perform the tasks in question without accounting data. Historical accounting data, even accompanied by a range of statistics, are not always sufficient to support clear-sighted management. In order to take into account the effect of predictable patterns in buyer behaviour and significant characteristics of technology, it is helpful to use historical data to generate analytics, using models drawn from economic theory.