**The last three briefings have gone into the issue of **** interdependence between resources ***, and have brought us to the point where we now have the essential elements needed to describe an entire business and its performance…*

*What are they?*

The essential elements needed to describe an entire business and its performance are:

- performance depends on resource-levels
- resources accumulate through in- and out-flows
- those flow-rates depend on existing resource levels, decisions and outside factors

When we put together a complex business with several resources, this will get quite complex, so we will start with what is about the simplest example imaginable. Even so, the story is quite involved, so this briefing is longer than most.

The case concerns a consumer brand, for which, there are just two interdependent resources – consumers and stores. These are both on the ‘*demand*’ side of the business, so those on the supply side are being simplified out of the picture. Winning stores depends on a sales force (*a third resource*), but we assume we can just raise or lower this number at will. All the production and distribution issues are being left out, along with intangibles like the brand’s reputation. We simply assume the brand is appealing enough to its target consumers.

The initial numbers for the three resources are:

- consumers, of whom there are 3 million potentially available
- retail stores, of which there are 20000 potentially available
- the sales force, which starts at zero

Management has three controls over this situation:

- advertizing spend, to capture consumers’ interest
- sales force hiring
- wholesale price (
*i.e. the price charged to stores, who add a percentage mark-up to set the retail price to consumers*)

After four years of sales and marketing efforts from its initial launch, the brand’s sales and profit statement is as shown in the following *table* and *figure 1.*

SALES AND PROFIT STATEMENT FOR A CONSUMER BRAND |
||

Consumers interested (000) | 1,448 | |

Consumption per person | 0.08 | units/month (constant) |

Sales force | 50 | |

Stores stocking the brand | 5682 | |

Product availability | 0.60 | fraction of consumers able to find the product |

Sales volume | 691 | 000 units per month |

$000/month |
||

Sales revenue | 6,220 | . . . at a wholesale price of $9.00 |

Product cost | (4,838) | . . . at a unit cost of $7.00 |

Gross profit | 1,332 | |

Advertizing spend | (500) | |

Salesforce cost | (250) | . . . at $5000 per person per month |

Brand profit | 632 | |

Rounding causes small errors |

Constant advertizing of $500,000 per month, and sales effort that built up over the first two years, were unable to drive sales growth quickly enough to generate gross profits that would recover those sales and advertizing costs until well into the third year. Even after this breakeven point was reached, sales growth slowed and by month 48 is nearly flat. It will take some years more before the losses of the product’s launch period are recovered.

** Figure 1:** A consumer brand’s performance after 4 years

Why did this disappointing result arise? The key resources grew too slowly

** Figure 2:** Slow growth of consumers and stores.

These resources are strongly interdependent – more consumers wanting the brand should help win stores quickly, and more visibility of the product in stores increases consumers’ interest in it. So why the slow growth? Like any system, this one needs some ‘push’ to get it started – it looks like this system just did not get enough of an early push.

Too little advertizing did not develop the number of interested consumers …

** Figure 3: **Drivers of growth in the number of consumers

… and too little sales effort grew the number of stores stocking the brand too slowly.

** Figure 4:** Drivers of growth in stores stocking the brand

Note that sales people soon have to spend time looking after stores they already won, so do not have time to capture new ones.

Space does not allow a more extensive picture of the architecture to be shown or discussed, but you will find the model explained in detail on *pages 192-202* of the text book.

**Until next time…**

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**“ Look-up” relationships**

Throughout the briefings we have emphasized that the causal links between variables have a specific, strong meaning – that if **A** and **B** are linked into **C**, then it means **C** can be calculated or estimated if we know the value of **A** and **B**.

Note the phrase “ *… or estimated …*” – the real world is not so helpful to us that every relationship between factors can be easily and precisely described with a simple arithmetic relationship like “*revenue = sales * price*“. Luckily it is often possible to quite accurately estimate one variable’s values if we know another.

In this case, for example, the rate at which consumers are won depends (*amongst other things*) on how widely the product is available. Put simply, the more stores stock the brand, the larger the fraction of potential consumers who see it and may thus be added to the number who are interested. Now there is no “*formula*” for working out how that availability depends on the number of stores, such as “*availability = number of stores stocking the brand / total stores*“.

But we can nevertheless estimate that relationship quite well. The first 1,000 stores (*5% of the total*) will likely be larger than average and reach higher numbers of consumers, so availability might be 10% if that is all the stores we have won. The next 1,000 may reach somewhat fewer consumers on average, so at 2,000 stores availability may be 18%, and so on. You can see in *figure 3* that 5682 stores (28%) reach about 60% of consumers.

This is an example of a “*look-up function*”, so-called because, if we know X wee can look up a value for Y. Relationships like this are frequently useful for describing relationships that are not known arithmetically, but are nevertheless well understood by management. They can therefore be very helpful for arriving at agreement about what is going on in situations where people have differing views. You can even go on to put different assumptions into your models to see what happens if one or other views is correct.

This briefing summarises discussion from chapter 4 of **Strategic Management Dynamics**, pages 192-202

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