Feb 9, 2010 | Change, Management, OE, Operations, Sales
For a long time as a consultant, who has done a fair but of sales training in a B2B environment, I have fallen back on a foundation proposition made up of three parts.
When planning a sales strategy to sell a product that is not a cheap disposable commodity (like paper clips)to a customer, you can only really do three things:
- Assist the customer increase his sales
- Assist the customer reduce his costs
- Assist the customer increase the productivity of his assets.
If the product you are selling does not address at least one of these three parameters, why would someone buy from you?
Recently, undertaking an improvement exercise for a manufacturing client, it became clear the same three questions can be applied to any improvement process, not just sales.
If any activity, policy, assumption, or behavioral norm does not contribute to at least one of these three outcomes for your organization why are you still doing it? “How does that contribute to…..?” becomes a very powerful question.
Jan 28, 2010 | Change, Management, OE, Operations
Scientific method calls for experimentation where you vary one variable at a time, observe the effect, making further changes only after consideration of the cause and effect relationships in the first experiment are understood.
Unfortunately, this is the opposite approach unwittingly adopted by many improvement initiatives, where there is a brainstorming session to identify “improvement opportunities” which are listed, prioritised, and implemented.
In the event of any improvement happening, we cannot tell which of the changed variables drove it, indeed, you may have good ideas in the mix whose positive impact is masked by the poor ideas and their outcomes.
One at a time takes more time, but not only offers the certainty of a positive outcome, it also educates you on the reasons why improvement has occurred, which can only benefit the ongoing process.
Jan 20, 2010 | Change, OE, Operations
It is generally accepted that Henry Ford was the first to automate production along a line, dramatically increasing productivity and reducing costs as a result.
Not the case.
There were remarkable instances of mass production much earlier, the most interesting and perhaps least known is the Venetian Arsenal, in the 1300’s, as well as Guttenbergs printing presses, and Springfield rifle manufacturing in Virginia in the 1860’s. All are well documented, classic examples of production line techniques, specifically, one-piece flow, that emerged well before Henry arrived to take the credit.
Shows again, that there are very few genuinely new ideas, and there is much to be gained by understanding where we came from, as well as where we are going.
Jan 17, 2010 | Management, OE, Operations
Machine utilisation and machine efficiency are probably the most commonly used KPI’s used to measure the performance of factory management. Both serve a purpose, but they do not by any means describe the “whole”.
The factor that completes the picture is “flow”, the state where product “flows” uninterrupted from one process to another, at a rate dictated by demand from the market.
Most factory managers know instinctively, if not by data, that their factories run best when there is uninterrupted flow through the processes, but if they are measured on machine efficiency, (production units/time) as they often are, they will be pushed to maximise the efficiency of individual machine points, building up inventory elsewhere, and interrupting the flow, and compromising the productivity of the factory.
The measurement of efficiency of individual points of a production process is ingrained, it is a fundamental part of the cost accounting and investment disciplines we all take for granted, but badly needs to be re-thought and taught to emerging operations and general management.
Jan 4, 2010 | Change, Management, OE, Operations
Over many years, the best marketers I have come across have been trained as scientists, in a wide range of disciplines, many had no formal marketing training.
Took me a long time to figure it out, the scientifically trained people had as a part of their automatic response, a systematic process of collecting data, forming a hypothesis based on the data, testing it and looking for inconsistencies in the results, then forming a further hypothesis based on the better data to test. Kaizen or “continuous improvement” by another name.
It was an automatic, built in response that works really well in a marketing environment, particularly where many marketing people are inclined to see a problem and jump straight to a conclusion based on what has worked in the past, rather than a detailed examination of the root causes of the problem.
As I write this post, I am reflecting on the role of the “automatic” response being one that seeks to understand the cause and effect relationships underlying a problem, and how little we know about how to make our businesses embrace it across all functions and all challenges.
That would lead to systemic Kaizen, and should prove to be a potent competitive tool.
Nov 22, 2009 | Demand chains, Management, Operations, Sales
If you want a prediction, go to the lady in the tent at the local fair.
If you want a forecast, talk to those who have an intimate knowledge of the drivers of the outcomes you are seeking to forecast.
Good forecasting is an iterative process, the more you do, the better you get, so long as you understand why the forecast is (almost) never right on each occasion it is done. Continuous improvement techniques are the core functions of good forecasting.
Forecasts are also improved when you leave aside some of the algorithms that manipulate the past into a forecast, and look instead at the drivers of demand, sometimes a qualitative input, to get a better picture of the sales that may come along. If you are selling ice-blocks, it is useful to look out the window to see how hot it may be, and factor that into forecasts, not just rely on sales over the last few weeks.
