2017_Brochure-Giugno-Lean-EN
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June <strong>2017</strong><br />
LEANFORMAZIONE<br />
The process of demand<br />
planning
Dear Colleagues<br />
“today we’re going to talk about demand planning …”, but what is it exactly and why<br />
devote an edition of <strong>Lean</strong>formazione to it?<br />
In this brief introduction I will attempt to answer these two questions.<br />
First of all, since the production area … produces, purchasing … purchases, sales … sell,<br />
well the demand planning department obviously plans demand!<br />
Every industrial company which supplies raw materials, manufactured goods or services,<br />
has to carry out estimations of future commercial demand. This feeds into the other<br />
processes of planning production and distribution, in such a way as to increase availability<br />
of goods without raising handling costs<br />
In an ever more competitive market, being the best in serving the customer turns out to<br />
be one of the main keys to success. But with rapidly changing trends, new distribution<br />
strategies and new products to market, it is getting more and more difficult to predict<br />
what customers want. We can say, then, that while forecasting is never easy, it is rarely<br />
impossible. We can’t expect exact forecasts but we can always try to increase their<br />
accuracy (80-90% is regarded as acceptable).<br />
And now I come to my response to the second question: following on from the Zero Back<br />
Orders project, with the separation of flows, we have embarked upon a new project which<br />
will lead to a new way of “doing” demand planning.<br />
What we are aiming for is a forecasting system where both processes and techniques are<br />
standardized and which is capable of predicting events, rapidly absorbing the latest data
and reacting quickly to avoid stockouts, carrying costs and organizational costs.<br />
This edition describes the key phases of this project, from analysis up to the first<br />
implementation of a prototype.<br />
Enjoy the read<br />
“According to the meteorologists the forecast was right, it was<br />
the weather that was wrong.”<br />
(Henri Tisot)<br />
Ilaria Del Gallo<br />
Demand Planning Manager
LIMA LEAN <strong>EN</strong>TERPRISE<br />
Our reorganizational journey to become a LIMALEAN <strong>EN</strong>TERPRISE goes on<br />
with one the cornerstones of the LEAN philosophy: the dissemination of a<br />
culture based on respect and trust, where the potential of the people who<br />
work inside an organization is fulfilled.<br />
This key principle is the driving force behind this issue, with focuses on the<br />
theme of Excellent People.<br />
VISION &<br />
COMMITM<strong>EN</strong>T<br />
EXCELL<strong>EN</strong>T<br />
PEOPLE<br />
EXCELL<strong>EN</strong>T<br />
BUSINESS<br />
PROCESS<br />
EXCELL<strong>EN</strong>T<br />
SUPPLY CHAIN
LEANFORMAZIONE<br />
ORGANIZATION OF THE<br />
BROCHURE<br />
Training pill<br />
introduces step by step a principle connected to one of the four areas:<br />
Vision & Commitment, Excellent People, Excellent Supply Chain and<br />
Excellent Business Process<br />
Pilot project<br />
on witch it is operationally applied what was explained in the learning pill<br />
Recommended reading<br />
with reference to the subject being discussed
LEANFORMAZIONE / 7<br />
CONT<strong>EN</strong>TS<br />
8/ TRAINING PILL<br />
FORECASTING DEMAND<br />
14/ PILOT PROJECT<br />
IMPROVING THE ACCURACY OF<br />
FORECASTS<br />
20/ RECOMM<strong>EN</strong>DED READING<br />
IMPLEM<strong>EN</strong>TING COLLABORATIVE<br />
FORECASTING
TRAINING PILL: FORECASTING DEMAND<br />
TRAINING PILL<br />
FORECASTING DEMAND<br />
One of Abraham Lincoln’s best-known sayings goes: “If we could first know where we are<br />
and whither we are tending, we could then better judge what to do and how to do it”.<br />
There is an ever-growing need for companies to analyze and plan their operations because<br />
an ability to monitor and predict plays such a huge role in efficient corporate planning.<br />
Forecasts help us to determine production volume, define investment, manufacturing and<br />
supply plans, and fix correct stock levels, as well as to organize shipping and transportation.<br />
A crucial role in this forecasting process is played by the Demand Planning office.<br />
The analysis and understanding of the demand phenomenon must allow forecasts to me<br />
made in as timely a way as possible. But let’s start with a general consideration: 100%<br />
accurate forecasts are impossible because, by definition, forecasts will always contain a<br />
margin of error. What we need to do is to limit the error as much as we can: the more
LEANFORMAZIONE / 9<br />
accurately an estimation is done, the lower is the margin of error committed in producing<br />
the estimate.<br />
In order to produce a significant and reliable demand forecast, two techniques are normally<br />
used:<br />
1. Qualitative techniques: these are mainly subjective because the forecast is based on the<br />
data available to the Commercial/Sales area, together with their knowledge and awareness<br />
of the market.<br />
2. Quantitative techniques: these are also called ‘mathematical’ or ‘objective’ techniques<br />
because they use data for past demand, where this is felt to be a reliable indicator of future<br />
demand.<br />
It follows that the key to success lies in the use of both these techniques, governed by a
TRAINING PILL: FORECASTING DEMAND<br />
strict demand planning process, as follows:<br />
1. Demand Analitycs: the gathering and statistical analysis of historical market data<br />
2. Data Filtering: the removal of anomalous values from historical data, i.e. unusually high<br />
or low consumption figures produced by events which will not re-occur in the future (e.g.<br />
spot sales, stockout, and promotional sales)<br />
3. Sales Forecasting: here statistical techniques and mathematical algorithms are used to<br />
generate a Sales Forecast on the basis of past sales figures.<br />
4. Consensus o Unconstrained Forecasting: a unconstrained forecast looks at the true<br />
demand potential and does not take into consideration constraints of capacity, distribution,<br />
etc. This type of forecast is produced by combining commercial actions inspired by the Sales<br />
and Marketing department’s experience and knowledge of the market, with Sales Forecasts
LEANFORMAZIONE / 11<br />
generated by statistical models.<br />
5. Demand Plan: the final result of the Demand Planning process is the Demand Plan.<br />
This does not necessarily coincide with the Consensus or Unconstrained Forecast because<br />
it also incorporates our awareness of production and/or distribution limitations, which, in<br />
certain circumstances, might not be sufficient to satisfy 100% of the forecast demand. The<br />
Demand Plan is therefore used to make and share decisions with the Supply Planning,<br />
Warehousing and Distribution, Purchasing and Production areas so as to balance supply and<br />
demand and thus guarantee the availability of raw materials and semi-finished goods, the<br />
actual manufacturing of finished products and their delivery to the customer in the quantities<br />
required and in the time agreed. In this way the Demand Plan transforms the Unconstrained<br />
Forecast into a workable plan.
TRAINING PILL: FORECASTING DEMAND
LEANFORMAZIONE / 13<br />
The process which governs both the forecasting activities perform in co-operation with the<br />
sales staff, and the sharing and alignment with the logistics/production department is called<br />
S&OP (Sales and Operations Planning). This process is of fundamental importance if all<br />
these areas of expertise are to be able to work together systematically and consistently,<br />
within a clear and unambiguous framework in which each player has well-defined roles<br />
and responsibilities. Regular formal meetings and liaison are needed in order to formulate<br />
and share the so-called “One Number”, a single, unambiguous view, which is shared by all<br />
departments, of expectations for the future. This will allow us to put into place a coherent<br />
and co-ordinated plan of action and therefore fully satisfy the customer’s needs, while at the<br />
same time optimizing the use of company resources.<br />
In conclusion, companies have to work on the one hand on the introduction of appropriate<br />
mathematical-statistical models and rigorous systems in order to optimize the forecasting<br />
process, and on the other on the development of internal co-operation and communication<br />
procedures so as to facilitate the execution of the strategy by means of shared decisionmaking<br />
processes. In this sense collaboration with the sales department acquires even<br />
greater importance.
PILOT PROJECT: IMPROVING THE ACCURACY OF FORECASTS<br />
PILOT PROJECT<br />
IMPROVING THE ACCURACY OF<br />
FORECASTS<br />
In the second half of 2016 Lima started an analysis of the strategic, organizational, operational and cultural<br />
background to the process of demand planning and, by extension, also of materials management right<br />
through the logistics-distribution network, according to the level of service required by customers and<br />
available production capacity.<br />
The project was subdivided into two phases: the first involved mapping the current demand planning<br />
process (As-Is analysis), while the second consisted of the definition of a future model (To-Be) of the same.<br />
A KPI was also defined, so as to be able to verify the effectiveness of the project. This indicator is called the<br />
WMAPE (Weighted Mean Absolute Percent Error) which measures the total forecast error as an average<br />
weighted according to the deviation between real and the forecast sales volumes.<br />
Below we can see how the two phrases developed:<br />
As-Is analysis<br />
The main aim of this phase was to map all processes associated with demand planning, define
LEANFORMAZIONE / 15<br />
organizational roles and responsibilities, and get a feel for the “field of play”.<br />
This analysis confirmed that demand has to follow the subdivision of flows already provided for in the Zero<br />
Back Order project (<strong>Lean</strong>formazione, December 2016) and should therefore have two cores:<br />
Consumption: this represents the component which forecasts sales volume (what will be implanted). If we<br />
analyze the historical sales data, certain trends are observed.<br />
Investments: implants and instrument sets forecasted in this component have to be produced, but they<br />
will not be sold, because they are used for getting any new business opportunity (e.g. to acquire new<br />
customers and hospitals). In this case the analysis of historical data revealed no trends or repeatability.<br />
Below is a description of the project which deals with consumption and we will start by looking at how
PILOT PROJECT: IMPROVING THE ACCURACY OF FORECASTS<br />
historical data can help us to understand possible future sales.<br />
To get to grips with this we analyzed the volume sold (consumption) in the last three years, according to<br />
two factors:<br />
Sporadicity, which measures sales frequency (is an item implanted every month or only once a year?)<br />
Variability, which measures whether monthly sales volume stays at more or less the same level or whether<br />
there is significant variability from one month to another.<br />
If we put these two components together we can see that the sales of Lima products are distributed as<br />
follows:<br />
VARIABILITY<br />
VARIABLE<br />
SPORADIC<br />
2<br />
4<br />
VARIABILITY<br />
Coefficient of Variation:<br />
measures dispersion of sales volumes<br />
with respect to the average value<br />
(arithmetic mean).<br />
REGULAR<br />
1<br />
3<br />
INTERMITT<strong>EN</strong>T<br />
SPORADICITY<br />
Density of null values:<br />
measures the density of consumption in a historical series<br />
SPORADICITY
LEANFORMAZIONE / 17<br />
Only 7% of products, meaning groups of similar items in different sizes, show a regular trend (green<br />
bubbles) and so are easily predictable from the previous months’ sales: for these items, therefore, we can<br />
apply quantitative techniques, based on historical data, to forecast the future.<br />
A large proportion (41%) of our products are only partially predictable in that they show a variable trend<br />
(blue bubbles). 34% are defined as “sporadic” (red bubbles) and 19% as intermittent (yellow bubbles). For<br />
all these three categories we can make forecasts by means of a quantitative technique, using a statistical<br />
model, for example, which is based on past sales, but this does not always provide satisfactory data. Only<br />
by combining this technique with qualitative data based on information from and the market awareness of<br />
the Sales staff, we can obtain a more accurate forecast of future consumption.<br />
The graph above, which represents the whole Lima group, may vary from country to country: the same<br />
product might include different components according to country or distributional channel.<br />
Future To-Be model<br />
Using the data generated by our analysis we have decided to implement a process of consumption<br />
forecasting which allows us to combine qualitative and quantitative data. Our future model will use both<br />
statistical algorithms, which will provide a basis for a forecast of consumption from historical data, and the<br />
contribution from our Sales department, who will bring their market expertise to bear on our forecasts. The<br />
final goal is to obtain a consensus forecast for each subsidiary, since that is the only way to adequately<br />
integrate market data.
PILOT PROJECT: IMPROVING THE ACCURACY OF FORECASTS<br />
To test our new model we have created three prototype environments on three pilot subsidiaries in which<br />
past sales are analyzed (Demand Analytics and Data Filtering) and statistical algorithms are applied in<br />
order to obtain an initial estimate of future demand (Sales Forecasting). At this point the demand planner<br />
analyzes the results obtained, adjusting them if necessary and passing them to the subsidiary in question,<br />
who will confirm and/or change the proposed figures on the basis of their market knowledge and strategy<br />
(Consensus Forecast).<br />
In order to verify the validity of this process, we compared the accuracy (1-WMAPE) of the forecast<br />
obtained by comparing actual sales with the budget, Sales Forecast and Consensus Forecast. The total<br />
accuracy for the three countries obtained by quantitative statistical methods amounted to 85%, which is<br />
better by six percentage points than the budget data. The accuracy increases even further to 89% if we<br />
incorporate information coming from the market.
LEANFORMAZIONE / 19<br />
The initial results obtained from the prototype test confirm that the use of a combination of quantitative and<br />
qualitative techniques, or consensus forecast, can improve the accuracy of the demand forecast plan by<br />
several percentage points, edging us ever closer to the unreachable 100% accuracy.<br />
Increasing the accuracy of forecasts means that we can work on two complementary factors: one affects<br />
the capital and the other the profits. Better estimates of what is going to happen means that we can spend<br />
less on maintaining stock in the warehouse against an uncertain future. This means both lower costs and a<br />
better return on invested capital. Improved forecasting accuracy also allows us to have product available,<br />
in the right place and at the right time, allowing us to avoid potential loss of sales, with urgent and costly<br />
movement of goods on the field, decreasing profit margins. The combination of higher sales and less<br />
erosion of margins again result in an improved return on investment.<br />
We are still continuously refining the prototype, checking all aspects of its implementation, without<br />
neglecting the importance of the KPI (WMAPE). The final goal is the implementation of a new system of<br />
demand planning, involving both software and process, in all countries by the end of the year.
RECOMM<strong>EN</strong>DED READING: IMPLEM<strong>EN</strong>TING COLLABORATIVE FORECASTING<br />
RECOMM<strong>EN</strong>DED READING<br />
IMPLEM<strong>EN</strong>TING COLLABORATIVE<br />
FORECASTING<br />
Fashion exists, even in management disciplines.<br />
In the past, many company executives thought that sales<br />
forecasts were rendered impossible by sales campaigns<br />
and specific promotions; others felt that, because they<br />
were so difficult to do, they would inevitably be wrong. And<br />
there were others still who believed that with a bit of extra<br />
stock, flexible plant and amenable suppliers, and with<br />
staff working overtime, that forecasting was redundant.<br />
More recently though, we have started again talking about<br />
demand planning (what used to be called simply “sales<br />
forecasts”) as a key element in the logistical organization<br />
of a company.
LEANFORMAZIONE / 21<br />
If you go to \\master19\data\<strong>Lean</strong>_Libri you will find an article which explains, in simple terms but<br />
in considerable detail, the five key processes in Demand Planning, and their inconsistent application<br />
in industrial and commercial companies operating in European supply chains: on the whole the larger<br />
companies use historical sales data and mathematical-statistical techniques to forecast demand,<br />
but only where an interfunctional organizational culture has taken root, do proper processes of<br />
Collaborative Forecasting really exist.
“Prediction is very difficult,<br />
especially about the future.”<br />
(Niels Bohr)
Limacorporate spa<br />
Via Nazionale, 52 - 33038 Villanova di San Daniele - Udine - Italy<br />
Tel.: +39 0432 945511 - Fax: +39 0432 945512<br />
E-mail: info@limacorporate.com<br />
www.limacorporate.com<br />
June <strong>2017</strong>