2017_Brochure-Giugno-Lean-EN

June 2017 

LEANFORMAZIONE 

The process of demand 

planning

Dear Colleagues 

“today we’re going to talk about demand planning …”, but what is it exactly and why 

devote an edition of Leanformazione to it? 

In this brief introduction I will attempt to answer these two questions. 

First of all, since the production area … produces, purchasing … purchases, sales … sell, 

well the demand planning department obviously plans demand! 

Every industrial company which supplies raw materials, manufactured goods or services, 

has to carry out estimations of future commercial demand. This feeds into the other 

processes of planning production and distribution, in such a way as to increase availability 

of goods without raising handling costs 

In an ever more competitive market, being the best in serving the customer turns out to 

be one of the main keys to success. But with rapidly changing trends, new distribution 

strategies and new products to market, it is getting more and more difficult to predict 

what customers want. We can say, then, that while forecasting is never easy, it is rarely 

impossible. We can’t expect exact forecasts but we can always try to increase their 

accuracy (80-90% is regarded as acceptable). 

And now I come to my response to the second question: following on from the Zero Back 

Orders project, with the separation of flows, we have embarked upon a new project which 

will lead to a new way of “doing” demand planning. 

What we are aiming for is a forecasting system where both processes and techniques are 

standardized and which is capable of predicting events, rapidly absorbing the latest data

and reacting quickly to avoid stockouts, carrying costs and organizational costs. 

This edition describes the key phases of this project, from analysis up to the first 

implementation of a prototype. 

Enjoy the read 

“According to the meteorologists the forecast was right, it was 

the weather that was wrong.” 

(Henri Tisot) 

Ilaria Del Gallo 

Demand Planning Manager

LIMA LEAN ENTERPRISE 

Our reorganizational journey to become a LIMALEAN ENTERPRISE goes on 

with one the cornerstones of the LEAN philosophy: the dissemination of a 

culture based on respect and trust, where the potential of the people who 

work inside an organization is fulfilled. 

This key principle is the driving force behind this issue, with focuses on the 

theme of Excellent People. 

VISION & 

COMMITMENT 

EXCELLENT 

PEOPLE 


BUSINESS 

PROCESS 


SUPPLY CHAIN

LEANFORMAZIONE 

ORGANIZATION OF THE 

BROCHURE 

Training pill 

introduces step by step a principle connected to one of the four areas: 

Vision & Commitment, Excellent People, Excellent Supply Chain and 

Excellent Business Process 

Pilot project 

on witch it is operationally applied what was explained in the learning pill 

Recommended reading 

with reference to the subject being discussed

LEANFORMAZIONE / 7 

CONTENTS 

8/ TRAINING PILL 

FORECASTING DEMAND 

14/ PILOT PROJECT 

IMPROVING THE ACCURACY OF 

FORECASTS 

20/ RECOMMENDED READING 

IMPLEMENTING COLLABORATIVE 

FORECASTING

TRAINING PILL: FORECASTING DEMAND 

TRAINING PILL 

FORECASTING DEMAND 

One of Abraham Lincoln’s best-known sayings goes: “If we could first know where we are 

and whither we are tending, we could then better judge what to do and how to do it”. 

There is an ever-growing need for companies to analyze and plan their operations because 

an ability to monitor and predict plays such a huge role in efficient corporate planning. 

Forecasts help us to determine production volume, define investment, manufacturing and 

supply plans, and fix correct stock levels, as well as to organize shipping and transportation. 

A crucial role in this forecasting process is played by the Demand Planning office. 

The analysis and understanding of the demand phenomenon must allow forecasts to me 

made in as timely a way as possible. But let’s start with a general consideration: 100% 

accurate forecasts are impossible because, by definition, forecasts will always contain a 

margin of error. What we need to do is to limit the error as much as we can: the more


accurately an estimation is done, the lower is the margin of error committed in producing 

the estimate. 

In order to produce a significant and reliable demand forecast, two techniques are normally 

used: 

1. Qualitative techniques: these are mainly subjective because the forecast is based on the 

data available to the Commercial/Sales area, together with their knowledge and awareness 

of the market. 

2. Quantitative techniques: these are also called ‘mathematical’ or ‘objective’ techniques 

because they use data for past demand, where this is felt to be a reliable indicator of future 

demand. 

It follows that the key to success lies in the use of both these techniques, governed by a

TRAINING PILL: FORECASTING DEMAND 

strict demand planning process, as follows: 

1. Demand Analitycs: the gathering and statistical analysis of historical market data 

2. Data Filtering: the removal of anomalous values from historical data, i.e. unusually high 

or low consumption figures produced by events which will not re-occur in the future (e.g. 

spot sales, stockout, and promotional sales) 

3. Sales Forecasting: here statistical techniques and mathematical algorithms are used to 

generate a Sales Forecast on the basis of past sales figures. 

4. Consensus o Unconstrained Forecasting: a unconstrained forecast looks at the true 

demand potential and does not take into consideration constraints of capacity, distribution, 

etc. This type of forecast is produced by combining commercial actions inspired by the Sales 

and Marketing department’s experience and knowledge of the market, with Sales Forecasts


generated by statistical models. 

5. Demand Plan: the final result of the Demand Planning process is the Demand Plan. 

This does not necessarily coincide with the Consensus or Unconstrained Forecast because 

it also incorporates our awareness of production and/or distribution limitations, which, in 

certain circumstances, might not be sufficient to satisfy 100% of the forecast demand. The 

Demand Plan is therefore used to make and share decisions with the Supply Planning, 

Warehousing and Distribution, Purchasing and Production areas so as to balance supply and 

demand and thus guarantee the availability of raw materials and semi-finished goods, the 

actual manufacturing of finished products and their delivery to the customer in the quantities 

required and in the time agreed. In this way the Demand Plan transforms the Unconstrained 

Forecast into a workable plan.

TRAINING PILL: FORECASTING DEMAND


The process which governs both the forecasting activities perform in co-operation with the 

sales staff, and the sharing and alignment with the logistics/production department is called 

S&OP (Sales and Operations Planning). This process is of fundamental importance if all 

these areas of expertise are to be able to work together systematically and consistently, 

within a clear and unambiguous framework in which each player has well-defined roles 

and responsibilities. Regular formal meetings and liaison are needed in order to formulate 

and share the so-called “One Number”, a single, unambiguous view, which is shared by all 

departments, of expectations for the future. This will allow us to put into place a coherent 

and co-ordinated plan of action and therefore fully satisfy the customer’s needs, while at the 

same time optimizing the use of company resources. 

In conclusion, companies have to work on the one hand on the introduction of appropriate 

mathematical-statistical models and rigorous systems in order to optimize the forecasting 

process, and on the other on the development of internal co-operation and communication 

procedures so as to facilitate the execution of the strategy by means of shared decisionmaking 

processes. In this sense collaboration with the sales department acquires even 

greater importance.

PILOT PROJECT: IMPROVING THE ACCURACY OF FORECASTS 

PILOT PROJECT 

IMPROVING THE ACCURACY OF 

FORECASTS 

In the second half of 2016 Lima started an analysis of the strategic, organizational, operational and cultural 

background to the process of demand planning and, by extension, also of materials management right 

through the logistics-distribution network, according to the level of service required by customers and 

available production capacity. 

The project was subdivided into two phases: the first involved mapping the current demand planning 

process (As-Is analysis), while the second consisted of the definition of a future model (To-Be) of the same. 

A KPI was also defined, so as to be able to verify the effectiveness of the project. This indicator is called the 

WMAPE (Weighted Mean Absolute Percent Error) which measures the total forecast error as an average 

weighted according to the deviation between real and the forecast sales volumes. 

Below we can see how the two phrases developed: 

As-Is analysis 

The main aim of this phase was to map all processes associated with demand planning, define


organizational roles and responsibilities, and get a feel for the “field of play”. 

This analysis confirmed that demand has to follow the subdivision of flows already provided for in the Zero 

Back Order project (Leanformazione, December 2016) and should therefore have two cores: 

Consumption: this represents the component which forecasts sales volume (what will be implanted). If we 

analyze the historical sales data, certain trends are observed. 

Investments: implants and instrument sets forecasted in this component have to be produced, but they 

will not be sold, because they are used for getting any new business opportunity (e.g. to acquire new 

customers and hospitals). In this case the analysis of historical data revealed no trends or repeatability. 

Below is a description of the project which deals with consumption and we will start by looking at how


historical data can help us to understand possible future sales. 

To get to grips with this we analyzed the volume sold (consumption) in the last three years, according to 

two factors: 

Sporadicity, which measures sales frequency (is an item implanted every month or only once a year?) 

Variability, which measures whether monthly sales volume stays at more or less the same level or whether 

there is significant variability from one month to another. 

If we put these two components together we can see that the sales of Lima products are distributed as 

follows: 

VARIABILITY 

VARIABLE 

SPORADIC 

2 

4 

VARIABILITY 

Coefficient of Variation: 

measures dispersion of sales volumes 

with respect to the average value 

(arithmetic mean). 

REGULAR 

1 

3 

INTERMITTENT 

SPORADICITY 

Density of null values: 

measures the density of consumption in a historical series 

SPORADICITY


Only 7% of products, meaning groups of similar items in different sizes, show a regular trend (green 

bubbles) and so are easily predictable from the previous months’ sales: for these items, therefore, we can 

apply quantitative techniques, based on historical data, to forecast the future. 

A large proportion (41%) of our products are only partially predictable in that they show a variable trend 

(blue bubbles). 34% are defined as “sporadic” (red bubbles) and 19% as intermittent (yellow bubbles). For 

all these three categories we can make forecasts by means of a quantitative technique, using a statistical 

model, for example, which is based on past sales, but this does not always provide satisfactory data. Only 

by combining this technique with qualitative data based on information from and the market awareness of 

the Sales staff, we can obtain a more accurate forecast of future consumption. 

The graph above, which represents the whole Lima group, may vary from country to country: the same 

product might include different components according to country or distributional channel. 

Future To-Be model 

Using the data generated by our analysis we have decided to implement a process of consumption 

forecasting which allows us to combine qualitative and quantitative data. Our future model will use both 

statistical algorithms, which will provide a basis for a forecast of consumption from historical data, and the 

contribution from our Sales department, who will bring their market expertise to bear on our forecasts. The 

final goal is to obtain a consensus forecast for each subsidiary, since that is the only way to adequately 

integrate market data.


To test our new model we have created three prototype environments on three pilot subsidiaries in which 

past sales are analyzed (Demand Analytics and Data Filtering) and statistical algorithms are applied in 

order to obtain an initial estimate of future demand (Sales Forecasting). At this point the demand planner 

analyzes the results obtained, adjusting them if necessary and passing them to the subsidiary in question, 

who will confirm and/or change the proposed figures on the basis of their market knowledge and strategy 

(Consensus Forecast). 

In order to verify the validity of this process, we compared the accuracy (1-WMAPE) of the forecast 

obtained by comparing actual sales with the budget, Sales Forecast and Consensus Forecast. The total 

accuracy for the three countries obtained by quantitative statistical methods amounted to 85%, which is 

better by six percentage points than the budget data. The accuracy increases even further to 89% if we 

incorporate information coming from the market.


The initial results obtained from the prototype test confirm that the use of a combination of quantitative and 

qualitative techniques, or consensus forecast, can improve the accuracy of the demand forecast plan by 

several percentage points, edging us ever closer to the unreachable 100% accuracy. 

Increasing the accuracy of forecasts means that we can work on two complementary factors: one affects 

the capital and the other the profits. Better estimates of what is going to happen means that we can spend 

less on maintaining stock in the warehouse against an uncertain future. This means both lower costs and a 

better return on invested capital. Improved forecasting accuracy also allows us to have product available, 

in the right place and at the right time, allowing us to avoid potential loss of sales, with urgent and costly 

movement of goods on the field, decreasing profit margins. The combination of higher sales and less 

erosion of margins again result in an improved return on investment. 

We are still continuously refining the prototype, checking all aspects of its implementation, without 

neglecting the importance of the KPI (WMAPE). The final goal is the implementation of a new system of 

demand planning, involving both software and process, in all countries by the end of the year.

RECOMMENDED READING: IMPLEMENTING COLLABORATIVE FORECASTING 

RECOMMENDED READING 

IMPLEMENTING COLLABORATIVE 

FORECASTING 

Fashion exists, even in management disciplines. 

In the past, many company executives thought that sales 

forecasts were rendered impossible by sales campaigns 

and specific promotions; others felt that, because they 

were so difficult to do, they would inevitably be wrong. And 

there were others still who believed that with a bit of extra 

stock, flexible plant and amenable suppliers, and with 

staff working overtime, that forecasting was redundant. 

More recently though, we have started again talking about 

demand planning (what used to be called simply “sales 

forecasts”) as a key element in the logistical organization 

of a company.


If you go to \\master19\data\Lean_Libri you will find an article which explains, in simple terms but 

in considerable detail, the five key processes in Demand Planning, and their inconsistent application 

in industrial and commercial companies operating in European supply chains: on the whole the larger 

companies use historical sales data and mathematical-statistical techniques to forecast demand, 

but only where an interfunctional organizational culture has taken root, do proper processes of 

Collaborative Forecasting really exist.

“Prediction is very difficult, 

especially about the future.” 

(Niels Bohr)

Limacorporate spa 

Via Nazionale, 52 - 33038 Villanova di San Daniele - Udine - Italy 

Tel.: +39 0432 945511 - Fax: +39 0432 945512 

E-mail: info@limacorporate.com 

www.limacorporate.com 

June 2017

2017_Brochure-Giugno-Lean-EN

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