Effective Demand Management

In recent years, market demand management has become increasingly variable and difficult to predict.

Drawing on the insights of Nissha Metallizing Solutions’ Supply Chain Director, Stefano Fascio, this article explores how companies must be ready to capitalize on demand when it arises, as it may decrease in the short term. Customers are demanding shorter reaction times, and companies must meet these demands by optimizing production capacity, avoiding underutilization, unnecessary stock creation, or lack of production capacity.

A crucial point is aligning production capacity with demand both in the long term, to make necessary investments in machinery and personnel and manage significant variations (e.g., above 20%), and in the short to medium term, to increase or decrease capacity within more contained variations (below 15%). This alignment is important not only to follow demand trends as closely as possible but also to meet internal efficiency goals of production activities. For many years, working capital management has been one of the main objectives of supply chains and companies as a whole. On the other hand, optimizing the use of company assets has always been the basis for achieving goals.

Multinational companies with the ability to produce in multiple sites face different levels of flexibility in the use of resource in the different countries in which they operate, which can have a significant impact on response times. In some countries, increasing or decreasing the human resources involved in production can be very complex and can only be achieved over a long period of time and with significant constraints. Adding to this the technical times for training new personnel, it becomes clear how important it is to plan capacity variations, both in terms of production and raw materials, as early as possible.

Forecasting is therefore one of the key references for achieving the desired level of internal efficiency and effectiveness towards customers.

In recent years, customers have found it difficult to make reliable forecasts requests are increasingly sudden. In order to compensate for the lack of information and to ensure high service performance, Nissha Metallizing Solutions (NMS) is working to continuously improve its demand planning tools. In general, the main tools used to plan resources are as follows:

Budget
Customer forecast
Sales department forecast
Customized agreements with key customers (reservation)
Analysis of current orders
Analysis of customer behavior in fulfilling forecasts and reservations
Historical statistical analysis of sales

These tools respond differently to various aspects such as perspective (future vs. past), horizon (long term vs. short term), degree of uncertainty (high vs. low), and level of detail (high vs. low).

The budget has a 12-month time horizon and provides information about future, both of which are positive elements. However, the reliability of the data and the level of detail do not always allow for proper resource planning. For example, it is one thing is to have the total amount of product expected for a particular customer, but another to have the detail for each application. In the case of NMS, this second piece of information allows us to understand which base paper will be used and the width of the reels.

The presented tools are ordered by decreasing temporal perspective, from the budget and forecast, which are oriented towards the future, to the statistical analysis of past sales, with an exclusively past perspective. The statistical analysis of the past is, in any case a good reference especially for more foreseeable applications. All the tools are important, and each can enrich the collection of data to be worked on. None excludes the others and all should be used. To do this, it is important to understand the differences in order to get the greatest contribution from each one.

Planning, in order to optimize the use of production capacity and minimize working capital, would always like to have reliable and detailed information about the long-term future. However, this is not possible and this lack has to be compensated by approaches or strategies.

In addition to the forecasts received from customers, the information provided by the Sales Department Forecast is very useful as it is very up-to-date. The Sales Department has access to customers to obtain, if not a real forecast, at least some indication of demand trends. This information, combined with market analysis and economic and geopolitical conditions, allows for hypotheses about the future. To talk about current events present in newspapers in recent years, for example, the introduction of import duties can significantly change demand from some countries, or a conflict can significantly alter not only the political but also the economic balance.

Customized agreements with key customers are very useful because, behind the guarantee of reserved production capacity, the customer is encouraged to provide more reliable forecasts. The mechanism works if, before the planned production date, the customer commits to covering these bookings with an order. If this does not happen, the production capacity is released and made available to other customers. The agreement must include the deadline by which the order can be confirmed (normally two months), to allow time for the material to be delivered and the production schedule to be changed.

The analysis of current orders shows customer behavior following the issuance of an order. In cases where agreements do not require the withdrawal of the ordered quantity, it is useful to know the statistics for the last period, what percentage was withdrawn and from which products. This analysis provides information on customer behavior and order reliability, allowing forecast to be adjusted.

Another analysis of customer behavior, which is always used to reassess forecasts, is the one that allows evaluating what percentage of the forecast or customer bookings have turned into orders. The historical statistical analysis of sales is the classic historical analysis of sales by customer and product. With this analysis we can get much detailed information about specific item codes, sizes, seasonal trends.

Specifically, regarding NMS, we have implemented a statistical analysis model that allows us to analyze sales against forecast and monitor customer behavior over the past six months.

A number of reasons are analyzed as to why the sale has undergone a variation compared to the expected date. The reasons for this variation can be various: the customer did not collect the goods made available, there are some overdue payments, the letter of credit was not issued in time, the customer postponed or anticipated the delivery, etc.

This information allows us to review the forecast based on repeated past dynamics and can be correlated, for example, to financial and political difficulties in the country where the customer is located, the development mode of the customer's delivery plans, the customer's financial difficulties. These information can be used to re-align the expected quantities and to reduce possible risk.

As demonstrated, effectively navigating the complexities of market demand requires a comprehensive approach, integrating tools that provide both future-oriented projections and reliable historical insights. While some tools provide valuable foresight, albeit with inherent uncertainty, others offer concrete data from past performance. Therefore, it is crucial to leverage all available resources, drawing from diverse sources of information to build a holistic understanding of market dynamics. Ultimately, the Supply Chain's pivotal role lies in synthesizing these insights to make informed decisions regarding production capacity, inventory management, and raw material procurement, ensuring both efficiency and responsiveness in an ever-changing environment.