Before starting this new post, I anticipate that the considerations that I will make below are a particular view on concepts and practices related to Demand Driven Supply Chain (DDSC), or Demand Driven Supply Network (DDSN), a topic that has been receiving increasing attention from companies that produce so-called fast moving consumer goods, or fast-moving products such as Nestlé, Procter & Gamble, Coca-Cola, PepsiCo, Philip Morris, Unilever, AB Inbev, BAT, L'Oréal and Nokia.
A Gartner Inc., still AMR Research, defined DDSN as the “system of technologies and processes that allow capturing and reacting, in real time, to changes in demand through a network of relationships between employees, customers and suppliers”. This definition may, at first glance, be quite similar to the idea behind the Rapid Response Programs (PRR), such as the Vendor Managed Inventory (VMI) and the Continuous Replenishment Program (CRP). However, a more careful analysis reveals significant differences in their practices and results.
The Rapid Response Programs emerged in the wake of the development and improvement of electronic data exchange (EDI) mechanisms, which make it possible, at least in theory, to access information from the sell out by manufacturers, improving the accuracy of sales forecasts and, consequently, reducing costs with safety stocks. However, the transactional cost of electronically exchanging data using interchangeable language standards, such as EDIFACT, and with the intermediation of specialized companies, known as Value Added Networks (VAN), it was still too high.
In addition to the high transaction cost, the difficulty of changing adverse patterns in the relationships between industry and retail, which often make it impossible to reach an agreement on the necessary investment and the sharing of benefits, and a possible increase in the industry's operating costs, arising from consignment stocks and more frequent shipments, also contributed to limiting the use of PRR's.
However, a technological revolution has made it possible to aspire, in the not so distant future, to truly demand-driven supply chain management. There are four pillars that support this hope: new data processing technologies, drastic reduction of transactional costs, three-dimensional printing and better understanding of the gains arising from collaboration in the supply chain.
Big Data Analytics
The best information available to forecast future demand and, from there, “push” the flow of products towards the final consumer has always been information from the sell out, or actual consumption. With it, companies can use structured methods to forecast time series and estimate future demand for their products and services. With that, one can understand the enormous value of having this information available, as advocated by the PRR's. Although ILOS research shows that, unfortunately, a minority of large Brazilian companies use sophisticated sales forecasting techniques, this can be considered the “state of practice”.
However, the “state of the art” with regard to data processing to carry out “forecasts” is already at another frontier of knowledge: Big Data Analytics. Imagine being able to estimate future demand no longer with historical consumption data, but based on the analysis of the immensity of information made available on social networks such as Facebook, Pinterest, Twitter and others!?
This is information about parties, moves, weddings, births… information that, if correctly analyzed, allows you to anticipate real future demand. And there are already robot-mechanisms that allow the search and treatment of this enormity of non-numerical data. See, in Figure 1, the result of the Google Trends search for the terms Big Data Analytics e Time Series Forecasting, showing the transformation that is taking place.
Figure 1 - Big Data Analytics X Time Series Forecasting
Source: Google Trends on 30/10/2015
XML
As a second transformation factor, the drastic reduction in transaction costs arising from the development of the eXtensible Markup Language (XML), the reduction of data storage costs and the increase in the availability and speed of the internet, which allow the electronic exchange of data without the intermediation of VAN's.
With lower transactional costs, for example, information flows can be duplicated and become bidirectional, allowing the offer to be shaped in real time based on the availability of resources in the chain. Imagine the idle capacity information of a logistics operator's fleet being transmitted in real time to a retailer, which offers a reduction in freight and immediate delivery in its virtual channels for purchases in that period?!
In addition, the reduction of transaction costs allows the insertion of a huge number of small companies in the supply chain, causing a real revolution in retail, as discussed in the post on Omni-Channel Supply Chain.
3D printing
One of the difficulties of managing the supply chain in a “demand-driven” way has always been the need to work with production and movement batches to dilute the costs of Setups machinery and transport. Part of the problems with the PRR's came from the increase in operating costs for working with smaller batches, which was almost never compensated by the savings with the reduction of inventories. However, we are very close, as Joseph DeSimone points out in Video 1, of a true revolution with the development of new, more robust and faster 3D printers.
Video 1 – What if 3D printers were 100 times faster?
Source: TED Talk – Joseph DeSimone
Not only will 3D printers allow for a setup extremely low, as they will enable the elimination of long transport distances, since they can be installed closer to the customers. Other automation initiatives in the industrial and warehousing areas, such as the use of image recognition technology, have allowed for a considerable reduction in response times to the market. An example of this can be seen in Video 2.
Video 2 – Automatic Fruit Sorting
Source: Allied Vision TV – Youtube
Collaboration in the Supply Chain
Finally, there seems to be a clearer understanding of how collaborative relationships can bring mutual gains to the companies involved. To the extent that initiatives such as PRR's or other collaboration mechanisms between commercial partners, such as the Collaborative Planning, Forecasting and Replenishment (CPFR), are beginning to be implemented, it is possible to quantify the results and realize that there is another way to obtain gains other than end-of-month trade negotiations.
Unfortunately, there is still a lot to be done before the concept of Demand Driven Supply Network become a practical reality for companies. Overcoming the paradigm that gains in relationships occur exclusively in the negotiation process, built in a period where the technological tools that make efficient management of information and product flows along the supply chain did not exist, is urgent. It is already evident that the possible gains from the collaboration of companies in a demand-driven supply chain far exceed those obtained in adverse business relationships.
It is up to us to create the managerial artifacts to make this wonderful future possible!
References
<https://www.ted.com/talks/joe_desimone_what_if_3d_printing_was_25x_faster>
Leonardo Julianelli
https://ilos.com.brExecutive Partner of ILOS. Graduated in Production Engineering from EE/UFRJ, Master in Business Administration from COPPEAD/UFRJ with extension at EM Lyon, France, and PhD in Production Engineering from COPPE/UFRJ. He has several articles published in periodicals and specialized magazines, being one of the authors of the book: “Sales Forecast: Organizational Processes & Qualitative and Quantitative Methods”. His research areas are: Demand Planning, Customer Service in the Logistics Process and Operations Planning. He worked for 8 years at CEL-COPPEAD / UFRJ, helping to organize the Logistics Teaching area. In consultancy, he carried out several projects in the logistics area, such as Diagnosis and Master Plan, Sales Forecast, Inventory Management, Demand Planning and Training Plan in companies such as Abbott, Braskem, Nitriflex, Petrobras, Promon IP, Vale, Natura, Jequití, among others. As a professor, he taught classes at companies such as Coca-Cola, Souza Cruz, ThyssenKrupp, Votorantim, Carrefour, Petrobras, Vale, Via Varejo, Furukawa, Monsanto, Natura, Ambev, BR Distribuidora, ABM, International Paper, Pepsico, Boehringer, Metrô Rio , Novelis, Sony, GVT, SBF, Silimed, Bettanin, Caramuru, CSN, Libra, Schlumberger, Schneider, FCA, Boticário, Usiminas, Bayer, ESG, Kimberly Clark and Transpetro, among others.
