No previous post, I commented on how the use of analytics tools based on Artificial Intelligence (IA) and Machine Learning (ML) in the demand planning process could improve accuracy, but there were barriers to its adoption by companies.
In this text, I intend to list some of these challenges and ways to overcome them:
- Low maturity in the current planning process
What is it? Rudimentary and disintegrated demand planning process, where organizational areas analyze information and make decisions independently. With that, there is no room for continuous learning about the impact of commercial and trade marketing actions on demand, nor a deep knowledge of exogenous variables to be considered in planning. Success in the effective use of Advanced analytics, with AI and ML, depends on adequate information inputs and aligned decision-making processes, which only exists in mature S&OP and IBP processes.
What to do? Engaging the commercial, trade marketing and financial areas in the S&OP/IBP processes, ensuring the discussion on the impact of market actions on demand and a detailed financial view of the plans, which will allow creating an analytical and decision-making process that will benefit from the use of analytics.
- Lack of technical knowledge of the team
What is it? In many companies, the teams responsible for the sales forecasting process have only elementary knowledge about extrapolative and causal models, depending heavily on specialized tools to project demand, which results in underutilization of their potential. When we talk about AI and ML, in addition to requiring more sophisticated technical knowledge, such as programming in Python and R, it is necessary to master the factors that influence sales behavior, whether to “train” the algorithm or enable access to exogenous information through APIs (Application Programming Interface). Unfortunately, data scientists, as professionals with this knowledge are called, are still rare and highly disputed.
What to do? Start as soon as possible the training of the team in the classic sales forecasting modelsOn tools of analytics and, in parallel, “mining” some data scientists for the team.
- Lack of supply chain integration and collaboration
What is it? The relationship between commercial partners is still based on adversarial relationships, with margin disputes in business processes, which lead to protection of critical information for demand planning, such as data from sell out and inventories, as a way to maximize bargaining power in negotiations. Furthermore, the structures of the databases were built thinking more about protection than about sharing information, which requires a considerable effort to connect data that can feed algorithms of analytics. In other words, much of the desirable information is not available or not accessible in a compatible structure.
What to do? It is necessary to advance quickly in collaboration mechanisms in the supply chain, such as the CPFR, in addition to starting a deeper – and difficult – movement to rethink the data architecture, favoring the exchange of information.
And to you? What are the biggest obstacles to using Advanced analytics in demand planning? Share with us on the ILOS social networks! Big hug!
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.
