Subsessions (part two)
Crunching Big Data into actionable insights
Substantial amounts of customer data are shared with Heineken daily, but until lately the full potential of this data had not been taken advantage of. Kalle Rasmussens (Heineken) demonstrated how Heineken together with EyeOn could crunch customer Big Data and create actionable insights to drive value for both Heineken and the customer. The main learning of the session: start simply by focusing on 1 main topic. Monitor 1 KPI to reach 1 success and then ramp up to reach the full potential.
How to react to short-term changes in the high-tech equipment supply chain?
During this breakout session two members of the high-tech team shared their experiences on managing short-term demand changes. Participants from various industries in the room discussed both best and poor practices in managing these changes. What all industries have in common is the need to control short-term changes. Companies battle complexity such as the high number of SKUs and changes in general. The main takeaways: be aware of critical resources, share information of high quality, and do not merely firefight, but also reward structural improvements.
Improving forecast accuracy by understanding human behaviour in demand planning
Quantifying the impact of human behaviour on demand planning. This is what PhD student ir. Bregje van der Staak from Eindhoven Technical University is currently researching. During the session interesting aspects of this research were shared with the audience. For example, did you know that demand planners:
- have a strong preference for round numbers; 75% of all forecast enrichments have 0 as a last digit? And that this preference increases the error in forecasting significantly!
- are good in forecasting negative trends, but on average not as good in forecasting positive trends?
More valuable insights are to come since fascinating research on this topic will continue the coming 2 to
Supply Planning – using apps to analyse scenarios in the review meeting
A variety of apps answer all sorts of questions every day, except when it comes to S&OP planning processes. Although we are bound to rigid systems and upfront prepared analysis, current market dynamics require scenario thinking when planning. Despite preparation of a baseline and a scenario, there are always questions pertaining to scenarios you anticipated as well as those you didn’t consider.
Our app demo was used to demonstrate a case study concerning the impact of capacity change as well as the effects of capability specialization and demand changes when exploited, among other things. Scenarios are calculated and visualized on the spot; so that the power of scenario analysis and optimization capabilities are immediately available during a meeting. However, do be aware of the pitfall ‘analysis paralysis’. By over-analysing a situation, a decision may never be reached. And in the end, the goal of the S&OP process is to make well-informed decisions!
Getting S&OP to work; 12 building blocks for process design and implementation
A successful S&OP process is often hampered by complexity. 80% of the audience attending this PID session, evaluated their S&OP maturity no higher than Gartner level 2.
In an interactive session, EyeOn guided participants through the 12 essential building blocks to make S&OP planning work.
Within this implementation framework, the audience concluded that the top 3 challenges for making S&OP a success, are in the following areas:
- Gathering accurate supportive data and using enabling tools which integrate the input and output of processes.
- Achieving plan integration across disciplines.
- Establishing a collaborative mindset that supports decision making.
- The framework offered participants a set of guidelines on how to tackle these challenges.
Machine learning – the planner of the future? The technology behind the hype
Best-in-class machine learning technology is currently beating world champions in games such as Go and Poker. These machines can even teach themselves how to dominate Atari. Will machines replace planners? Machine learning covers an entire range of techniques, many of which are already used to produce better forecasts or optimize inventory in supply chains. While no one can say for sure how far these techniques will evolve, the best way to find out is to get started! Ironically this is very much a people business. It takes a smart data scientist to convert machine learning techniques into planning applications. So, hire data scientists to ramp up your analytical capabilities, start datafying your organization and explore possibilities in your organization on how to conquer the modern age!
From local to global APS at Janssen
During the 60-minute breakout session, Bram Bongaerts, Sr Life Science Consultant at EyeOn, handled Janssen’s (a pharmaceutical company) global APS implementation. Representatives from different life science companies were really interested and curious about the process, pitfalls, the result, as well as the next steps to be taken in such an implementation. Questions were raised concerning whether their companies are on the right track and what should be considered when starting an APS project.
ERP: Master or Monster for engineering to order companies
In the discussion to which extent ERP adds value to project planning tools, guidelines have been offered to consider, when configuring and implementing ERP. Company scale and volumes, the use of critical shared resources, and the desired detail level of project management and financial reporting impact the relation between ERP and Project management tools, for both tools overlap in their functional capabilities.
Shipbuilding companies, like Damen, Huisman, de Vries and van Lent, but also NXP Semiconductors and Voortman Steel construction joined this session.
Attendants questioned the role of master data and specifically for product data management. With whom should lie the responsibilities of data accuracy and completeness? What kind of interfaces are possible and how should these interfaces be arranged?
Driving towards optimal inventory levels
In the session “Driving towards optimal inventory levels” we discussed two examples from practice on making a change. Involvement and fact-based were the key-words. In the first case we discussed how various stakeholders need to be involved from the start in defining inventory strategy and policies. Once these are accepted, manual deviations are the exception. In the second case we discussed the importance of early consideration of inventory and customer service in strategic network optimization, even if transportation and warehousing costs are a multiple of inventory costs. Indeed, alternative scenarios may result in largely equal logistics cost while having a impact on inventory and customer service.