How Manufacturers Can Simply Use Data and Analytics
The business world is abuzz about Big Data and Analytics. Everywhere you turn there is a hot new application being discussed, from customer-facing insights all the way to support functions. Why all this attention, and why should manufacturers care?
Analytics, applied on Big and Small Data, can help companies become more agile and resourceful to improve top and bottom lines alike. Manufacturers in particular ultimately have a lot to gain from Big Data in its truest sense – the Internet of Things. Sensors on products and equipment continuously throwing off real-time signals can be used to predict quality issues, increase efficiency, and engage directly with end customers. Just imagine the better decisions you could make using real-time data from connected vehicle components, social media, spoilage-triggered RFID tags and the like!
Data and Analytics for Manufacturers
Returning to the here and now, better using Small Data, which is mostly transactional and results data (e.g. sales orders, financial results, lead times, production rates, time reports, etc.) with slightly more advanced analytics, can generate significant returns for manufacturers. Applications such as statistical forecasting to reduce error or dynamic finite scheduling to reduce waste in an uncertain environment lead directly to improved working capital efficiency…and they don’t require armies of data scientists or expensive technology to accomplish.
In our experience with growth manufacturers, Small Data analytics applications are not only valuable, but easier to implement and a great place to start. They also force cross-functional collaboration, provide a basis for data-driven decision-making, and allow data analysts/scientists to gain experience in using data they are comfortable with so they can focus on building their capabilities. The figure below illustrates some of the most common, and some emerging, Data and Analytics applications for manufacturers, plotted generally for level of value against ease of implementation. The highest value tools/processes that are the easiest to implement are “quick wins,” while the more difficult yet high value tools/processes should be taken on as longer term initiatives once the quick wins have been successfully adopted. “Quick” is not overnight, and “adoption” is more than merely completing a model. “Adopted” means incorporated into decision-making processes and actually used.
Note that marketing optimization and customer-related applications would be higher for manufacturers with branded consumer-facing products (e.g. CPG).
Even the Quick Wins identified above can make the difference between profit and loss and red and green on customer scorecards. They require a handful of important changes to work:
- A mindset shift to become data driven
- Sharing data cross-functionally to understand the interconnections between actions and results
- Common definitions for common terms
- Basic modeling and reporting capability
How do you tackle Data and Analytics to get the benefits, and where should you start and end? Our suggestion is to tackle it in phases and take the time upfront to establish a plan. This roadmap should be designed to achieve a realistic three to four year vision.
The first step is to determine those business decisions that matter the most to your results – in a high growth manufacturer, it may be how to best deploy your limited product capacity or how much financing you will need to keep the growth going. Use your financial statements to determine the areas that have the biggest impact based on understanding your business processes and the financial impacts of each step of your value chain. For instance, variability in order lead time may drive up required inventory and tie up significant working capital, which compromises your ability to fund sales growth. Rank the list, as this will be a key input into the next step.
Next, understanding the possible – i.e. the wide variety of applications of both Big and Small Data to create a “right-sized” Data and Analytics vision for your organization. Once the vision is set, it’s important to identify which of these applications are relevant to your objectives and make a high level assessment of the cost/benefit of each. The cost/benefit/execution timeline framework (see the sample in the previous section) based on where you are today versus what it will take to fulfill your vision can help prioritize the applications to investigate. This detailed investigation into Data, Analytics Capabilities, Data Governance and Technology elements should result in a comprehensive roadmap as illustrated below. This will lead you to achieving your objectives in phases with measurable milestones throughout the journey.
Shifting to Execution
As you begin implementation of your roadmap, there are three critical success factors to note:
- Talent and Change ManagementData and Analytics require a change in mindset and company culture to be effective. It’s no use to expend the resources if decisions will continue to be made by gut feel or the data will be used to create silo-based stories. In addition, building analytical talent will likely require hiring or training, especially as more advanced analytics are adopted. In some cases, a centralized Business Analytics team may be an effective way to ensure focus and quality. Think through the pros and cons of such an arrangement.
- Evolution of Analytics and TechnologyThis is a rapidly evolving area to say the least. New techniques for advanced analytics, such as predictive modeling, Natural Language Processing and Machine Learning, are being commercialized quickly. Technologies for sensors, data storage and data processing are coming to market at a dizzying rate. For this reason, we recommend you review the roadmap on an annual basis to ensure the mid- to long-term initiatives will incorporate the most appropriate new innovations.
- Packaged versus Home Grown SoftwareSome of the applications outlined here are available as pre-packaged software. These solutions can accelerate your implementation and incorporate emerging innovations for you. As you consider these packages, take into account the level of interoperability with your existing and planned systems, how tried and true the solutions are and how much customization they require to meet your needs. The more customization they require, the slower it will be to adopt new innovations as the provider incorporates them. And in many cases, we customize to match our current processes when we actually need to use the new applications to evolve them. Hence, you need to think business first!!! Find the solutions that fit your business objectives and process, not visa-versa.
Laura Khoury is a Principal with Blossom Growth Partners and a seasoned expert in using big data and analytics to develop and execute innovative growth strategies. To learn more about Laura and Blossom Growth, please visit us at www.blossomgrowth.com or follow us on Twitter at @blossomgrowth.