Supply chain planning has matured to the point where advanced methods such as Machine Learning (ML) are both relevant and necessary: relevant because the value gained from improved analytics offers an impressive ROI relative to the cost of analytics development; necessary because more and more enterprises are creating assets from data that improve business operations and profit margins. Companies that are not investing in advanced analytics for their supply chain are subject to lower profit margins, because analytics combined with the right decision framework always creates more value and competitive advantage.

Supply chain analytics has many levers – demand forecasting, supply planning, and sales and operations planning (S&OP), for example, are three common use cases for supply chain analytics. The complexity of the supply chain use case for advanced analytics and ML requires a trustworthy solution that capitalizes on external demand signals to uncover insights and improve forecast quality for better resource allocation and capital that organizations manage today and in the future.

Bachar Says... Even more important is the role of expert judgement and corporate governance in the application of advanced analytics, both broadly applied and in the supply chain. What do you say? Click here to send us your comments

What business leader in, manufacturing, distribution or retail has looked at innovative data and analysis systems and not wanted a solution for their own organization? And at the same time, how many business leaders find this to be a stretch, due either to past failure to capitalize on the value of data, internal issues that inhibit innovation, or failed attempts to leverage third-party providers to create change? If Machine Learning were easy, it would already be happening in most organizations.

One goal here is to highlight the foundational issues that, once solved for, help to divide the larger problem into parts that can be solved for incrementally, and in an order where quick wins provide insights and the confidence to continue the work and the necessary investment. These parts include defining the problem, building the analytical process, studying the process to identify and solve for likely failure points while automating processes, and creating ease of use through a user interface (UI) and user experience (UX) that leads to optimal human-computer interaction.

Many overviews start with discussions about advanced algorithms, programming languages, data processing, storage, and retrieval.  The discussion should begin with the user experience and identify the optimal way for an expert to interact with data to improve decisions. Then work backwards to find the appropriate solutions that enhance the user experience so that the expert holds valuable data for making better decisions. The expert user interacts with data, reports, forecasts, and the variance from forecast identifies business processes that decision makers will want to understand and take action on. This is the essence of supply chain analytics; it is two steps removed from programming languages and the particular algorithm used to build the forecast. But it cannot work if the decision maker’s user experience is not optimal.

One skill advanced analytics cannot replace is the role of the expert who understands their business model, their products, and their distribution channels, and the importance of meeting that person’s needs when it comes to data access, data visualization, report creation, and decision monitoring. This is why it is important to use a solution that has invested in foundational tools that organize data, visualize and manipulate data for data discovery and reporting, and all within an intuitive user interface that facilitates the translation of insights and forecasts into new decisions.

Even more important is the role of expert judgement and corporate governance in the application of advanced analytics, both broadly applied and in the supply chain. An exceptional user experience will gain greater buy-in across the organization, will reduce resistance to adopting a new software solution and new reporting processes, and thus will have a higher likelihood of success.

One reason why supply chains are adopting Machine Learning is because the typical use cases for advanced analytics are well understood, and analytical methods work well in these situations. Typical supply chain analytics frameworks include demand planning that is grounded in forecasting methods, the segmentation of customers and products, and the management of additional data sources that improve prediction and provide more actionable insights to business decisions. These additional data sources can be both internal, like POS data, and external factors such as the economic cycle or a major weather event that are outside of the business’s control.

More advanced applications involve the correlation of marketing and sales activities with forecasting errors as a way of identifying a signal of the effectiveness of marketing spend, price discounts, and competitor activities.

Most organizations are not well equipped to achieve this. This is where an organization benefits from advisory services that can identify gaps and offer well informed solutions backed up by successful deployments and managed services to ensure a successful supply chain analytics roadmap. If a company builds a foundation of data, analysis, reporting, and knowledge sharing, even more advanced goals can be pursued.

I encourage business leaders to regard the user experience as a top differentiator when selecting an advanced analytics solution. If the user experience is solved for, improving the analytics and managing the data can follow. If the user experience does not facilitate knowledge discovery, decision, and report management, and continuous learning, then the value of the data and analytics will not be realized.

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