The late, great, hall of fame baseball player Yogi Berra once stated, “If you don’t know where you are going, you’ll end up someplace else.” A similar sentiment was provided to Alice during her adventures in Wonderland when the Cheshire Cat told her, “If you don’t know where you are going any road can take you there.” Both these sentiments underscore the importance of planning. This is no less true for supply chain operations than it is in any other of life’s activities. Historically, planning draws heavily on past patterns of behavior. Occasionally, however, a paradigm shift takes place that makes much of the past irrelevant. Cliff Saran (@cliffsaran), Managing Editor of Computer Weekly, observes that the coronavirus pandemic resulted in just such a paradigm shift. He explains, “Looking at historical data has hampered businesses’ attempts to move forward effectively during the pandemic.”[1] Bolstering his argument, he cites a conference presentation given by Francesca Gamboni, a Senior Vice President for supply chain at Groupe PSA. Gamboni told conference participants, “We need new ways to forecast demands.” She added, “Companies need to stop driving their business by only looking in the rear view mirror. Instead, AI-based tools can be used to capture elements from the environment, which can be correlated with demand and then extrapolated to improve forecast accuracy.”
Cognitive technologies and supply chain planning
Stephan Algie, head of business development at INFORM, writes, “I am often asked whether Artificial Intelligence can help to achieve better sales forecasts and procurement planning. My answer: Yes, it certainly can, and especially the improvements by using machine learning algorithms can be really impressive.”[2] He adds, “To achieve this, two important things must be taken into account from the very beginning of a project: [First], you must develop a good feeling for the ‘external’ driving factors that significantly influence your demand planning, [and, second,] your logistics chain must be able to respond to the improved forecasts of future sales.” His first criteria — identifying external driving factors — is especially important when confronted with a paradigm shift. During the pandemic, as consumer behavior changed radically, Enterra® clients had lots of questions about exactly how consumer behavior was changing and how best to respond. As a result, to help find answers, we found new ways to combine and analyze data in the Enterra Global Insights and Optimization System™. Identifying the right external factors was critical to this effort.
Asena Yosun Denizeri, Head of Retail Solutions at Solvoyo, observes, “Artificial Intelligence and Machine Learning are hot topics being discussed in the retail world. Across the globe, whether they are grocery, mass merchant, or fashion, retailers are looking for ways to make faster and smarter decisions using this technology. Among the leading applications of AI & ML in planning are forecasting and replenishment processes.”[3] She adds, “AI and Machine-learning are quickly becoming differentiating capabilities for Demand Planning and Replenishment solutions.”
The importance of concurrent planning
Hank Canitz, the Product Marketing Director at Logility, notes, “Even under the best circumstances, developing production/sourcing plans three, six or nine months into the future with a high degree of confidence is difficult. During times of change and disruption it is nearly impossible to get an accurate plan.”[4] He goes on to note, “Today, many companies run separate processes for long-, medium- and short-term business planning. These individual plans are built from different data sets, based on varying business assumptions, displayed in different units and across different horizons and rely on different systems. It is not surprising that these separate planning processes produce very different results and generate very different recommendations. Trying to integrate these different plans after the fact is next to impossible. Details will be lost or miscommunicated in the data translation efforts leading to strategic plans that lack operational reality and operational plans that lack strategic focus.” At Enterra, we think the answer is concurrent planning.
Concurrent Planning refers to planning by multiple departments simultaneously with consistent objectives. The ultimate goal is having an enterprise wide planning objective filter down into each department to keep the enterprise in alignment. Even during so-called “normal” times, supply chain planning can be difficult. The stark truth is that planning and optimizations are done in many departments within a business, such as supply, manufacturing, distribution, warehouse, transportation, budget, labor, and so on. Often, departmental planning functions are disconnected from one another and may have conflicting goals. In order to deconflict goals, companies need to create an objective function — and overarching objective — that balances these goals. Developing an objective function ensures corporate alignment in order to optimize operations and maximize profits. Why is this important? An objective function scores an outcome’s utility so the best outcome can be chosen. For example, manufacturing may be tasked to fill all orders which requires having inventory on-hand in distribution centers. However, distribution centers may be given the conflicting task of minimizing excess inventory space and cost. Without an objective function that spans multiple planning departments, each department’s objective function may conflict with others in often subtle ways. Cognitive solutions — like the Enterra Concurrent Planning Intelligence Solution™ — can help minimize conflicts and create a balanced concurrent plan.
Canitz concludes, “Efforts should be focused on generating a single, integrated business planning process supported by a single platform that enables intelligent multi-horizon planning. A platform approach drives closer collaboration between planning teams, ensures consistency from one planning horizon to the next and allows the team to adjust plans confidently and quickly as the market shifts. Handovers are seamless and everyone receives reliable answers sooner. Accurate forecasts and capacity plans are developed at the appropriate aggregation level and time horizon. It’s a win-win for the everyone involved.”
Concluding thoughts
In a separate article, Canitz notes, “The adoption of artificial intelligence and machine learning in supply chain planning is not a passing fad. AI and ML are fundamentally changing supply chain.” He continues, “Attaining the full benefits of artificial intelligence will be an evolutionary process. We must learn to crawl, then walk, then run. The introduction of advanced capabilities based on AI into most supply chain organizations will take time, but that should not stop supply chain professionals from planning for the future or taking advantage of the purpose-built solutions available today. Implementing algorithmic planning and optimization technologies today builds the kind of expertise and experience that will ease the adoption of advanced AI capabilities in the future.” Algie adds, “[Cognitive technologies] can increase the prediction accuracy many times over and provide the planner with optimized order proposals. … Machine Learning, with its constantly growing access to data, gives us the opportunity to go one step further and make our forecast models even more future-proof through artificial intelligence.”
Footnotes
[1] Cliff Saran, “Coronavirus shows inadequacy of rear-view mirror planning,” Computer Weekly, 24 September 2020.
[2] Stephan Algie, “Artificial intelligence for sales and procurement planning: fundamental success criteria,” All Things Supply Chain, 12 October 2020.
[3] Asena Yosun Denizeri, “Using AI & ML in Retail Supply Chain Planning,” Logistics Viewpoints, 24 September 2020.
[4] Hank Canitz, “Fragmented Planning Undermines Supply Chain Success,” Logility Blog, 22 September 2020.
[5] Hank Canitz, “Artificial Intelligence in Supply Chain Planning,” Logility Blog, 8 September 2020.
