Close the Gap Between Planning and Execution with AI Powered Decision Automation
Whether ERP or OMS, TMS or WMS, the execution systems do only what they are told – they follow your instructions. Businesses invest significant time and money in their planning systems. Yet, many need to adjust their plans in Excel hell and then tell the execution systems what to do, mostly with manual uploads. Now, the market put its hope on AI and its subcategories to close this gap between planning and execution.
No one argues with the elusive goal of optimal planning. The aim is to get the right product to the right place at the right time with minimal cost and maximum service. However, the supply chain plans are generally made in isolated functional silos of demand, supply, production, or transportation. Inventory becomes an observation rather than a managed function. As a result, the goal remains elusive.
Large volumes in Retail, CPG, and High-Tech industries overwhelm many traditional planning solutions. These solutions have neither the mathematical competence nor the computing power to precisely plan large product volumes, scenarios, external effects, and contingencies. This fundamental deficiency dumbs down the plan and creates the need for post-plan intervention.
Yet, every day, the execution systems must be told to make a host of decisions:
- what to order
- what to stock
- how much, where, and what orders to fill
- how to allocate
- what to produce
- when and how to transport
An army of planners and Excel become unwilling partners to bridge the gap between the plan and the execution and continue instructing the operational systems to act.
Decision Automation is Possible
The concept behind Solvoyo’s Decision Automation is quite simple: tightly coupling planning output to execution systems and turning optimized plans directly into action. In a few cases, if full decision automation is impossible, automate as much as possible and let the planners handle the exceptions.
Turning optimized plans into action requires concurrency, quality, speed, and scale.
Concurrent Planning
Concurrent planning optimizes orders, inventory, and transport simultaneously and in one plan. Rather than sub-optimizing a siloed operation, you optimize the entire enterprise.
Quality
The quality of a plan is measured by how well it accounts for your business rules, planning targets, and operational constraints. If a plan complies with your rules, targets, and constraints, it should be executed without manual intervention.
Speed and Scale
Speed and scale are necessary to produce an implementable plan routinely, with large SKU volume and transactional data, in time to meet the response requirements of the operation.
Solvoyo designed all these attributes into its cloud-native SaaS platform and in-memory engine and provides its clients with operational plans that require little or no post-plan intervention.
Solvoyo’s Decision Automation approach minimizes the involvement of people in routine decisions by producing executable plans that codify all the intricacies, complexities, and constraints down to an individual SKU level. However, most organizations still use the classic functional silos and are reluctant to surrender total decision responsibility to computer systems. To reduce the resistance to full decision automation and change management risks, Solvoyo allows you to automate the decision-making level per your comfort level to help user adaptation. You execute the routine decisions immediately and review only the plan exceptions.
Exception Handling for Automated Planning
Business rules and quantifiable tolerances filter the plan exceptions. Thus, planners get the time and the opportunity to use their knowledge to resolve exceptions before forwarding the results for operational execution.
Over time, Solvoyo’s decision automation approach aims to reduce the exceptions through AI, including Machine Learning. How? We can reach 100% decision automation over time by adjusting planning parameters . Here’s a list of clients who achieved significant success in approaching that elusive goal.
- Procter & Gamble, our global CPG client with operations in 180 countries, uses the Solvoyo Platform to improve data quality and maintain visibility into its global demand, inventory, and supply operations.
The company has been able to standardize its master data across 180 countries and automate data reconciliation throughout the organization – No need for Excel!
- Vestel, our consumer electronics client since 2011, uses our platform to optimize its daily fulfillment, allocation, and transportation plans. They have executed 100% of the plan recommendations daily in the past few years, with no exceptions!
Vestel also uses the Solvoyo platform to measure the improvements in service and total transportation costs.
- A101, our discount retailer client since 2012, uses our artificial intelligence (AI) platform and machine learning (ML) for automated end-to-end supply chain planning and analytics. The company replenishes 12,000+ stores daily and purchases up to 1,800 SKUs into 40 DCs. A101 routinely executes over 98% of the replenishment recommendations daily without needing to review the daily forecasts and the weekly refreshed optimal reorder levels for any of the stores’ SKUs.
The plan exceptions are resolved within a half-day window every day, and the store manager relays the overrides to the system through a hand-held smart device. Over the past few years, A101 achieved rapid and profitable growth while operating with the highest inventory turns in its market. A101 tracks many KPIs on the Solvoyo Platform.
Goal Programming for Concurrent Optimization
To increase the quality of its plans, Solvoyo uses ‘Goal Programming’ to optimize multiple goals to get the best overall results. It considers various -and sometimes conflicting- goals. For example, you might want to optimize your fulfillment and allocation plans to get the highest profit margin and minimize transportation costs.
If you are a manufacturer, you continually face the tradeoffs among order due dates, inventory investment in materials, and the optimal use of your manufacturing capacity. Again, goal programming allows a manufacturing planner to quantify tradeoffs across conflicting goals to produce the best plan and achieve the best overall result.
Machine Learning & AI Empowering Decision Automation
Solvoyo reduces the need for manual intervention with its ability to measure the effect of promotions, decide on markdown timing, recommend price adjustments on special days, and, as appropriate, incorporate external factors such as weather, Google Analytics results, and propensity to buy through social media input.
Solvoyo also uses Machine Learning and Neural Network approaches to refine the plan or the planning parameters automatically, enabling rapid response to the changing business conditions:
- Find clusters and patterns to predict demand in highly dynamic and price-sensitive markets
- Provide product portfolio adjustments in e-commerce based on dynamic changes in brick & mortar data
- Automatically adjust planning parameters such as lead times, demand, lead-time variability, and target service levels based on actual data and future information.
- Create more efficient transportation routes.
Another top-of-mind need is a scalable approach to high-quality plans for extremely large transactions and complex planning problems. Solvoyo demonstrates a significant advantage thanks to our supercomputer-like computational capacity in the cloud. Solvoyo generates plans at the lowest required resolution to achieve a high degree of decision automation, enabling immediate execution.
In summary, measurable value in supply chain planning comes from decision automation that successfully creates executable plans requiring little or no intervention. Artificial Intelligence and Machine Learning are valuable tools in a toolbox, with many other helpful tools to achieve that goal. Let Solvoyo drag your planners out of the routine rot with Excel and give them the time and the tools to add real value.
