Modern supply chains need end-to end optimization to manage resilience and an expanded set of responsibilities. As a result, manufacturers are now asking whether supply chain planning should begin even before sourcing — with product design and production planning.

In the relatively frictionless business-to-business universe prior to the COVID-19 pandemic, manufacturers and retailers could take much more for granted in their supply chain planning. Demand was the critical, elusive variable; supply was an external partner’s responsibility; raw materials and transportation were, for the most part, cheap and plentiful.

COVID, climate and market disruption upended that sense of stability. Suddenly, no part of the end-to-end supply chain was certain; a pallet shipped to a warehouse was now many, smaller, time-definite direct-to-consumer (D2C) parcels with multi-point delivery options; sourcing reliability could no longer be assured amid rising costs, congested terminals and workforce shortages. As geopolitical and trade tensions ratcheted up, a trusted supplier one day could be sanctioned the next.

In addition, businesses have since moved beyond the basic supply chain focus of on-time, in-full delivery at the lowest possible cost to a new, more complex set of questions: How secure are my suppliers from sanctions, government interference or negative headlines? Am I too dependent on any one supplier or country? Will replacements be available as key parts and components reach the end of their product life cycles? Can product designs and portfolios be simplified or optimized to lower costs and manage risk? How should I be weighing potential cost/benefit tradeoffs important to customers and regulators going forward, notably around carbon emissions or circularity? 

That’s a lot to ask from a network designed around legacy technology for speedy, cost-efficient delivery of industrial freight to distribution centers.

Is It Time to ‘Shift Left’? 

Manufacturers are now asking whether they should reach further back in the chain, in their pursuit of both resilience and growth opportunities. Product design and production planning, after all, are where initial strategic choices will ultimately have the most impact on product availability, sustainability, production and logistics costs, and downstream processes.

Dr. Christian Schmidt, director of technology for Siemens Digital Logistics (SDL), points out, for example, that 80% of the factors that determine an industrial product’s carbon footprint over its life cycle are determined in the product design and production planning stages. “By the way that you decide which materials you use, how you design the shape of the product, how you build it, this will all influence downstream in the supply chain how you prepare and deliver it,” Schmidt explains. “Yet if you ask the supply chain or logistics guys whether they’ve ever been asked about the supply chain consequences of a specific design, the answer is no.”

Relatively simple decisions can have outsized impacts, depending on a company’s overall business objectives, Schmidt adds. The decision to import a particular part may make sense from a cost perspective, but that advantage can be more than offset by shipping cost, dependency on a high-risk source country, after-sales serviceability or carbon content. “And it’s not just a difference of 10% to 20%,” he says, “it can be magnitudes of that.”

Downstream supply chain consequences, however, are well outside the scope of what a design team is normally asked to consider in its work. A good first step is to connect relevant functions — in this case product development, product life cycle and procurement — with supply chain, to share real-time information in a kind of expert, crowd-sourced review process. But that, on its own, would entail a significant, ongoing commitment of staff time and resources.

Suppose the process could be automated via an always-on, modular digital twin of the end-to-end product lifecycle/chain — a single real-time source for shared internal, partner-generated, device-generated and third-party contextual data, across functions? AI-enabled “avatars” could communicate and share cumulative expertise, judgment and strategic objectives instantly. Billions of data points from internal, partner and third-party sources could sense demand shifts, supply constraints, inefficiencies, potential disruption, even market opportunities, and enable faster and better decisions and problem-solving. 

‘What-if’ scenarios involve many complex variables, and time is critical. “To move quickly you need to find a way to get fast digital answers, to reduce the need to communicate to only the most relevant scenarios or design alternatives,” Schmidt explains. “The purpose is not to predict the production cost down to the cent; it’s about understanding the bigger picture, the bigger numbers, the bigger differences and tradeoffs.” Better informed guesses, made earlier, produce faster exception responses and shorter time-to-market for new products. 

Preventing Fires, Not Putting Them Out

With their current focus on shipments of materials and inputs for manufacture or finished goods for sales or end use, sourcing is the farthest upstream point where supply chains enter the picture. By that point, with most of the core product design and production planning decisions baked in, supply chain managers find themselves scrambling to put out fires that might have been avoided, now with few levers to pull beyond managing customer expectations and frustration. 

A shift-left strategy reaches back to product design and the engineering process and considers early service parameters like the service life of a part or component, or limitations on how and where it can be sourced. This helps to align supply chain planning with the product design and production planning from the outset, to avoid problems arising largely from siloed data within the organization.

The next step involves connectivity, of data and people — building shared data sets to encourage collaboration across functions and among partners, and developing common data connectors that translate product design and engineering decisions into supply chain implications, and vice versa. Among the potential use cases:

An early warning system for sensing supply bottlenecks.  A useful case in point can be found in the electronics industry which, unlike most other industries, uses standardized, industrywide identifiers, across brands, for some 600 million materials, parts and components. 

Product lifecycle management (PLM) and source-to-design intelligence (SDI) platforms track these items worldwide by source, availability and product lifecycle. When these are integrated with an electronics manufacturer’s bill of materials (BOM) residing in the ERP platform, the digital twin tracks whether a part is sole-source or not, if and when it is near end of product life, if it is in short supply, or is being discontinued and replaced — or not. Using stored form, fit and function (FFF) specifications for each part, the twin can recommend replacement options. 

Based on the BOM, it can also assess in seconds likely shifts in market demand, production shortages and downtime, relative market risks of using a particular part or supplier from a particular origin, and so on. “It’s a nice example of how different functions which don’t usually work together can find that joint language into the supply chain, in this case the bill of materials,” Schmidt adds, with clear early value added for supply chain planning teams as they consider transportation and logistics resilience, cost or sustainability impacts.

Another useful data connector, layered onto the BOM, is the production schedule. Strangely, Schmidt argues, there is no digital “joint language” to translate an inbound container that has missed its cutoff time in Asia, into a materials or parts delay advisory. As a result, critical time is lost and response options narrow. In an always-on, shift-left network, the translation would be automated. “If you widen the scope of logistics,” Schmidt adds, “you have more capability to react.” 

Supply chain + product design + engineering = materials flow design.  The benefits of continuous, real-time information flow between supply chain, product design and production extend well beyond shipment visibility, however. Over time a continuous, real-time flow of production and shipping information will align product design with how a product is ultimately handled, stored and shipped. 

That might influence decisions such as whether to centralize manufacture of a specific product, accepting more safety stocks in regional warehouses to achieve service level commitments, versus localize production where economies of scale in production and inbound material flows might be offset with local sourcing opportunities or resilience from decentralization.

In the warehouse, the BOM and production schedule will optimize performance and flexibility and, in turn, space configuration and processes down the chain — to combine BOM, order data and product KPIs to design material flow layouts within a warehouse, evaluate fixed versus mobile robotics solutions, configure and size pick stations, or optimize travel paths or slotting to smooth workflow. Warehouses across a network can be synchronized to share capacity and resources, scaling up or down as needed to ensure service reliability.

Taking product design and supply chain to the next level.  The ability of product designers and supply chain managers to fully grasp the implications of each’s decisions on the other can unlock significant value over time. 

The lifecycles, availability and source of materials, parts and components used in a product should be integral to the product design process. With this knowledge in hand the supply chain team can configure transportation and logistics, calculate costs and manage risk well in advance of production and, later, parts replenishment.

Centralized, transparent communication is essential, with all product data and processes, 3D constructions, electronics, embedded software, documentation and BOM stored and instantly shareable by relevant partners — a virtual workshop and database. Preferred carriers, 3PLs and suppliers are listed, with service characteristics and KPIs. 

More granular product and input data enables faster, more accurate logistics cost calculation and more effective negotiations with service providers. This level of visibility will be key in calculating and mitigating carbon emissions as governments and large industrial customers put new rules and standards in place, especially for “Scope 3” supplier reporting. 

Generative AI Predicts a Brighter Future 

Given the acknowledged data collection and integration complexity, plus the organizational hurdles in building a shift-left supply chain redesign, manufacturers are nonetheless warming to the concept after the harsh reality of four years of pandemic, climate events, worker and materials shortages, congestion and trade conflicts. “It made people aware that there is no use in having a great product design or a perfect production process if you don’t have the material, if you can’t ship your product,” Schmidt says. 

Introduction of generative AI into the supply chain design is likely to accelerate acceptance. Natural language models will enable managers to ask conversational language questions as they would a human assistant, but in this case the “assistant” will have access to the entire organization’s institutional knowledge and supporting data to run the analytics, recommend multiple options and rank them based on parameters defined in the query.

“Right now you have a product design expert, a production management expert and a logistics expert; the end-to-end model brings all three together at once, instantly. It may not always be as good as talking with an experienced planner, but again, time to market and competitive advantage are more and more about speed.”  

In short, the shift-left is poised to go from zero to must-have quickly. Start your engines.  

Resource Link: https://resources.sw.siemens.com/en-US/white-paper-the-shift-left-logistics-imperative