From raw commodities and materials, to top-level finished goods inventory (FGI) shipped to market or used in the field, materials are the cornerstone of nearly all manufacturing.
Materials is the single, largest cost on the balance sheet and, materials are topic of discussion by more decision makers in more functional groups, and materials are mentioned in every process and phase of manufacturing production starting at the beginning of every new customer relationship and every new product development phase.
The smallest change in materials cost, or change in spec, in your supply chain can help you make deals, or break even the longest of partnerships.
But today, most companies do not view individual compounds or single components as necessarily interesting. And they do not have the time or technology know-how to build large scale portals to capture corporate knowledge which surrounds them.
Manufacturers today focus on the larger business ROI/ROE. But decision makers who count contributing costs of engineering and other support functional groups more clearly see where your ROI/ROE come from.
Electronics industry and beyond
Take for example, the electronics industry. Every OEM company and EMS manufacturer must find ways to manage complexity at scale and master their largest manufacturing cost – materials cost of goods sold (MCOGs).
The declining electronics distributor model has steadily been losing its grip on electronics manufacturing supply chains because distributors only add more cost to OEM BOM (products). The distributor provides no real value to OEM companies. Distributors simply move components between parties.
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Artificial intelligence exceeding EMS manufacturing industry expectations
There is good reason nearly every electronics distributor is diversifying away from handling parts and PPV for their revenue. Their game is up and OEMs (and EMS providers) are both seeing distribution as obsolete.
Until now, the challenge for OEMs and EMS providers alike has been resourcing accurate and comprehensive materials information for optimal pricing, availability, and alternate spec materials for better forecasting and production planning.
To put this in perspective let’s take a look at the solution landscape in this area and then break out the variances between the last generation platforms and the future of supply chain systems. We first have to consider some differences between current systems and future systems.
Supply chain shortcomings
Current supply chain systems provide a pre-made set of rigid interfaces which offer manufacturers the ability to enter data, adjust data, and collaborate around the data. Vendors sell these systems with professional consulting services to adjust the system to the client’s process. When completed it is up to the organization to capture the true value through good education, and willingness to learn and use new technologies.
While common practice, customers still must rely on an internal person(s) to conduct tasks (even if mundane), data interpretation and analysis, decision-making, and execution. These are the basis of classic systems integrations.
For emphasis, consider ERP, MRP, FICO and other common systems or modules; users remain at the system’s helm, but the value is future-based (comes later) since nothing has radically changed and, as the customers’ business evolves (and attempts to speed up) customers are required to engage their vendors with change requests for vendors to fix or enhance workflows, customize interfaces, and implement additional data integrations.
This is the current situation customers face, according to Forrester Research, when working with SAP, Oracle, QAD, Kinaxis, to name a few. None of these systems share a forward transition to modernize their core.
SAP is a market leader but incorrectly, SAP is marketing HANA as an A.I. solution. HANA is not artificial intelligence. HANA is an in-memory database similar to H2, Redis and others. Oracle (with its SCM cloud and Fusion middleware offerings) and QAD are both industry laggards, and to their detriment, cores for these two vendors were written (or re-factored) 10 to 20 years ago.
Today’s incumbents all share one common means to survive: using add-ons or modules to try to extend their relevance or functionality.
The manufacturing supply chain system must be fluid. Manufacturers want systems that can adjust to the dynamics of the business as situations and requirements change. Truly intelligent systems must be able to learn behaviors of the business.
Artificial intelligence supply chain systems
Today’s supply chain platform must work with a new set of rules, with new ways of thinking, able to consume data at the speed the business requires and all of this should be priced into the system ready-to-buy, not priced as individual, feature side project with additional costs and added complexity. Silicon Valley-based Neural Corporation has achieved this, and more.
The chart below reveals industry positioning of current industry leaders with Neural. Chart inputs for each company take into consideration the following:
- Core offering: how automated, or near A.I. the vendor is, and
- Capabilities the system provides the buyer (KPIs), and
- Cost (chart: Value Proposition)*
Value proposition refers to customers seeing immediate results (output) post-implementation v. cost. Whereas industry incumbents price and market their solutions based on future value.
In contract electronics supply chains, OEMs and their EMS manufacturing partners do not coordinate or communicate with each other nearly as well, or as often, as manufacturing executives might think. Constant connectivity is crucial to supply chain success just as demand planning, forecasting, and effectively orchestrating workflows for all functional groups supporting production, are individually critical.
But not every EMS provider can invest in better technology without commitments from OEMs who are typically viewed as pulling their EMS partner’s capabilities forward.
Neural Corporation is now seeing pilot programs between OEMs and EMS partners where engagements are not seen as program costs. Instead, engagements are identifying synergies between the parties and converting opportunities and savings discovered into additional revenue streams for both sides.
Originally, Neural set out with its artificial intelligence (codenamed: Smith) with a vision to build intelligent systems which would help companies like Aptiv, Ford, and Boeing better manage large scale BOMs and massive materials volumes and challenges.
Artificial intelligence masters materials
But Smith evolved. As a manufacturer, what if you could find any materials, commodity or component, anywhere in the world, and once found it is forever curated? Right now.
Enter Materials Master. By Neural.
As you take on new business or solve problems for prospects and clients, you are building a detailed swarm of materials knowledge. You then use this knowledge inside your business to help clients solve problems months before they occur in real time.
Smith is the artificial intelligence from Neural Corporation that evolves over time to better serve manufacturing systems which it easily integrates into. Examples of challenges Smith is solving right now include:
- Materials conflicts, known and unknown constraints
- Design level options and recommendations
- Alternatives in NPI or late product exits
- Product family alternatives for emerging markets
- Regulatory and compliance challenges between material options
- Materials blending and bundles to offset peering eyes or transparent costs
Costs and pricing and inventory are key to the complexity and success of every manufacturing company and product produced. Today, materials and commodities, sub-assemblies and products built by you, or coming from any part of your supply chain, can be referenced accurately, in a timely manner, and tracked: when and where each is used.
Think about the implications special to your business treasury and your cost basis.
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