The high-tech industry has gone through tremendous change over the last few decades. Gone are the days when a complex system costing tens or even hundreds of thousands of dollars could start production after the order was received, with delivery expected a comfortable six or eight weeks later. Now customers of high-tech products expect rapid delivery, but without sacrificing anything in terms of being able to tailor the purchase to their specifications. Similar demands face producers of high-tech consumer products.
Tech producers face challenges associated with their complex manufacturing and distribution networks, whether they are OEMs or upstream component suppliers. Establishing demand and supply plans is hard enough; they also have to be communicated effectively to the other participants. And this must be done in an environment where the technology itself is changing rapidly, which shortens life cycles under the best conditions and mortally disrupts plans under the worst. Frequent engineering changes must be navigated, both for current products and those in support.
End-to-End Analytics cut its teeth solving High Tech’s operations management problems, and the team continues to help its clients tackle challenges related to:
Planning demand in high tech means coping with a number of difficult problems. Industry firms contend with frequent – and significant – product transitions, complex product structures, and high part counts. Moreover, data sources are generally complex, and data often comes from multiple sources (as when contract manufacturers are utilized).
In these situations, the rewards for creating an analytically-driven exception management process are great, especially when they bring together forecasts from the sales teams, marketing organization, distributors, and customers.
Allow the planning team to focus on the key product transitions and the top two to five customer accounts to unlock their significant insights. More scalable and objective analytics can then leverage the rest of the data and drive the business with a light touch from the planning team.
At End-to-End Analytics we have built many customized forecast algorithms to blend the various demand planning input signals, enabling our clients to:
- Deploy analytical tools to capture insight from historical sales data.
Fold in additional information like distributor forecasts, sales pipeline information, and the like.
Follow a process founded on exception management – responding to those situations most requiring hands-on attention.
- Institute a family of performance metrics and incentives to motivate the right activity.
Establish appropriate roles and responsibilities for analysis and decision-making, along with a suitable governance structure.
Supply Planning & Inventory Optimization
Whether semiconductors, network equipment, servers, storage appliances, or any other high-tech product, an effective postponement strategy (and configure-to-order model) is core to realizing increased inventory turns. It will also help reduce your excess and obsolete inventory exposure.
Of course, the typical “hockey-stick” demand curve makes this complicated. So does the challenge of maintaining inventory targets throughout the distributor network. And few customers really expect to have to honor quoted delivery lead times, which exacerbates the problem.
These factors challenge the core assumptions of most inventory optimization approaches. At End-to-End Analytics we take on these challenges, ensuring that our solution faithfully addresses your key constraints. Furthermore, we emphasize a management framework that escalate only the exceptional situations where human judgement will shine. Our approach enables you to:
- Identify the relevant assumptions, requirements, and constraints to properly frame the planning process.
Optimize the balance between inventory and the resupply process to minimize overall cost while ensuring appropriate responsiveness.
Institute a suite of metrics that motivates the right behaviors without overburdening the organization.
- Document and deploy business processes that encourage teamwork and good decision making.
Leverage analytics to evaluate different scenarios, characterize risks, and identify responsible corrective actions.
Sales, Inventory, and Operations Planning
The SIOP process aligns the organization around its big bets, and in high tech many of these bets are tied to product transitions. Companies need to get the new product to market as soon as possible. They have to provide appropriate life-cycle support for outgoing products. And they need to manage inventory, and especially excess and obsolete exposure, as tightly as possible.
To do this, the SIOP process needs to expose the right trade-offs and drive alignment. Tools supporting SIOP need to be able to evaluate multiple scenarios and telegraph likely performance of key metrics. The right analytics enable all of this.
We help our SIOP clients in high tech to:
- Design a framework for executive decision making that effectively combines both quantitative and qualitative inputs.
Institute supply and demand planning processes that lead to clear recommendations in the face of different scenarios and their associated risks.
- Implement analytics and visualization tools to facilitate fact-based discussions.
Keep management focused on the important, high-level problems without becoming embroiled in tactical execution details.
Whether or not your business is vertically integrated or relies entirely on contract manufacturers, your organization will need to resolve questions driven by technology, customer purchasing behavior, and demand uncertainty. Integrating strong capacity planning processes and tools with your inventory optimization efforts and your SIOP process is essential.
There may be trade-offs between investments in capacity or inventory depending on the balance between building to stock and configuring to order. How you compete for share in different markets matters as well. For example, there may be hard decisions on what to convert from die-bank to finished goods during low demand periods early in the quarter to ensure that you have enough capacity to close out the quarter.
End-to-End Analytics has developed a suite of processes, tools, and metrics that will help:
Identify optimized part lists for pre-building to level-load the quarter – often not your highest runners! – by balancing the chance of being sold soon with the risk of lingering unsold for some time.
Evaluate the tradeoffs between adding capacity and adding inventory in order to deliver shorter lead times to your customers.
Set capacity levels and optimize the allocation of product between internal manufacturing facilities and external partners.
Analyze shop floor work-in-process tracking data to establish inputs for capacity planning models, to help with customer quotes, and to systemically identify process issues.
Pricing & Customer Profitability
Transactional analysis opens a treasure trove of information for both sales and manufacturing organizations in a high-tech company. There are a lot of deals, and they often are negotiated heavily. They are also complex, with varying terms on non-recurring engineering, payment terms, logistics costs, lead times, liabilities, and engineering changes.
Creating a list price and standard terms and conditions is just the beginning of the process. The real work requires detailed models that can establish the profitability of each transaction, customer, product, and channel. For more advanced companies, it can also include evaluation of CRM win/loss data and pipeline velocity.
The ultimate goal is a portfolio of processes and tools that help to:
Provide clear visibility to profitability at the transaction, customer, product, and channel levels.
Guide sales and customer teams to help them structure more profitable deals, particularly where they have leverage to stand their ground in negotiations.
Establish guidelines that define a clear escalation path for deal parameters outside of target profitability.
Unfortunately, most conventional network design tools are poorly suited to solve the problems most commonly found in high tech. In high tech the candidate manufacturing and distribution locations are generally limited, and transportation costs for many of the products are minimal compared to their value.
The real issues include identifying push-pull boundaries, enabling rapid fulfillment, and optimizing tax strategy. This takes more than just solving a complex integer programming problem. It means putting a premium on understanding the industry more generally, the technology and customer more specifically. And it requires developing a tool that captures these dynamics and the underlying volatility of the business.
We help our high tech customers addressing network design problems to:
Frame the business problem to capture the key issues, trade-offs, and constraints.
Formulate a model for analyzing the network that provides the right level of detail and insight – not too much, and not too little.
Understand the qualitative factors in play with each proposed solution in addition to the hard costs that are easier to quantify.