Manufacturing Real cases of how manufacturers use analytics to increase their bottom line

Transcription

1 Analytics Applications for: Manufacturing Real cases of how manufacturers use analytics to increase their bottom line

2 Table of Contents Section 1.Introduction Section 2.Production scheduling Section 3.Variable Overhead Manufacturing Cost Analytics Section 4.Supply Chain Analytics Section 5.Transportation Cost Forecasting Analytics Section 6..Customer Segmentation Analytics 2

3 Introduction One of the most frequent concerns business owners have about analytics is the lack of clarity on how it would impact their business. Most medium sized business executives and small business owners have heard about analytics and how it could potentially help them. The business owner (or executive) has in mind a specific problem or problems for which data analytics is the solution. Although this is an excellent starting point, each problem has a different level of data maturity and a different level of business impact. Even after narrowing down the business problem to address using analytics there still may be some questions. Here are some of them that may bubble up to the top: Should we spend money on buying software to solve the problem? Should we invest in a custom solution for the problem? How well would the solution (or software) integrate with existing IT systems? How easily does the solution scale as our business grows? Do we have sufficient data to address the problem? Is our data quality reasonable enough to proceed? How much should the solution cost? What is the measure of success for the analytics solution? These are not all the questions, but they are the most likely ones. Every one of these questions require a systematic effort to effectively address and because of this the complexity, most business default on the side of not doing anything! Time flies and a year later the business problem still persists, there is more buzz about data and analytics in the marketplace and executives find themselves in the same place next year. There is a simple way out of this dilemma: a well thought out data strategy. What is Data Strategy? Data Strategy is a process that starts with your strategic business objectives and views them together with an analysis of your workflows, data requirements, and technical capabilities. The result is a custom development roadmap for your organization, focused on achieving near-term business benefits and creating a platform for future innovation. We ve compiled some of our customer s success stories in this book to show you some of the many ways manufacturers have used their existing data to solve business problems, increase efficiency, and improve the bottom line. The first step to all of these projects was implementing a well-planned data strategy. 3

4 The Benefit Production Scheduling The Challenge Our client, Trenton Corporation, makes several types of anti -corrosion products for use in the pipeline industry. Trenton is challenged by a limited production capacity to make all of its offerings; as well as fluctuation in demand for those offerings. How can we quickly and systematically decide how to allocate production capacity next month; so as to meet demand for every product without incurring excessive labor costs or inventory?. The Goal 90% reduction in planning time Reduced reliance on expertise of a single individual Confidence in planning by reducing human error or bias Continuous app relevance by updating the underlying model with new data Efficiently decide production capacity allowance in advance Meet demand for all products (SKUs) Avoid incurring excessive labor costs (e.g. overtime) or accumulation of inventory The Solution A forecasting model to predict demand for each product Visualization of the demand forecast via a custom app 4

5 SimaFore helped my company understand the right level of data detail to analyze and built a forecasting model for us which has become a critical component of our monthly business planning. Previously no one believed that sales forecasting could be done for our company, but SimaFore provided a tool which is both easy to use and understand. -Stephen Field, GM, Trenton Corp, Ann Arbor, MI Demand Forecast Detail How it works With input from the sales data, we built a high quality forecasting model which provides monthly and quarterly demand projections for a range of products The results are made available in a custom-built dashboard (can be hosted on the cloud and accessed by a secure link if desired) The dashboard is interactive and can not only display forecast results, but also historical and break-downs of key input data into key segments as chosen by the end user When new monthly sales data is uploaded, the model is rapidly updated and the next forecasts can be generated with a few clicks 5

6 The Benefit Variable Manufacturing Overhead Cost Analytics The Challenge Our client, Optiflow, designs, manufactures, and assembles parts used in advanced products of various industries, including defense and medical devices. Manufacturing overhead costs are always varying. To be profitable on any custom manufacturing project, we need to monitor and closely control manufacturing overhead costs. The Goal More than 3x reduction in effort required to accurately measure and track costs Early identification of the impact of various factors, e.g. overtime, new employee hire, etc. on overhead In on glance, spot the reasons for a spike in overhead costs (seem image below) Focus more cost/time on resolving cost issues rather than identifying causes for overhead fluctuations Continuously track manufacturing overheads Identify which cost factors are most influential on overhead The Solution A custom built real-time cost tracking app An interactive dashboard allowing custom views of individual & aggregate data (for cross-referencing 6

7 "It is critical for manufacturing businesses to be able to accurately quantify the impact of production schedules and special expenses on overhead rates. Simafore s custom analytics apps combined with affordable bysiness consulting make it easy for companies like ours to deploy high value analytic. -Fred Collin, CEO, Opti- Flow Inc., Ann Arbor, MI Employee Hours Analysis How it works With input from payroll, work log, and expense data, we built a high quality data model which provides weekly calculations of manufacturing overhead The results are made available in a custom-built dashboard (can be hosted on the cloud and accessed by a secure link if desired) The dashboard is interactive and can not only display overall costs, but also drill down into specific cost factors which can impact overhead When new weekly data are uploaded, the model is automatically updated and the new costs can be displayed in just a few clicks 7

8 The Benefit Supply Chain Analytics The Challenge One area of focus for supply chain managers is the acquisition of market intelligence to help minimize production costs. They need to frequently access commodities market data and related news; compare budgets and contract prices with that market data; forecast pricing changes; and update cost models of products to accurately reflect cost of goods. If done manually, this may be time/labor intensive The Goal Automatically track market prices and commodities data of raw materials Model and forecast the cost of goods (to be sold) on a regular basis The Solution Productivity gains by automated gathering ad conversion of data into information to be analyzed Profitability forecasts and real-time knowledge to make purchasing and pricing decision Performance analytics views to show contracts (purchase prices) and budgets measured against the markets An application that tracks daily market data for commodities used in production An intuitive tool that builds cost models of finished goods An interactive dashboard that allows the end user to review the costs of each product based on underlying commodity prices 8

9 In a world that is saturated with developers, Simafore brings the crucial value of data analytics which made the process of building our application easier and expedient. Further, their customer service and response times kept us at ease during the process. The response from our client base from our application has been beyond expectations and that is due in a large part to the Simafore team. -David Maloni, The American Restaurant Association Dashboard view - Market Data - Commodity Reports - Twitter Feeds How it works Web scraping to provide automatic updates on market data for any selected commodities Market table data comparing today s prices against most recent, 3-month, 6-month, and 1-year historical prices Interactive chart showing commodity price, variation, time, and history Cost modeling to incorporate commodity price effects into product prices Automated algorithmic forecasting of market data to compare against budget, contract, and other manual forecasts Delivered as a Software as a Service (SaaS) cloud based application 9

10 The Benefit 90% reduction in planning time Reduced reliance on experience and knowledge of a single person Confidence in proactive planning by eliminating human error and bias Continuous app relevance by updating its underlying model with new data Transportation Cost Forecasting Analytics The Challenge Our client, Tempur-Pedic, is known for making mattresses and other luxury sleep goods. It ships product across the country via multiple distribution centers (DCs), and incurs different costs at different DCs at different times. Tempur-Pedic was challenged to conduct proactive transportation budget planning with a high level of confidence The Goal Understand why different distribution centers (DCs) have different costs Identify which costs are most influential to each DC Predict aggregate transportation costs with cofidence The Solution An analysis of historical data to identify key cost factors A budget forecasting model to predict costs 10

11 SimaFore helped us understand which key factors impact the costs for each of our DCs and built an aggregate forecasting model for us which has become an important part of our monthly business planning. The high quality of work and combined with the ease of applying rigorous analytic methods SimaFore provides has greatly enhanced our forecasting processes. -Bruce Biby, Transportation Manager, Tempur- Pedic, Lexington, KY Employee Hours Analysis How it works With input from transportation cost databases, a high quality aggregate model which combines data from different DCs was built to provide a single cost-per-unit (CPU) forecast. The model relates different key input factors from across DCs and outputs an aggregate The dashboard is interactive and can not only display forecast results, but also historical trends and breakdowns of input data into key segments as chosen by the end user When new monthly data is uploaded, the model is rapidly updated and the next forecast can be generated with a few clicks 11

12 The Benefit Customer Segmentation Analytics The Challenge Our client, the National Center for Manufacturing Sciences (NCMS), is a non-profit organization focused on providing small and medium manufacturing (SMM) companies access to high technology resources. Manufacturing firms are diverse in terms of goals, needs, budgets, and appetite for risk. So, NCMS endeavors to categorize them into technology-adoption segments, and then match solutions accordingly. To this end, a survey was designed. NCMS s challenge was to efficiently and accurately process hundreds of survey responses, which included both standard (rank on a scale) type and free form or open ended textual responses The Goal Profile and segment over 300 NCMS prospective members based on quantitative and qualitative data Automate or streamline the process (while still including the unstructured, i.e. qualitative data) The Solution Ability to effectively customize offerings and marketing communications to the profiled prospective firms Expansion of the survey nationwide with minimal effort to process the repsonses and categorize new firms A high accuracy classification model that identifies the technological adoption category of any prospective firm based on its survey response, and quantifies the confidence of that result An interactive dashboard that allows NCMS managers to graphically view results, including the overall distribution of prospects across categories Dashboard displays a word cloud of trends in responses and the associations between different keywords. 12

13 "With the Voice of the Customer (VoC) market research project, the main goal was to understand the pain points among the backbone manufacturers. Once we had the data, we needed to find a way to match manufacturers with technology and Simafore provided immense value with the Scorecard app. They were able to quickly classify manufacturers into high, medium and low technology users and provided direction for us to move forward with engagement. -Jon Riley, Vice President of Digital Manufacturing Survey Analysis How it works Approximately 100 companies were manually scored by NCMS and used as training data for the machine learning algorithm The training data and numerical data were split into numerical responses and text responses Both were analyzed separately and then integrated into a final predictive model (image shown on previous page) Feature selection was performed in the loop before building the final model The final model was a neural network that combined text and numerical inputs to predict with 92% overall cross-validated accuracy The model was then used to predict the rankings for the unscored companies 13