SIP - Problem
Problem? There are no problems, only opportunities! Carl
Recap, Supply Improvement Program (SIP) – Abstract
In the first post in this series, SIP-Abstract, you were introduced to SIP, or Supply Improvement Program. SIP was my child and, yes, I had help raising it. SIP is an innovative DMAIC program to resolve multi-channel input and output supply issues in the full SiPoC supply chain.
Mfg. Company was experiencing very high numbers of complaints regarding quality from external and internal sources. The depth, breadth, and causes of the issues were, mostly, unknown. Several internal cross-functional problem-solving sessions were conducted. As a result of the problem-solving exercise, the available data pointed to three areas of focus. The Supply (the ‘S’), Manufacturing Operations (the ‘P’), and Customer Service (the ‘C’) in the SiPoC workflow. The focus of this blog series is on ‘S’, the supply side of SiPoC to reduce the undesired flow of materials.
Background
Four years prior, I was asked to determine metrics to field failures of products (due to my recent experience managing field service). The first, most obvious measure and easiest to monitor was material Quality failure returned (MR) from field and customer. Later we added product returns from factory to supplier. Then later we added On-Time-Delivery (OTD) as a metric. Next, I combined the MR and OTD. The product of MR and OTD was referred to as the Quality Rating% (QR). A QR of 100% is achieved when the product is received in the expected condition and on time. I monitored 5 suppliers to start which grew to 36 (with the help of other great engineers) by year 0 (year timeline is shown below). At year 0, I wrote the white paper which kicked off the scale-up of the Supply Improvement Program, SIP. This is where the story begins, at year 0. SIP and data collection began 4-5 years previous…
Problem Data
Our first task is to define, understand, and size the problem. Our focus here is on the disruption caused by supplied materials on three end-users: Customer, Employee, and Product. As the issues are broad scoped, multiple-input and output streams, we relied heavily on available data. One of the challenges faced immediately was data inconsistency. A normalization process was created on the sources of data (siloed Excel, Access, checklists, and global enterprise). We also deployed a data management technique ELETR (Extract, Load (to data warehouse), Extract, Transform, and Report) to maintain data integrity and traceability. Not an exact science although gave us what was needed to see the landscape. In this section, we are looking directly at the scale of disruptions caused by failures.
Let us define a failure. For our purposes, a failure may also be termed as; disruption, issue, incident, hit, and chargeable incident. A failure occurs when a supplier does not meet the agreement, specification, standard, and/or negatively affects company goals in the following areas: Cost, Delivery, Logistics, Management, Performance, or Quality. Failure can result in not meeting explicit or implied expectations.
We discovered quickly that explicit failures were easier to work with: those issues that were clearly not meeting a defined and documented agreement or standard. On the other hand, the implied failures were a bit more challenging. Implied failures were not a documented agreement or standard although an expectation of the product or part end-use. An example of an implied issue is the degree that constitutes a finished product blemish. In most cases, this was not a document standard at the time. Everyone had an opinion.
Initially, we included only explicit failures, but kept records of the implied failures. In the improvement phase, we began to include implied failures in two ways.
Work to move implied issues into explicit by documenting in agreement or standards where it made sense and was realistic.
Understand that it is impossible to make everything explicit and therefore mature our processes to evaluate satisfaction based on customer expectations. Then help Suppliers use the same expectation standard.
Problem
Mfg. Company was experiencing an enormous amount of quality failures. Each disruption is a negative hit to customer satisfaction and increases Mfg. Company operating expense. In the last 5 years Mfg. Company had averaged nearly 5,000 supplier issues per year, over 6,000 in the current year, and expected to exceed 7,000 issues in the next year.
Mfg. Company utilized 758 direct suppliers to support the business of designing, manufacturing, and servicing the products sold to their customers. Their customers rely on Mfg. Company solutions to, in turn, meet their customer requirements. When products fail, customers become dissatisfied causing stress to Mfg. Company business. Mfg. Company uses thousands of components and sub-assemblies to provide solutions for its customers. These components and sub-assemblies are used to build and service highly specialized systems. Most of the parts used come from external suppliers. Let us size the problem.
By the Numbers (MRs)
NOTE: Values shown below represent the previous 12 months, year 0 (unless otherwise noted).
6,237 – number of supplied materials returned due to quality failures (MRs)
4,999 – average material rejected annually for failing Technical Quality standards (previous 5 years).
758 – number of direct suppliers.
673 – number of suppliers with no chargeable incidents.
99 – Average percent received material meeting Technical Quality standards.
85 – number of suppliers with a chargeable incident.
36 – Mfg. Company suppliers with performance monitor(s) in place (68% of spend).
17 – number of negative hits per day to Mfg. Company employee and customer satisfaction.
2.3 – average annual percent of direct supplier total spend on chargeable incidents (previous 5 years).
1 – percent of material failing to meet Technical Quality standards.
Picture This
Summary
We have viewed and framed aspects of the problem thus far.
Described the background of this project and the catalyst that kicked it off.
Defined a failure as not meeting Cost, Delivery, Logistics, Management, Performance, or Quality expectations.
We looked at and quantified material quality failures (MRs).
Showed a higher than the desired number of MR disruptions, with 6,237 in year 0.
Showed year 0 MR was nearly 25% greater than the previous four-year average of 4,999.
We performed a Pareto Analysis of the MRs by suppliers.
With the Pareto Analysis of MRs we narrowed our focus on the sources of the problem. Reducing our focus from 758 to 85 suppliers.
We utilized returned material data for its objectivity and known workflow.
Note: we also performed Pareto Analysis on causes with inconclusive results due to multiple input streams and failure reporting methods.
Next
In the blogs to come, I will continue to share the SIP methods used to dramatically reduce the supply issues and improved the relationships with suppliers and customers.
In the coming weeks, I will release/publish another blog based on the topics of SIP (Supply Improvement Program) as shown below. In the next post, we will discover the Opportunity available in resolving these issues.
Abstract (posted)
Problem (this post)
Opportunity (next post)
Solution
Goal
Measures
Analysis
Controls
Improvement
Result